arxiv
Score 35.0
2026-06-22 · Ulas Berk Karli, Tesca Fitzgerald
Research Track A · General AI
Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs poorly. This approach incurs several downsides: it requires the robot to fail before data collection is triggered, provides little guidance ab…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 35.0
2026-06-29 · Byeong Hoon Yoon
Research Track A · General AI
We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces. Instead of treating Low-Rank Adaptation (LoRA) modules as disposable per-task adapters, NSR manages them as compressible, retrievable memory units on a frozen backbone through a recurring cy…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 34.5
2026-04-15 · Noureddine Kermiche
Research Track A · General AI
Catastrophic forgetting remains a primary hurdle in sequential task learning for artificial neural networks. We propose a silicon-native modular architecture that achieves structural parameter isolation using Task-Specific Experts and a distributed, outlier-based Gatekeeper. Moving beyond traditional sequential consoli…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 34.5
2026-05-12 · Hamza Ahmed Durrani, Rafay Suleman Durrani
Research Track A · General AI
Large language-vision models (LVLMs) such as CLIP, Flamingo, and BLIP have revolutionized AI by enabling understanding across textual and visual modalities. These models excel at tasks like image captioning, visual question answering, and cross-modal retrieval. However, they face catastrophic forgetting when learning n…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 30.0
2026-03-12 · Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, Roberto Martin-Martin
Research Track A · General AI
Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. FT) leads to catastrophi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 29.5
2026-08-17 · Yikai Si, Shanshan Qin
Research Track A
Lifelong learning requires acquiring new knowledge without erasing the old. Yet neural population codes for familiar stimuli and behaviors change over days and weeks. This coexistence of stable memory and changing internal codes may depend on how a learning system prevents forgetting. We therefore tested whether differ…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 29.0
2026-04-17 · Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau, Marius Dragoi, Florin Brad, Cristian Daniel Paduraru, Alexandru Tifrea, Elena Burceanu, Radu Tudor Ionescu
Research Track A · General AI
Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each task. To mitigate catastrophic forgetting, state-of-the-art approaches impose constraints on new adapters with respect to the previous ones,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 28.5
2026-06-05 · Rahul Nair, Chun Tao
Research Track A · General AI
Deploying Small Language Models (SLMs) on edge devices requires efficient fine-tuning strategies that adapt models to new tasks without degrading their general capabilities. In this study, we benchmark five sub-1B models (135M-1B) on mathematical reasoning tasks and uncover a critical vulnerability: Full Fine-Tuning (F…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 28.5
2026-07-17 · Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li, Nan-Yow Chen, En-Jui Kuo
Research Track A
Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge. However, variational quantum classifiers (VQCs) are prone to catastrophic forgetting under nonstationary task distributions. We propose quantum elastic weight consolidation (QEWC), a quantum Fisher i…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 28.0
2026-07-13 · Jing Liu, Chenxuanyin Zou, Jiayang Ren, Gaoyun Fang, Chengfang Li, Yan Wang, Zhenchao Ma, Bo Hu
Research Track A · General AI
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired k…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 26.5
2026-06-09 · Toan Nguyen, Yang Liu, Trung Le, Celso de Melo, Flora D. Salim
Research Track A · General AI
We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step. When such knowledge is never revisited, these losses compound into lon…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 26.0
2026-03-29 · Ashish Pandey
Research Track A
Sequential fine-tuning of pretrained language encoders often overwrites previously acquired capabilities, but the forgetting behavior of parameter-efficient updates remains under-characterized. We present a controlled empirical study of Low-Rank Adaptation (LoRA) in sequential transformer encoder fine-tuning with compa…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 26.0
2026-07-13 · Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed, Dilnaz Utemissova, Ufaq Khan, Mohammad Yaqub
Research Track A · General AI
Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge. Continual learning (CL) provides a practical framework for this setting. Despite rapid progress in medical vision-language model…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 26.0
2026-07-16 · Yao He, Gan Sun, Wenqi Liang, Fazeng Li, Yang Cong
Research Track A
Similar to the natural capabilities of humans to sequentially learn new tasks, robots with Vision-Language-Action (VLA) models should possess lifelong learning ability to learn a new task when deployed in open-world environments. However, most recently proposed lifelong learning models aim to effectively learn the curr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.5
2026-04-20 · Lixian Chen, Jianhong Tan
Research Track A
Adapting foundation models under resource budgets relies heavily on Parameter-Efficient Fine-Tuning (PEFT), with LoRA being a standard modular solution. However, LoRA suffers from spectral interference. Low-rank updates often concentrate energy on the leading singular directions of pretrained weights, perturbing genera…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.5
2026-07-29 · Ashmith Atmuri, Yashaswini Rao Bhogarajula
Research Track A · General AI
We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?sents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns through local updates without end-to-end backpropagation through its feature-generating system. We investigate whether combining s…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.0
2026-04-10 · Xingyu Shao, Zhiqiang Yan, Liangzheng Sun, Mengfan He, Chao Chen, Jinhui Zhang, Chunyu Li, Ziyang Meng
Research Track A · General AI
Robust geo-localization in changing environmental conditions is critical for long-term aerial autonomy. While visual place recognition (VPR) models perform well when airborne views match the training domain, adapting them to shifting distributions during sequential missions triggers catastrophic forgetting. Existing co…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.0
2026-04-23 · Nicolae Filat, Ahmed Hussain, Konstantinos Kalogiannis, Elena Burceanu
Research Track A · General AI
Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the same stream can induce d…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.0
2026-06-29 · Bertram Taetz, Hugo Albuquerque Cosme da Silva, Gabriele Bleser-Taetz
Research Track A · General AI
Motion-language agents must possess the bidirectional capability to both understand human movement (motion-to-text, M2T) and generate it from natural language (text-to-motion, T2M). While foundational models have achieved strong performance in static settings, autonomous agents operating in dynamic environments must co…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 25.0
2026-07-24 · Qingyu Yang, Haonan He, Minglei Li, Jingqi Ye, Tao Chen, Lei Bai, Peng Ye
Research Track A · General AI
Mixture-of-Experts (MoE) architectures have been widely adopted in large language models, yet parameter-efficient fine-tuning (PEFT) for MoE models remains underexplored. Existing PEFT methods for MoE either ignore router priors with uniform adapters, reducing efficiency and risking forgetting, or rely on static expert…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.5
2026-05-01 · Beining Wu, Zihao Ding, Jun Huang
Research Track A · General AI
While current federated multimodal continual learning over mixture-of-experts low-rank adaptation (MoE-LoRA) is built on the unverified assumption that routing isolates task-specific knowledge into disjoint experts, we argue that routing operates per-sample, while forgetting accumulates across the task sequence, and gr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.5
2026-06-24 · Luke McDermott, Robert W. Heath, Rahul Parhi
Research Track A · General AI
Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context learning. The quadratic nature of attention, however, prohibits transformers from performing this process on arbitrarily long sequences. In this work, we argue that ext…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.0
2026-03-16 · Zhaohui Geoffrey Wang
Research Track A · General AI
A critical failure mode of current lifelong agents is not lack of knowledge, but the inability to decide how to reason. When an agent encounters "Is this coin fair?" it must recognize whether to invoke frequentist hypothesis testing or Bayesian posterior inference - frameworks that are epistemologically incompatible. M…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.0
2026-04-09 · Yushuo Zhang, Yu Cheng, Yongkang Hu, Jiuan Zhou, Jiawei Chen, Yuan Xie, Zhaoxia Yin
Research Track A
The rapid advancement of facial forgery techniques poses severe threats to public trust and information security, making facial DeepFake detection a critical research priority. Continual learning provides an effective approach to adapt facial DeepFake detection models to evolving forgery patterns. However, existing met…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.0
2026-04-27 · Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Research Track A · General AI
Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because uncertainty reliability can degrade earlier and more sharply than top-1 performance. We study this empirically by measuring conformal coverag…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.0
2026-06-04 · Kion Fallah, Silen Naihin, Barak Widawsky, Qingqing Mao
Research Track A · General AI
Deployed large language model agents must adapt to distribution shift in dynamic environments. Ideally, adaptation can be performed from accumulated agent experiences and retain prior capabilities while transferring to future tasks. However, agent actions and environmental transitions can only be sampled once per scena…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 24.0
2026-07-30 · Anubhav Lakra, Yue Feng
Research Track A · General AI
Large Language Models (LLMs) deployed in dynamic financial environments face a critical challenge: maintaining factual accuracy as market conditions, regulations, and corporate facts change continuously. While 4-bit quantization enables efficient deployment, it severely limits the viability of sequential memory editing…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.8
2026-08-13 · Weitao Chen, Hu Jiaxin, Xie Tianyidan, Yang Li, Yuyi Qian, Banghao Xu, Ziheng Tang, Shenyi Wang, Mingyue Yu, Duo Li, Jiacheng Shi, Gao Wang, Zhan Xu, Zhicheng Qiu, Xuanfu Li, Jian Yang, Lanjun Wang, Zili Yi
Research Track A · General AI
Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks. However, existing benchmarks rely predominantly on web-sourced videos that lack inter-clip spatiotemporal continuity, making it difficult to asse…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.5
2026-04-16 · Cuong Hoang, Le-Minh Nguyen
Research Track A · General AI
The proliferation of financial misinformation poses a severe threat to market stability and investor trust, misleading market behavior and creating critical information asymmetry. Detecting such misleading narratives is inherently challenging, particularly in real-world scenarios where external evidence or supplementar…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.5
2026-07-26 · Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh
Research Track A · General AI
Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting. To mitigate this, LoRA-based continual learning methods allocate a separate low-rank adapter per task, yet existing approaches either require task identity at infe…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.5
2026-08-03 · Mohammad Rostami
Research Track A · General AI
Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied. We show the opposite: as demonstrations accumulate, a subset of VLMs undergo an \emph{in-context collapse}, a sharp,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.3
2026-05-07 · Hanxiang Chao, Yihan Bai, Rui Sheng, Tianle Li, Yushi Sun
Research Track A · General AI
Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory, yet current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges. We identify a critical and underexplored failure mode, Implicit Con…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.0
2026-06-20 · Mohammed Rawhani, Dervis Karaboga, Ozkan Ufuk Nalbantoglu, Alper Basturk, Bahriye Akay
Research Track A · General AI
Pre-trained language models struggle when applied to new domains, as full fine-tuning is computationally expensive and prone to catastrophic forgetting. This study addresses this challenge by presenting a novel parameter-efficient strategy for unsupervised domain adaptation that combines custom PEFT architectures with …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.0
2026-07-08 · Sojung An, Junha Lee, Sujeong You, Nam Ik Cho, Donghyun Kim
Research Track A · General AI
Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks. The key challenge of VFM adaptation stems from the prohibitive costs of full fine-tuning and catastrophic forgetting. To address this, Low-Rank Adaptation (LoRA) has emerged as the prevailing paradigm for Par…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 23.0
2026-07-16 · Dante Lok
Research Track A
We introduce \emph{gate-zero growth}, a function-preserving (FP) operator for continual learning that adds new residual blocks through a zero-initialised gate. Under a transversality condition, gate-zero growth induces \emph{rank separation} in the functional Jacobian: old directions are unchanged, new-weight direction…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-03-13 · Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin
Research Track A · General AI
Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm inherent to modern LLMs. This survey presents a comprehensiv…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-03-31 · Michael Chertkov
Research Track A · General AI
An agent that operates sequentially must incorporate new experience without forgetting old experience, under a fixed memory budget. We propose a framework in which memory is not a parameter vector but a stochastic process: a Bridge Diffusion on a replay interval $[0,1]$, whose terminal marginal encodes the present and …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-04-08 · Radu Negulescu
Research Track A · General AI
Catastrophic forgetting is not an engineering failure. It is a mathematical consequence of storing knowledge as global parameter superposition. Existing methods, such as regularization, replay, and frozen subnetworks, add external mechanisms to a shared-parameter substrate. None derives retention from the learning dyna…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-04-14 · Jagadeesh Rachapudi, Ritali Vatsi, Praful Hambarde, Amit Shukla
Research Track A · General AI
Recent advances in deep learning underscore the need for systems that can not only acquire new knowledge through Continual Learning (CL) but also remove outdated, sensitive, or private information through Machine Unlearning (MU). However, while CL methods are well-developed, MU techniques remain in early stages, creati…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-04-28 · Dominik Żurek, Kamil Faber, Marcin Pietron, Paweł Gajewski, Roberto Corizzo
Research Track A · General AI
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new tasks arise over time, but adapting the model in live environment interactions is expensive, risky, …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-06-22 · Amrita Singh, Rishabh Jha
Research Track A
Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety problem as an accuracy one. Updating a deployed model for a new imaging modality can fail silently in two ways that harm patients: it can forget modalities it already handled (catastr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-06-29 · Xuan Zhao, Haonan He, Qingyu Yang, Minglei Li, Jingqi Ye, Zelin Tan, Bo Wan, Peng Ye
Research Track A · General AI
Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, manually authored or extracted from task trajectories, are textual recipes encoding mature problem-solving experience and are critical to agentic capabilities. Despite wi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-07-06 · Giulia Lanzillotta, Mandana Samiei, Doina Precup, Razvan Pascanu, Claire Vernade
Research Track A · General AI
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, often unstated assumption: that a lifelong learner should approximate the Joint-Task Learning (JTL) solution and retain all previously acquired knowledge. We challenge this…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.5
2026-08-02 · MD Shaikh Rahman, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
Research Track A
Understanding sentiment in low-resource languages remains a key challenge for Natural Language Processing (NLP), particularly when domain-specific data is scarce. In this work, we present SentiBanglaBERT, a two-stage Bengali sentiment classification framework combining domain-adaptive continual pretraining and paramete…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 22.3
2026-06-09 · Jaewoo Lee, Zaid Khan, Archiki Prasad, Justin Chih-Yao Chen, Supriyo Chakraborty, Kartik Balasubramaniam, Sambit Sahu, Elias Stengel-Eskin, Hyunji Lee, Mohit Bansal
Research Track A · Research Track B · General AI
Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments. However, existing critics suffer from two key limitations: they (1) focus primarily on short…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.5
2026-04-06 · Satyam Goyal, Anirudh Kanchi, Garv Shah, Prakhar Gupta
Research Track A · General AI
Large Language Models (LLMs) are typically static after training, yet real-world applications require continual adaptation to new knowledge without degrading existing capabilities. Standard approaches to updating models, like full finetuning or parameter-efficient methods (e.g., LoRA), face a fundamental trade-off: cat…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.5
2026-05-12 · Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal, Joseph E. Gonzalez, Matei Zaharia, Kurt Keutzer, Inderjit S Dhillon, Rishabh Agarwal, Devvrit Khatri
Research Track A · General AI
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can chea…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.5
2026-05-27 · Cheng Chen, Pengpeng Zeng, Yuyu Guo, Lianli Gao, Hengtao Shen, Jingkuan Song
Research Track A · General AI
Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors ($A$ and $B$), creating a composite update to the full weight matrix through their interaction. To prevent catastrophic forgetting, this update should remain orthogonal to the task-specif…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.3
2026-06-03 · Bo Mao, Jie Zhou, Yutao Yang, Xin Li, Xian Wei, Qin Chen, Xingjiao Wu, Liang He
Research Track A · General AI
Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learning agents for long-horizon tasks typically depend on discrete skill or past experiences retrieval with static parameters during inference, which prevents them from contin…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.3
2026-07-22 · Alexis Fox, Junlin Wang, Paul Rosu, Bhuwan Dhingra
Research Track A · General AI
Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their limited performance on continual learning benchmarks such as ARC-AGI-3, especially when models are evaluated out of the box. Various agent har…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.3
2026-08-16 · Maksim A. Kazanskii
Research Track A · General AI
Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks. Existing methods primarily mitigate forgetting through parameter regularization or experience replay, while the representation-space dynamics associated with f…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.2
2026-08-05 · Kartikey Singh Bhandari, Aarya Wadhwani, Dhruv Kumar, Pratik Narang
Research Track A · General AI
LLM agents that persist across sessions accumulate stored memories whose validity varies enormously by content type, yet existing memory architectures treat all memories as equally persistent and systematically contaminate retrieved context with outdated facts. We show that per-memory, type-conditioned temporal decay, …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-04-22 · Noah Flynn
Research Track A · General AI
Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performance due to negative cross-lingual interference. To address this, we introduce COMPASS (COntinual Multilingual PEFT with Adaptive Semantic Sampling), a novel data-centric …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-05-06 · Alaa Zniber, Ouassim Karrakchou, Mounir Ghogho
Research Track A · General AI
Early-exiting neural networks enable adaptive inference by allowing inputs to exit at intermediate classifiers, reducing computation for easy samples while maintaining high accuracy. In practice, exits can be trained sequentially by incrementally adding them to a shared backbone; however, this sequential training can c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-05-12 · Xinrui Wang, Shao-Yuan Li, Bartłomiej Twardowski, Alexandra Gomez-Villa, Songcan Chen
Research Track A · General AI
Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical organization of real\text{-}world concepts that evolves both horizontally (sibling classes) and vertically (coarse or fine categories). To bett…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-05-12 · Patryk Krukowski, Jacek Tabor, Przemysław Spurek, Marek Śmieja, Łukasz Struski
Research Track A · General AI
Data-free continual learning (DFCIL) relies on model inversion to synthesize pseudo-samples and mitigate catastrophic forgetting. However, existing inversion methods are fundamentally limited by a simplifying assumption: they model feature distributions using diagonal covariance, effectively ignoring correlations that …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-05-21 · Javad Parsa, Enis Simsar, Amir Joudaki, Thomas Hofmann, André M. H. Teixeira
Research Track A · General AI
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation interference. Existing modular methods either rely on expensive post-hoc fusion or freeze adaptation subspaces, which limit expressiveness and …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-06-06 · Emre Alyamac, Himanshu Janmeda, Shashwat Krishna, Yash Vijay
Research Track A
Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Learning. This project investigates whether the order in which a model learns information affects how well it retains knowledge. Specifically, we ask: does learning general…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-07-10 · Julius Störk
Research Track A · General AI
Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation. This work argues that forgetting should instead be modeled directly as interference between tasks. In the frozen-feature regime, forgetting from learning a new task is exactly the interference energy induc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 21.0
2026-07-22 · Nabila Tasnim, Haoran Liu, Qing Cao, Saugata Ghose
Research Track A · General AI
Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learning from new data in the field. Continual learning has emerged as a promising solution for edge training, by incorporating techniques that successfully combine a highly sum…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.8
2026-06-23 · Tianyu Yang, Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni, Srinivas Chappidi
Research Track A · General AI
Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. Existing memory agents actively update external memory through generated write, revise, and delete operations, but these updates may omit important information, corrupt …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.5
2026-04-27 · Kevin McKee, Thomas Hazy, Yicong Zheng, Zacharie Bugaud, Thomas Miconi
Research Track A · General AI
Block-sequential continual learning demands that a single model both protect prior solutions from catastrophic forgetting and efficiently infer at inference time which prior solution matches the current input without task labels. We present Functional Task Networks (FTN), a parameter-isolation method inspired by struct…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.5
2026-06-08 · Steven Vander Eeckt, Hugo Van hamme
Research Track A
Speech foundation models enable strong general-purpose ASR and are attractive for downstream adaptation. However, their size and the catastrophic forgetting induced by sequential fine-tuning demand parameter-efficient and regularized training methods, motivating parameter-efficient continual learning (PECL). While PECL…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.5
2026-07-20 · Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria, Seul Lee, Daniel Apley, Wei Chen
Research Track A
Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift. Maintaining surrogate fidelity under drift, particularly when models must also capture aleatoric uncertainty, remains an open challenge. Exist…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-04-01 · Haiyang Guo, Yichen Shi, Fei Zhu, Wenzhuo Liu, Hongbo Zhao, Fanhu Zeng, Shijie Ma, Da-Han Wang, Xu-Yao Zhang
Research Track A · General AI
Video Large Language Models (Video-LLMs) require continual learning to adapt to non-stationary real-world data. However, existing benchmarks fall short of evaluating modern foundation models: many still rely on models without large-scale pre-training, and prevailing benchmarks typically partition a single dataset into …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-04-07 · Md Shamimul Islam, Luis G. Jaimes, Ayesha S. Dina
Research Track A · General AI
Network Intrusion Detection Systems (NIDS) face important limitations. Signature-based methods are effective for known attack patterns, but they struggle to detect zero-day attacks and often miss modified variants of previously known attacks, while many machine learning approaches offer limited interpretability. These …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-04-14 · Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
Research Track A · General AI
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. Conversely, discrete…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-06-17 · Shengyuan Ding, Xilin Wei, Xinyu Fang, Haodong Duan, Dahua Lin, Jiaqi Wang, Yuhang Zang
Research Track A · General AI
Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks either expose the full state, conflate hidden-state reconstruction with other agent skills, or test recall only after an episode has ended. W…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-07-23 · Gaurav Dadhich
Research Track A · General AI
Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history while paying a token c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.3
2026-08-17 · Jiawei Liu, Jiacheng Guo, Tian Zhang, Yiwei Xu, Juan Wang, Jinlin Fan, Bowen Xiao, Chi Guo, Keyan Guo, Hongxin Hu
Research Track A · General AI
Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks. Traditional LL…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-04-19 · Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu, Eric Eaton, René Vidal
Research Track A · General AI
Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years, the theoretical development of CL remains in its infancy. At the heart of developing CL theory lies the challenge that the data distributi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-04-22 · Yingjie Gu, Bo Xiong, Yijuan Guo, Chao Li, Xiaojing Zhang, Liqiang Wang, Pengcheng Ren, Qi Sun, Jingyao Ma, Shidang Shi
Research Track A · General AI
For LLM agents, memory management critically impacts efficiency, quality, and security. While much research focuses on retention, selective forgetting--inspired by human cognitive processes (hippocampal indexing/consolidation theory and Ebbinghaus forgetting curve)--remains underexplored. We argue that in resource-cons…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-05-27 · Guangyu Li, Meng Ding, Lijie Hu
Research Track A · General AI
In-context learning (ICL) derives its power from enabling Large Language Models to adapt to new tasks via prompt-based reasoning alone, entirely bypassing the need for parameter updates. Existing theories primarily study ICL in single-task settings, while real-world prompts often contain sequences of heterogeneous task…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-06-11 · Zhibao Chen, Qian Cheng
Research Track A · General AI
Long-running LLM agents accumulate interaction histories far larger than any context window, forcing a standing decision: what to encode deeply, what to forget, and what to retrieve under a fixed memory budget. Production systems answer with semantic similarity or recency -- both mis-specified for the forgetting decisi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-07-31 · Jiajia Tang, Sizhe Yuen, Francisco Gomez Medina, Yali Du, Adam Sobey
Research Track A · General AI
Parameter-Efficient Fine-Tuning (PEFT) commonly adapts large language models using a single shared Low-Rank Adapter (LoRA). This shared optimization space often suffers from interference when adapting heterogeneous task sequences, leading to poor transfer and catastrophic forgetting. Existing approaches mainly improve …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 20.0
2026-08-12 · Tieliang Gong, Zhongbo Zhang, Wen Wen, Yong-Jin Liu
Research Track A · General AI
Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting. Yet its generalization behavior is shaped by two coupled effects that existing analyses fold into a single hypo…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.8
2026-06-23 · Wei Zhou, Xuanhe Zhou, Shaokun Han, Hongming Xu, Guoliang Li, Zhiyu Li, Feiyu Xiong, Fan Wu
Research Track A · General AI
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluati…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.8
2026-07-13 · Jinxiu Liu, Jianru Li, Tanqing Kuang, Xuanming Liu, Kangfu Mei, Yandong Wen, Weiyang Liu
Research Track A · General AI
Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing monolithic models remain fundamentally constrained by their inability to learn cumulatively and evolve autonomously, which is a limitation we term the "perpetual novice"…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.8
2026-08-12 · Nicholas E. Kyrkewood
Research Track A · General AI
Gist-based context compression---summarising older conversation history into compact representations---is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood. We use Salience-Weighted Consolidation (SWC), a biologically-inspired compression…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.5
2026-04-24 · Yuelin Hu, Zhenbo Yu, Zhengxue Cheng, Wei Liu, Li Song
Research Track A · General AI
Many continual-learning methods modify gradients upstream (e.g., projection, penalty rescaling, replay mixing) while treating Adam as a neutral backend. We show this composition has a hidden failure mode. In a high-overlap, non-adaptive 8-domain continual LM, all shared-routing projection baselines collapse close to va…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.5
2026-05-03 · Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
Research Track A · General AI
Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and efficiency. However, as AI transitions from static benchmarks to real-world deployment, the traditional focus on hardware-aware efficiency is no longer sufficient. We observe…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.5
2026-05-07 · Md Anwar Hossen, Fatema Siddika, Juan Pablo Munoz, Tanya Roosta, Ali Jannesari
Research Track A · General AI
Large language models (LLMs) can acquire new capabilities through fine-tuning, but continual adaptation often leads to catastrophic forgetting. We propose CRAFT, a continual learning framework that avoids updating model weights by instead learning low-rank interventions on hidden representations. CRAFT proceeds in thre…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.5
2026-06-29 · Yiting Hu, Lingjie Duan, Qian Zhang
Research Track A
Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant challenge in the context of continual learning (CL), where models update sequentially on dynamic datasets. A major limitation is that current certified unlearning algorithms…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.5
2026-07-02 · Qianyu Chen, Canran Xiao, Runxuan Tang
Research Track A · General AI
Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined. We study this overlooked failure mode and ask whether a continually adapted …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.4
2026-08-10 · Mind Lab, :, Vin Bo, Asher Cai, Jingwei Cao, Song Cao, Vic Cao, Amelia Chen, Andrew Chen, Kaijie Chen, Cleon Cheng, Steven Chiang, Kaixuan Fan, Hera Feng, Huan Feng, Arthur Fu, Jun Gao, Pyke Han, Nolan Ho, Ori Hong, Hailee Hou, Piers Hua, Charles Huang, Miles Jiang, Nora Jiang, Yuyi Jiang, Qiuyu Jin, Fancy Kong, Kuss Koo, Jaron Lee, Andrew Lei, Alexy Li, Dawn Li, Lucian Li, Ray Li, Ricardo Li, Smith Li, Theo Li, Allen Lin, Elliot Lin, Fan Lin, Chen Ling, Kairus Liu, Kieran Liu, Logan Liu, Neo Liu, Xiang Liu, Yuxin Lu, Maeve Luo, Pony Ma, Verity Niu, Cole Qiao, Guian Qiu, Vince Qu, Sentry, Niko Song, Vincent Wang, Bo Wu, Rio Yang, Evelyn Ye, Fiona Ye, Ina Ye, Regis Ye, Josh Ying, Atlas Zeng, Danney Zeng, Salmon Zhan, Anya Zhang, Di Zhang, Mia Zhang, Sueky Zhang, Wei Zhao, Ada Zhou, Adrian Zhou, Yuhua Zhou, Juno Zhu, Murphy Zhuang
Research Track A · General AI
Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized around two system goals. Adaptation is pursued through recursive improvement of versioned model-harness pairs, where experience from one configu…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.3
2026-04-21 · Md Nayem Uddin, Kumar Shubham, Eduardo Blanco, Chitta Baral, Gengyu Wang
Research Track A · General AI
Personalized agents that interact with users over long periods must maintain persistent memory across sessions and update it as circumstances change. However, existing benchmarks predominantly frame long-term memory evaluation as fact retrieval from past conversations, providing limited insight into agents' ability to …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.3
2026-05-20 · Wujiang Xu, Yu Wang, Kai Mei, Kaiqu Liang, Zhenting Wang, Mingyu Jin, Han Zhang, Shi-Xiong Zhang, Wenyue Hua, Sambit Sahu, Dimitris N. Metaxas
Research Track A · Research Track B · General AI
Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-turn chat scenarios, overlooking the dynamic memory formation that occurs during extended agent execution. Consequently, the memory systems …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.3
2026-06-07 · Ruoyu Yao, Pei Liu, Ruiguo Zhong, Mingxing Peng, Rui Yang, Jun Ma
Research Track A · General AI
While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high computational overhead, and static learning paradigms. To address these challenges, we propose LUNA-AD, a lightweight uncertainty-aware langu…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.0
2026-03-30 · Tiantian Wang, Xiang Xiang, Simon S. Du
Research Track A · General AI
In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits n…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.0
2026-04-20 · Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, William A. P. Smith, Yue Lu
Research Track A · General AI
In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off through regularization, penalizing changes to parameters critical for previous tasks. In most cases, this regularization term is directly added to the training loss and o…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 19.0
2026-05-06 · Yazheng Liu, Yuxuan Wan, Rui Xu, Xi Zhang, Sihong Xie, Hui Xiong
Research Track A · General AI
Large language models (LLMs) often suffer from catastrophic forgetting in continual learning: after learning new tasks sequentially, they perform worse on earlier tasks. Existing methods mitigate catastrophic forgetting by data replay, parameter freezing, or regularization. However, these methods lack semantic awarenes…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.8
2026-08-13 · Dongfang Li, Zixuan Liu, Junmai Wang, Jiahe Huang, Fuhao Li, Bonian Jia, Baotian Hu, Min Zhang
Research Track A · General AI
Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager consolidation, invoking LLMs after each interaction to extract, summarize, or update memories. This design makes memory construction increasingly costly as conversations grow…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.8
2026-08-13 · Ruizhe Li, Licheng Zhang, Benfeng Xu, Mingxuan Du, Zheren Fu, Weidong Chen
Research Track A · General AI
Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or knowledge graphs before any question is asked. We ask how much of that benefit comes from the structure itself, rather than from competent retrieval over the raw history.…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.8
2026-08-16 · Brian B. Moser, Ahmed Anwar, Tobias Christian Nauen, Shishir Muralidhara, Federico Raue, René Schuster, Stanislav Frolov, Andreas Dengel
Research Track A
Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically diagonal Fisher values. Per-parameter looks more flexible than per-layer, but each layer's diagonal Fisher is a weak summary of its actual curvature, missing the top-eig…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.5
2026-06-04 · Parth Asawa, Christopher M. Glaze, Gabriel Orlanski, Ramya Ramakrishnan, Benji Xu, Asim Biswal, Vincent Sunn Chen, Frederic Sala, Matei Zaharia, Joseph E. Gonzalez
Research Track A · General AI
Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We introduce Continual Learning Bench (CL-Bench), the first difficult, expert-validated benchmark designed to measure whether LLM-based systems…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.5
2026-06-19 · Yu Luo
Research Track A · General AI
Social intelligence is a core competency for language agents, yet current research primarily focuses on static capability evaluation rather than how these skills are continuously shaped and accumulated. This gap calls for a shift toward sustainable learning paradigms. Currently, two methodological pain points exist: so…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.5
2026-06-23 · Ahmed Anwar, Andreas Wagner, Federico Raue, Tobias Nauen, Andreas Dengel
Research Track A
Accuracy degradation is the standard metric for Catastrophic Forgetting (CF), however, it records only whether forgetting occurred or not. It saturates at the extremes and collapses discretely at task boundaries, hiding the internal structure of what is being forgotten. We introduce six softmax-derived metrics spanning…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.4
2026-08-05 · Azizi Ariffin, Afif Haris, Faiz Zaki, Hazim Hanif, Nor Badrul Anuar
Research Track A
Network intrusion detection systems (IDS) trained on fixed traffic snapshots decay silently after deployment as threat distributions shift. Fine-tuning models on new attacks triggers catastrophic forgetting, while retraining from scratch is computationally infeasible. Replay-based continual learning counters this, but …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.3
2026-03-26 · Cristian Lupascu, Alexandru Lupascu
Research Track A · General AI
Large Language Model based agents increasingly operate in high stakes, multi turn settings where factual grounding is critical, yet their memory systems typically rely on flat key value stores or plain vector retrieval with no mechanism to track the provenance or trustworthiness of stored knowledge. We present Elephant…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.3
2026-07-21 · Rui Cheng, Meixing Shi, Yuxiang Cai, Jingcai Guo, Jianwei Yin, Zhi Chen
Research Track A · General AI
Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remains underexplored. In real-world deployments, video data often arrives continuously from heterogeneous domains, requiring the model to acquire new domain-specific knowled…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.3
2026-08-15 · Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng, Seongdeok Bang, Octavia Camps
Research Track A · General AI
Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. For continual anomaly detection where tasks arrive sequentially, extending greedy sampling is straightforward with unbounded memory through co…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.3
2026-08-17 · Reza Bayat, Ali Behrouz, Vahab Mirrokni, Aaron Courville
Research Track A · General AI
The quadratic cost of attention-based sequence models for long contexts has motivated a growing line of research on memory-based models that can compress context into a compact state. However, most existing memory models expose a static memory throughout the entire sequence. Because early tokens face no compression pre…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-05-06 · Andreas Pattichis, Constantine Dovrolis
Research Track A · General AI
LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicitly rather than letting it adapt on its own. Biological memory works differently: coupled multi-timescale dynamics make new associations immediately usable, strengthen wha…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-05-11 · Qianqian Shi, Yue Che, Faqiang Liu, Hongyi Li, Mingkun Xu, Sandra Reinert, Pieter M. Goltstein, Rong Zhao, Luping Shi
Research Track A
Adaptive behavior requires the brain to transition between distinct contexts while maintaining representations of prior experience. The ability to reconfigure neural representations without erasing previously acquired knowledge is central to learning in dynamic environments, yet the neural mechanisms that support this …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-05-18 · Ali Zindari, Xiaowen Jiang, Rotem Mulayoff, Sebastian U. Stich
Research Track A · General AI
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method, yet its learned correction is static: the same low-rank update is applied to every input. This input-agnostic approach creates an inevitable compromise between adapting to the fine-tuning distribution and preserving pre-trained behavior…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-05-28 · Runze Xu, Arpit Garg, Hemanth Saratchandran, Simon Lucey
Research Track A · General AI
Low-Rank Adaptation (LoRA) has become one of the most widely used fine-tuning mechanisms for adapting large language models to new domains, tasks, and users. Yet adaptation performance alone can obscure an important failure mode: LoRA updates may improve performance on the target distribution while degrading prior capa…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-06-03 · Jiahua Dong, Wenqi Liang, Hongliu Li, Yang Cong, Duzhen Zhang, Hanbin Zhao, Henghui Ding, Yulun Zhang, Salman Khan, Fahad Shahbaz Khan
Research Track A · General AI
Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts. However, the majority of CDMs unrealistically presume that the user's collection of personalized concepts is static and incapable of incremental growth over time. Furthermore, they …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-06-13 · Xinze Zhang
Research Track A · General AI
Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making. Yet models trained on a few well-photographed metropolises systematically misjudge underrepresented districts, propagating geographic bias into downstream policy. We address this gap with H…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-06-15 · Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou, Yankun Hong, Xialiang Tong, Mingxuan Yuan, Tong Wei, Min-Ling Zhang
Research Track A · General AI
Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge. This paper presents KeepLoRA++, balancing these objectives through a u…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-06-24 · Zhihao Gu, Lin Wang
Research Track A · General AI
Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge. Previous works alleviate the catastrophic forgetting problem by parameter-efficient fine-tuning for single-task adaptation. However, they fail to extract reusable skills and model the interactio…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 18.0
2026-08-11 · Kushal Chakrabarti
Research Track A · General AI
Agentic coding READMEs like CLAUDE.md grow without bound in real repositories, stopping only when the repository retires or someone rewrites the file wholesale. We trace this to imperfect recall: appending an instruction is always cheap, but once an instruction's rationale is gone, deleting it without risking a correct…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.8
2026-07-15 · Zihao Yu, Xiu Yuan, Chongjie Zhang
Research Track A · General AI
Long-horizon robot planning requires more than predicting what actions will do next; it also requires memory of the embodied experience that makes future goals interpretable. People do not plan from the present scene alone: they draw on remembered places, object-state changes, prior procedures, and regularities reveale…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-03-15 · Xudong Wang, Gan Li, Zhiyu Liu, Yao Wang, Lianqing Liu, Zhi Han
Research Track A · General AI
Deploying vision-and-language navigation (VLN) agents requires adaptation across diverse scenes and environments, but fine-tuning on a specific scenario often causes catastrophic forgetting in others, which severely limits flexible long-term deployment. We formalize this challenge as the all-day multi-scenes lifelong V…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-04-29 · Qisheng Hu, Quanyu Long, Wenya Wang
Research Track A · General AI
Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-plasticity dilemma of parametric learning. We show that this challenge does not disappear but resurfaces at the memory…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-05-07 · Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
Research Track A · General AI
In recent years, the integration of non-topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio-temporal information in non-Euclidean graphs. However, most existing methods rely on static underlying graph structures, which are inadequate for capturing the cont…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-05-11 · Debashis Guha
Research Track A · General AI
Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose \emph{Consolidation-Expansion Operator Mechanics} (OpMech), a framework that makes this structure precise. The central object is the \emph{order-gap} $\Ogap(θ; e)$, the d…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-06-09 · Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni
Research Track A · General AI
Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are inherently non-stationary, …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-06-14 · Shuaike Zhang, Shaokun Wang, Haoyu Tang, Jianlong Wu, Liqiang Nie
Research Track A · General AI
Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. Compared with conventional continual learning, ECL suffers from more severe catastrophic forgetting. Feature drift accumulated under closed-loop c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-06-22 · Chuangxin Zhao, Canran Xiao, Siyuan Ma, Mengyao Lyu, Yanbiao Ma, Jun Xia, Guiguang Ding, Yang Liu
Research Track A · General AI
Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instructions, yet continual fine-tuning often causes severe forgetting of previously acquired multimodal skills. Existing continual vision-language methods mainly preserve ou…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.5
2026-07-31 · Douwe den Blanken, Martin Lefebvre, Charlotte Frenkel
Research Track A
With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keyword spotting) or patients (e.g., adaptive health monitoring). Yet, most edge devices rely on fixed inference algorithms and thus cannot learn on-device to personalize pred…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 17.4
2026-08-07 · Guiyu Zhao, Longteng Guo, Yanghong Mei, Zilin Zhu, Yu Zhang, Bin Cao, Mingming Yu, Xingjian He, Jie Jiang, Jing Liu
Research Track A · General AI
While Vision-Language-Action (VLA) models have advanced embodied AI, their fundamentally reactive paradigm severely limits performance in partially observable and long-horizon tasks. When restricted to a single wrist-mounted camera, they inevitably suffer from perception forgetting as objects exit the field of view, an…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-04-12 · Cheng-Yen Li, Xuanjun Chen, Claire Lin, Wei-Yu Chen, Wenhua Nie, Hung-Yi Lee, Jyh-Shing Roger Jang
Research Track A · General AI
Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While Retrieval-Augmented Generation (RAG) grounds generation in external sources, existing methods often treat evidence as isolated units, failing to reconstruct the logical c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-04-18 · Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
Research Track A · General AI
Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. We present AutoPKG, a multi-agent Large Language Model (LLM) framework that automatically constructs a Product-attribute Knowledge Graph (PKG) from multimodal product content. AutoPKG indu…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-05-27 · Jizhan Fang, Buqiang Xu, Zhixian Wang, Haoliang Cao, Xinle Deng, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu, Ying Wei, Guozhou Zheng, Feiyu Xiong, Haofen Wang, Huajun Chen, Ningyu Zhang
Research Track A · General AI
Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be co…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-07-20 · Qingcan Kang, Mingyang Liu, Shixiong Kai, Kaichao Liang, Zhentao Tang, Yuqi Cui, Tao Zhong, Mingxuan Yuan
Research Track A · General AI
Language agents depend on memory across interactions. However, the limited context windows of large language models (LLMs) and their inference costs constrain how much memory can be used at once. Existing systems mainly follow two strategies: memory retention and memory consolidation. Retention keeps raw records and pr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-07-30 · Qiming Shi, Yibo Dou, Jiawen Zhu, Yulong Tao, Linbo Jin, Zhaolu Kang, Yunfan Zhou, Di Weng
Research Track A · General AI
Skill-based prompting has become a practical mechanism for improving large language model (LLM) agents, yet existing skill acquisition methods often treat skills as experience summaries, memory entries, or direct summaries of successful demonstrations. This creates a mismatch for weaker student agents: when a student f…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.3
2026-07-30 · Mo Li, Zixin Yin, Ting Cao, Yunxin Liu
Research Track A · General AI
While Large Language Models (LLMs) demonstrate remarkable general instruction-following capabilities, they often fall short of human experts in highly specialized, open-ended domains such as creative screenwriting. Prior approaches typically adopt post-training, yet both supervised fine-tuning and reinforcement learnin…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.2
2026-08-05 · Zehua Chen, Junyou Wang, Yuxuan Jiang, Zhenying Fang, Yusheng Dai, Jianfei Chen, Ziwei Liu, Jun Zhu
Research Track A · General AI
Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis. However, existing conditional diffusion-based V2A methods typically enhance visual conditioning with additional audio-visual supervision, acoustic structure…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.0
2026-04-18 · Zhaokang Liao, Yingguo Gao, Yi Yang, Yongheng Hu, Jingting Ding
Research Track A · General AI
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach to improve the reasoning abilities of Large Language Models (LLMs). Among RLVR algorithms, Group Relative Policy Optimization (GRPO) and its variants have demonstrated strong performance and high training efficiency. However, GRPO…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 17.0
2026-07-13 · Paul A. Bereuter, Mark D. Plumbley, Alois Sontacchi
Research Track A
State-of-the-art speech enhancement models benefit from large-scale labeled datasets, whereas singing voice separation models suffer from limited available training data. To address this limitation, we formulate singing voice separation as domain adaptation from speech enhancement to singing voice separation. We invest…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.9
2026-08-10 · Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Yuntao Liu, Xing Xu, Honggang Zhang
Research Track A · General AI
Large language model (LLM) agents provide a promising interface for command-line-based network operations, but a plausible command may still fail or introduce operational risk after execution. Existing approaches mainly focus on command generation or final configuration correctness, and do not use execution-grounded ex…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.8
2026-07-08 · Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai
Research Track A · General AI
As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem of context management and mitigating forgetting. We argue this framing is incomplete: continual learning is fundamentally about increasing mo…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-04-06 · Varun Pratap Bhardwaj
Research Track A · General AI
AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with single-channel retrieval, require cloud LLMs for core operations, and implement none of the cognitive processes that make human m…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-04-07 · Guhao Feng, Shengjie Luo, Kai Hua, Ge Zhang, Di He, Wenhao Huang, Tianle Cai
Research Track A · General AI
The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast we…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-04-26 · Alexander Bering
Research Track A · General AI
Despite a century of empirical memory research, existing AI agent memory systems rely on system-engineering metaphors (virtual-memory paging, flat LLM storage, Zettelkasten notes), none integrating principles of consolidation, forgetting, and reconsolidation. We present ZenBrain, a multi-layer memory architecture integ…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-05-01 · Steven Tang, Xinze Xiong, Anna Hakhverdyan, Andrew Patterson, Jacob Adkins, Jiamin He, Esraa Elelimy, Parham Mohammad Panahi, Martha White, Adam White
Research Track A · General AI
In continual reinforcement learning (CRL), good performance requires never-ending learning, acting, and exploration in a big, partially observable world. Most CRL experiments have focused on loss of plasticity -- the inability to keep learning -- in one-off experiments where some unobservable non-stationarity is added …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-05-06 · Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera, Stjepan Picek, Saraga Sakthidharan
Research Track A · General AI
The open-source ecosystem has accelerated the democratization of Large Language Models (LLMs) through the public distribution of specialized Low-Rank Adaptation (LoRA) modules. However, integrating these third-party adapters often induces catastrophic forgetting of the base model's foundational safety alignment. Restor…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-06-10 · Ahmed Sharshar, Naveen Kumar Kummari, Mohsen Guizani
Research Track A · General AI
Continual learning (CL) models often use experience replay to reduce catastrophic forgetting, but their robustness to replay sampling interference remains underexplored. Existing CL attacks alter inputs or training pipelines (poisoning/backdoors) and rarely include explicit auditable constraints, limiting realism. Here…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-06-19 · Jianwei Lou
Research Track A · General AI
Continual learning that is gradient-free, local, online, and append-only is attractive for edge and streaming deployment, but its value is usually argued informally. We give a provable account on recurring-regime streams. Given segmentation, a warm-start library learner attains amortized recovery cost $O\!\big(KD/\vare…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-06-23 · Beining Wu, Zihao Ding, Jun Huang, Yanxiao Zhao
Research Track A · General AI
On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights. This memory is hard-bounded and exposed: it consumes RAM and energy, reaches peers through a thin uplink, and becomes an attack surface because it is writable by what the agent reads. Existing systems…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-07-24 · Yunan Zhang, Yang Fan, Heng Li, Xiangping Wu, Qingcai Chen
Research Track A
Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired ones. However, existing methods suffer from catastrophic forgetting and insufficient exploitation of shared information across tasks. This paper proposes FSE, a Fast-Slow …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-07-29 · Keegan Harris, Brian W. Lee, Ian Waudby-Smith, Philip Amortila, Nika Haghtalab, Michael I. Jordan
Research Track A · General AI
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy. A standard way to balance this trade-off is via a KL-regularized RL objective, although this formulation does not by itself provide a principled w…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-07-30 · Zihao Cai, Xinghan Li, Ruiyan Yang, Xue Song, Haijun Shan, Jingjing Chen
Research Track A
As generative models continue to evolve, AI-generated image detectors must incrementally adapt to emerging generative domains while preserving knowledge acquired from previous ones. This continual learning setting is particularly challenging because forensic traces are often subtle and generator-specific, making detect…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-08-13 · Zeyang Zhang, Tieliang Gong, Junyan Lu, Weizhan Zhang
Research Track A · General AI
Plasticity loss has emerged as a critical challenge in continual learning that significantly hinders the acquisition of sequential tasks. While optimizing activation designs offers a potential solution, current fixed-form functions suffer from an inherent spectral bias towards low-frequency variations, whereas learnabl…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.5
2026-08-17 · WooJoo Kim, HyunSik Yoo, JunYoung Kim, JaeHyung Lim, SeongKu Kang, HwanJo Yu
Research Track A
Continual recommendation aims to capture evolving user interests from streaming data but struggles with sparsity. LLM enhancers mitigate this with semantic knowledge, but naive integration creates a new conflict. We identify this as the Stability-Plasticity-Cognitivity (SPC) Trilemma, where generalized LLM semantic pri…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-03-31 · Yang Shen, Zhenyi Yi, Ziyi Zhao, Lijun Sun, Dongyang Li, Chin-Teng Lin, Yuhui Shi
Research Track A · General AI
As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optimal multi-agent coordination framework for these autonomous agents remains largely unexplored. In this paper, we present …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-04-22 · Naizhong Xu
Research Track A · General AI
Modern retrieval-augmented generation (RAG) systems treat vector embeddings as static, context-free artifacts: an embedding has no notion of when it was created, how trustworthy its source is, or which other embeddings depend on it. This flattening of knowledge has a measurable cost: recent work on VersionRAG reports t…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-04-27 · Mofei Li, Taozhi Chen, Guowei Yang, Jia Li
Research Track A · General AI
Large Language Models (LLMs) excel at general code generation, but their performance drops sharply in enterprise settings that rely on internal private libraries absent from public pre-training corpora. While Retrieval-Augmented Generation (RAG) offers a training-free alternative by providing static API documentation, …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-04-30 · Binyan Xu, Xilin Dai, Kehuan Zhang
Research Track A · General AI
Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they implement lookup. We argue that treating lookup as memory is a category error with provable consequences for agent capability, long-term learning, and security. Retrie…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-05-29 · Tao Zou, Yichen He, Tian Qiu, Yuan Lin, Hang Li
Research Track A · General AI
Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, constructing effective memory goes beyond memory module design and basic requirements such as accuracy and fidelity; the key challenge lies in determining what to memori…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.3
2026-06-15 · Haonan Ge, Yiwei Wang, Hang Wu, Yujun Cai
Research Track A · General AI
Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets. Existing methods address this by adding memory banks, retrieval modules, or visual token compression to preserve long-range history. …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-05 · Gunn Kim
Research Track A · General AI
Continual learning in artificial neural networks is fundamentally limited by the stability--plasticity dilemma: systems that retain prior knowledge tend to resist acquiring new knowledge, and vice versa. Existing approaches, most notably elastic weight consolidation~(EWC), address this empirically without a physical ac…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-07 · Manuel Barusco, Francesco Borsatti, David Petrovic, Davide Dalle Pezze, Gian Antonio Susto
Research Track A · General AI
Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively studied, two key challenges remain largely unaddressed in conjunction: edge deployment, where computational resources are severely constrained, and continual learning, w…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-08 · Mohamed Rabie, Chinthana Panagamuwa, Konstantinos G. Kyriakopoulos
Research Track A
Reliable radar pulse classification is essential in Electromagnetic Warfare for situational awareness and decision support. Deep Neural Networks have shown strong performance in radar pulse and RF emitter recognition; however, on their own they struggle to efficiently learn new pulses and lack mechanisms for expressing…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-09 · Danit Yanowsky, Daphna Weinshall
Research Track A · General AI
Catastrophic forgetting remains a key challenge in Continual Learning (CL). In replay-based CL with severe memory constraints, performance critically depends on the sample selection strategy for the replay buffer. Most existing approaches construct memory buffers using embeddings learned under supervised objectives. Ho…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-19 · Ou Wu
Research Track A · General AI
Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved samples through selection, augmentation, or poisoning, while the latter tunes model weights via masking, quantization, or low-rank adaptation. This paper establishes a un…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-04-20 · Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros
Research Track A
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-05-04 · Joern Hentsch
Research Track A · General AI
Continual learning systems face a fundamental tension between plasticity -- acquiring new knowledge -- and stability -- retaining prior knowledge. We introduce MPCS (Multi-Plasticity Continual System), a neuroplastic architecture that integrates eleven complementary mechanisms: task-driven neurogenesis, Fourier-encoded…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-05-12 · Rodney A Sanchez, Ferat Sahin, Alex Ororbia, Jamison Heard
Research Track A · General AI
Advancements in reinforcement learning have produced a variety of complex and useful intrinsic driving forces; crucially, these drivers operate under a direct conditioning paradigm. This form of conditioning limits our agents' capacity by restricting how they learn from the environment as well as from others. Off-polic…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-05-21 · Dianzhi Yu, Vireo Zhang, Hongru Wang, Yanyu Chen, Minda Hu, Wanghan Xu, Siki Chen, Philip Torr, Zhenfei Yin, Irwin King
Research Track A · General AI
Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting previously acquired abilities. Existing approaches either internalize knowledge by updating model parameters, which induces catastrophic forgetting, or rely on external m…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-05-28 · Kellian Cottart, Théo Ballet, Djohan Bonnet, Damien Querlioz
Research Track A · General AI
Always-on edge systems must keep learning as conditions change under tight compute budgets and must detect unreliable predictions. Bayesian binary neural networks are attractive in this setting, but mean-field Bernoulli posteriors can saturate on long non-stationary streams, wiping out epistemic uncertainty and freezin…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-06-07 · Nazreen Shah, Govinda Arya, Bharath B. N., Ranjitha Prasad
Research Track A · General AI
In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch. Continual learning (CL) addresses this challenge by incorporating new tasks while mitigating catastrophic forgetting, where learning new information degr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-06-10 · Dayananda Herurkar, Federico Raue, Joachim Folz, Jörn Hees, Andreas Dengel
Research Track A · General AI
Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance. In many real-world applications, data arrive sequentially from diverse domains, rendering conventional continual lea…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-06-12 · Oxana Salish, Kuniyilh S
Research Track A
Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility. While continual adaptation is essential for long-lived IoT deployments where data patterns evolve, it also…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-06-19 · Jiacheng Wang, Xinjia He, Qi Ding, Yutao Yang, Jie Zhou, Liyang Yu, Liang Dou, Qin Chen
Research Track A · General AI
Continual learning (CL) is commonly studied under the assumption that sequential tasks are semantically related or structurally similar. However, in highly heterogeneous settings, where tasks differ substantially in reasoning patterns and input-output formats, existing methods often suffer from catastrophic forgetting …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-06-29 · Haoliang Han
Research Track A · General AI
Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone. Retrieval systems can reinsert past text, but they do not by themselves show that an experience has been selectively consolidated into the model's own behavior. We introduce EVAF, an Echo-Valenc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 16.0
2026-07-02 · Meng Wang, Haohan Zhao, Wenzhuo Liu, Lu Yang, Geng Liu, Haiyang Guo, Guo-Sen Xie, Gaofeng Meng, Hongbin Liu, Fei Zhu
Research Track A · General AI
Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgetting, with on-policy self-distillation emerging as a particularly attractive approach. In this work, we revisit this optimistic view through …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.9
2026-08-09 · Emma Graham
Research Track A · General AI
This work explores the relationship between task similarity and catastrophic forgetting in reinforcement learning. Catastrophic forgetting, the phenomenon in machine learning of losing the ability to effectively perform on previous tasks, is a significant impediment to continual learning. This study aims to understand …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.9
2026-08-10 · Jiahong Liu, Ming Shen, Xiaohao Liu, Rex Ying, Menglin Yang, Tat-Seng Chua, Irwin King
Research Track A · General AI
Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic structure across modalities. Despite this progress, how such representations behave under continual learning poses fundamentally different challenges that remain underexplo…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-03-29 · Chongyang Zhao, Mingsong Li, Haodong Lu, Dong Gong
Research Track A · General AI
Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge. Mixture of Experts (MoE) architectures naturally facilitate this by incrementally adding new experts and expanding routers while keeping th…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-04-03 · Lei Song, Shihan Guan, Youyong Kong
Research Track A · General AI
Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably precipitates feature drift. As…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-04-06 · Seoyoung Park, Haemin Lee, Hankook Lee
Research Track A · General AI
Task-free online continual learning has recently emerged as a realistic paradigm for addressing continual learning in dynamic, real-world environments, where data arrive in a non-stationary stream without clear task boundaries and can only be observed once. To consider such challenging scenarios, many recent approaches…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-04-09 · Zhuang Qi, Ying-Peng Tang, Lei Meng, Guoqing Chao, Lei Wu, Han Yu, Xiangxu Meng
Research Track A
Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representative samples from past tasks. Existing studies focus on designing sample-importance estimation mechanisms to identify information-rich samples. However, they typically ove…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-02 · Wenhao Li, Xiu Su, Yichao Cao, Hongyan Xu, Xiaobo Xia, Shan You, Yi Chen, Chang Xu
Research Track A · General AI
Vision-language-action (VLA) models have advanced the field of embodied manipulation by harnessing broad world knowledge and strong generalization. However, current VLA models still face several key challenges, including limited reasoning capability, lack of status monitoring, and difficulty in self-correction. In this…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-12 · Minjong Cheon
Research Track A · General AI
Catastrophic forgetting remains the central obstacle in continual learning (CL): parameters shared across tasks interfere with one another, and existing regularization methods such as EWC and SI apply uniform penalties without awareness of which input region a parameter serves. We propose KAN-CL, a continual learning f…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-12 · Neha Verma, Nikhil Mehta, Shao-Chuan Wang, Naijing Zhang, Alicia Tsai, Li Wei, Lukasz Heldt, Lichan Hong, Ed Chi, Xinyang Yi
Research Track A · General AI
Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, language-based reasoning abilities. This work investigates and addresses this challenge in the context of the Generative Retrieval (GenRetrieval) t…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-20 · Kei Hiroshima, Kento Uchida, Shinichi Shirakawa
Research Track A · General AI
Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-trained models (LPMs) and model merging techniques, such as MAGMAX, have demonstrated effective CL performance by combining task-specific par…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-27 · Elvin Hajizada, Michael Neumeier, Edward Paxon Frady, Yulia Sandamirskaya, Axel von Arnim, Bing Li, Eyke Hüllermeier
Research Track A · General AI
Recognizing and continuously learning novel human actions without forgetting prior classes is a requirement for emerging AR/VR and robotics applications. For these applications, both on-device processing and learning are essential for privacy and low-latency adaptation. Event cameras address the efficiency of visual se…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-05-28 · Kajetan Schweighofer, Conor F. Hayes, Roberto Dailey, Risto Miikkulainen, Xin Qiu
Research Track A · General AI
Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning, offering advantages through simplicity, scalability, and inference-only training. However, recent work suggests that ES fine-tuning on new tasks may induce forgetting of…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-06-07 · Anthony Bazhenov, Jean Erik Delanois, Giri P. Krishnan
Research Track A
One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to interference and forgetting of the previous ones. While several algorithms have been proposed to protect old memories from interference, they are typically applied during or …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-06-15 · Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li
Research Track A · General AI
Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic. Although deep learning and tree-based ensemble methods can achieve high accuracy, their black-box nature remains a major obstacle to cl…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-07-08 · Gunner Levi Howe
Research Track A · General AI
On analog neuromorphic hardware, intrinsic device noise is normally an accuracy tax. We ask whether it can instead consolidate memories. We cast per-synapse consolidation as a Doob h-transform: condition each weight's stochastic dynamics on never crossing a memory-critical barrier around its consolidated value. The con…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-07-14 · Richmond Alake, Cesare Bernardis, Paul Cayet, Luca Engel, Damien Hilloulin, Sungpack Hong, Allen Hosler, Nickolas Kavantzas, Ingo Kossyk, Son Le, Rhicheek Patra, Kartik Talamadupula, Valentin Venzin
Research Track A · General AI
Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and preferences across sessions, and accumulation of procedural knowledge from prior outcomes. These requirements extend beyond document retriev…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-07-22 · Yubiao Ma, Han Yu, Kai Guo, Changtai Lv, Zhengquan Mao, Boyang Xing, Xuemei Ren, Dongdong Zheng
Research Track A
Humans can progressively acquire highly dynamic motor skills while preserving reliable everyday motor abilities. In contrast, existing humanoid controllers face a trade-off between generalist and specialist capabilities: generalist motion tracking policies struggle to reliably execute rare highly dynamic motions, where…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 15.5
2026-07-24 · Haoyuan Wu, Aoqi Wu, Hai Wang, Jiajia Wu, Jinxiang Ou, Bei Yu
Research Track A · General AI
Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world. Native multimodal pre-training avoids this limitation by training models from scratch on multimodal inputs, thereby achieving deep cross-mo…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-08-02 · Jeong Min Kong, Richard S. Sutton
Research Track A
Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively, have recently been explored in offline continual learning with a particular focus on catastrophic forgetting. Driven by the observations that 1) online continual learn…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.5
2026-08-03 · Zixuan Wang, Xingyu Dang, Rui-Jie Zhu, Zixin Wen, Hengyu Fu, Wenhao Chai, Jason D. Lee
Research Track A · General AI
Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later. Test-time training (TTT) is an appealing approach that performs online parameter updates for long-context modeling, yet existing TTT methods only optimize either reconstruc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.3
2026-04-02 · Payal Fofadiya, Sunil Tiwari
Research Track A · General AI
Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes temporal decay and false memory propagation. Benchmarks such as LOCOMO and LOCCO report performance degradation from 0.455 to 0.05 across stages, while MultiWOZ shows 78.2% accuracy with 6.8% false …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.3
2026-04-13 · Stefan Miteski
Research Track A · General AI
Retrieval-Augmented Generation remains the dominant pattern for giving LLMs persistent memory, but a visible cluster of personal wiki-style memory architectures emerged in April 2026 -- design proposals from Karpathy, MemPalace, and LLM Wiki v2 that compile knowledge into an interlinked artifact for long-term use by a …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.3
2026-04-22 · Yuxuan Cai, Jie Zhou, Qin Chen, Liang He, Wei Li, Xin Li, Bo Zhang
Research Track A · General AI
Online lifelong learning enables agents to accumulate experience across interactions and continually improve on long-horizon tasks. However, existing methods typically treat retrieval from past experience as a passive operation, triggering it only at task initialization or after completing a step. Consequently, agents …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.2
2026-08-04 · Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du
Research Track A · General AI
Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems. However, existing studies primarily focus on forgetting within individual modalities. Alth…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.2
2026-08-06 · Fardin Afdideh, Fernando Seoane, Farhad Abtahi
Research Track A · General AI
Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-efficient adaptation, alignment, retrieval augmentation, model editing, unlearning, calibration, and Multimodal Instruction Tuning. However, the literature remains fragmente…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.2
2026-08-10 · Björn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska, Przemysław Uznański, Junlin Jiang, Rohan Phadke, Remigiusz Kinas, Richard Zhong
Research Track A · General AI
We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermedi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-04-08 · Ziqiao Ma, Xueyang Yu, Haoyu Zhen, Yuncong Yang, Joyce Chai, Chuang Gan
Research Track A · General AI
Large Chunk Test-Time Training (LaCT) has shown strong performance on long-context 3D reconstruction, but its fully plastic inference-time updates remain vulnerable to catastrophic forgetting and overfitting. As a result, LaCT is typically instantiated with a single large chunk spanning the full input sequence, falling…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-04-14 · Yifei Yan, Linqi Ye
Research Track A · General AI
As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence. Existing approaches either rely on complex topology adjustments in Mixture-of-Experts (MoE) models…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-04-22 · Saish Sachin Shinde
Research Track A · General AI
We present SCM (Sleep-Consolidated Memory), a research preview of a memory architecture for large language models that draws on neuroscientific principles to address a fundamental limitation in current systems: the absence of persistent, structured, and biologically plausible memory. Existing approaches rely on truncat…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-06-09 · Bocheng Ju, Jianhua Wang, Chengliang Liu, Xiaolin Chang
Research Track A · General AI
Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities. Many unlearning objectives focus on suppressing undesired answers, while recent target-guided variants specify replacement behavior but still leave update locality largely unconstrained. This paper in…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-07-17 · Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen
Research Track A
Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch. Existing methods, whether rehearsal-based (replaying stored past data) or rehearsal-free (regularising or isolating parameters), overwhelmingly target one objective: preventing catastroph…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-07-21 · Simone Milani
Research Track A
Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training. Unfortunately, such approaches pose new challenges both in terms of final accuracy and…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-07-29 · Zhen Zhang, Jielei Chu, Bin Liu, Tianrui Li
Research Track A · General AI
Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current MMCL methods usually focus on cross-modal representation alignment or semantic similarity, but they overlook whether the relative contributions of individual modalities …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-07-31 · Mostafa ElAraby, Samer B. Nashed, Liam Paull
Research Track A
The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks. A similarly important though less well-studied aspect of CL systems is their ability to distinguish inputs that are unlikely to come from within the set of tasks the system has already …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 15.0
2026-08-13 · Rafal Robert Karpinski, Fethiye Irmak Dogan, Nikhil Churamani, Yiming Luo, Maartje M. A. de Graaf, Davide Dell'Anna, Hatice Gunes
Research Track A · General AI
Social robots are expected to operate across diverse environments, where similar arrangements can imply different socially appropriate actions, e.g., starting a conversation may be acceptable in a crowded home but disruptive in an office meeting. Because such norms and environments cannot all be anticipated in advance,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.9
2026-08-05 · R. Blake Lawlor, Daniel S. Brown
Research Track A · General AI
Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to changes in the environment. Model-based algorithms have much higher sample efficiency, but still fail when the environment shifts. This paper introduces Adaptive Topological …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.9
2026-08-08 · Michail Mamalakis, Carmen Jimenez-Mesa, Yonghao Li, Hao Chen, Chao Li, Antonios Mamalakis, John Suckling, Richard Bethlehem, Stephen J. Price, Richard J. Gilbertson, Pietro Lio
Research Track A · General AI
Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, populations or imaging protocols. Foundation models promise more general representations, yet they are usually pretrained once and can lose earlier capabilities when upda…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.8
2026-07-13 · Mikhail Komarov, Ivan Bondarenko, Stanislav Shtuka, Oleg Sedukhin, Roman Shuvalov, Yana Dementyeva, Matvey Solovyov, Nikolay O. Nikitin
Research Track A · General AI
Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval. RAGU, an open-source modular GraphRAG engine, addresses this by separating extraction fro…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.8
2026-08-03 · Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi
Research Track A · General AI
Cognitive AI seeks to move beyond language generation and autonomous task execution toward systems capable of sustained reasoning, adaptive behavior, persistent memory, and self-regulation. While generative and agentic AI have demonstrated impressive capabilities across a wide range of tasks, many fundamental cognitive…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.8
2026-08-03 · Zhichen Liu, Ruihan Sun, Hengjie Yang, Zipeng Wu, Zhaohan Chen, Xiaofan Zhang, Yang Xu
Research Track A · General AI
Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retrieval preserve access to selected history, but do not provide a persistent state over the full lifecycle when working context changes. We formulate this missing inferenc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.8
2026-08-12 · Congchao Wang, Diwakar Singh, Qiaozi Gao, Spyros Matsoukas, Yang Liu, Mahdi Namazifar
Research Track A · General AI
Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signals during reinforcement learning, and an in-depth understanding of reasoning behaviors during model performance evaluation. Additionally, surfacing reasoning mistakes t…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-03-06 · Xudong Wang, Jiahua Dong, Baichen Liu, Qi Lyu, Lianqing Liu, Zhi Han
Research Track A · General AI
Embodied navigation agents powered by large language models have shown strong performance on individual tasks but struggle to continually acquire new navigation skills, which suffer from catastrophic forgetting. We formalize this challenge as lifelong embodied navigation learning (LENL), where an agent is required to a…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-03-15 · Jiayuan Du, Yuebing Song, Yiming Zhao, Xianghui Pan, Jiawei Lian, Yuchu Lu, Liuyi Wang, Chengju Liu, Qijun Chen
Research Track A · General AI
End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents. To address these issues, we propose DeLL, a Deconfounded…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-04-15 · Muhammad Ahmed Ullah Khan, Muhammad Haris Bin Amir, Didier Stricker, Muhammad Zeshan Afzal
Research Track A · General AI
Continual learning enables models to acquire new knowledge over time while retaining previously learned capabilities. However, its application to text-to-3D generation remains unexplored. We present ReConText3D, the first framework for continual text-to-3D generation. We first demonstrate that existing text-to-3D model…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 14.5
2026-06-16 · Guibin Zhang, Xun Xu, Yanwei Yue, Zikun Su, Wangchunshu Zhou, Xiaobin Hu, Shuicheng Yan
Research Track A · General AI
Memory has become a standard substrate for self-evolving agents, yet retaining experience is not the same as learning how to evolve through it. Existing memory agents can store trajectories, retrieve reflections, or accumulate skills, but often lack the holistic competence to select useful experience, act on it, write …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-06-23 · Yujiang He, Frederic Uhrweiller, Bernhard Sick
Research Track A
Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors. In practice, these models face strict operational constraints: historical data …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-06 · Rai Hisada, Kanji Tanaka
Research Track A
In non-stationary streaming environments, simultaneously adapting to complex, non-linear domain shifts via continual learning while mitigating the catastrophic effects of severe, uncalibrated label noise poses a fundamental mathematical challenge. In this paper, we propose \FlatManifold{}, a novel, streamlined robust c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-07 · Nilay Kushawaha, Muhammad Sunny Nazeer, Baljinder Singh Bal, Cecilia Laschi, Egidio Falotico
Research Track A
Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their inherent compliance, flexibility, and high degrees of freedom. Modular soft robots (MSRs), composed of multiple interconnected segments, represent an emerging class of rob…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-08 · Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bartłomiej Twardowski, Szymon Łukasik, Tinne Tuytelaars
Research Track A · General AI
Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams. This has led to the development of continual self-supervised learning (CSSL), a rapidly growing area that lacks a dedicated,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-10 · Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang
Research Track A · General AI
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability. This survey provides a comprehensive overview of the green development of la…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-13 · Tingxu Yan Ye Yuan
Research Track A
Dynamic graph continual learning (DGCL) is an effective manner for handling catastrophic forgetting in dynamic graphs. However, existing DGCL methods underutilize temporal information across graph snapshots. To address this critical issue, we propose a novel framework for Dynamic Graph Continual Learning via Condensati…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-13 · Ziang Ren, Guodong Lin, Yuchen Ai, Kaize Tan, Wei-Qiang Zhang
Research Track A
Large-scale pretrained ASR models such as Whisper exhibit strong multilingual capabilities. However, fine-tuning on low-resource languages often causes catastrophic forgetting. Although continual learning mitigates this issue, existing methods struggle to regulate cross-task interference in multilingual settings, where…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.5
2026-07-14 · Enrico Gottardis, Mattia Tamiazzo, Simone Milani
Research Track A
Fake speech detectors are increasingly challenged by the development of new and more accurate generative models. To cope with this problem, continual learning techniques are nowadays widely considered feasible strategies for updating models to new datasets, but they also lead to decreased performance on previously seen…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.4
2026-08-06 · Zhiyan Hou, Dan Zhang, Tao Feng, Liyuan Wang, Wei Li, Xiangzhao Hao, Hongyan An, Junfeng Fang, Haokai Ma, Zhaohui Xu, Haiyun Guo, Jinqiao Wang, Tat-Seng Chua, Xinyu Tang
Research Track A · General AI
Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architectural designs, and weight adaptation. However, emerging paradigms are reshaping the scope of CL beyond this traditional model adaptation view…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.3
2026-04-15 · Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang
Research Track A · General AI
The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-based reviewers often generate superficial, formulaic comments lacking substantive, evidence-grounded feedback. We attribute this to the underutilization of two key compone…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.3
2026-04-28 · Qianqian Chen, Anglin Liu, Jingyang Zhang, Yudong Zhang
Research Track A · General AI
Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data privacy regulations, universal models require employing Continual Learning (CL) to adapt to evolving clinical tasks without losing previously acquired knowledge. Howev…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.3
2026-06-14 · Ali Sarabadani, Mahtab Tajvidiyan
Research Track A · General AI
Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining. We propose DYNA, a lightweight framework that augments a frozen LLM with a temporal knowledge graph where events are nodes and temporal relations are directed, timestamped edges. The graph serves as an external, …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.3
2026-07-31 · Jinghan Xu, Yiyong Xiao, Wanru Shao, Hankai Liu, Xinjin Li
Research Track A · General AI
Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context. We identify memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history or wo…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.3
2026-08-17 · Minh-Ha Nguyen, Cathy Shyr
Research Track A · General AI
Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations. Policy Iteration with Human Feedback (PIHF) builds on this development and the recurrent evalu…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.2
2026-08-04 · Tianyi Guan, Yiding Wang, Haotong Yang, Siyuan Cao, Shirui Liu, Yi Hu, Jiaqi Li, Muhan Zhang
Research Track A · General AI
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities. To bridge this gap, we introduce ContinualSkillBench, a dyna…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.2
2026-08-04 · Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang
Research Track A · General AI
Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history. However, existing memory systems typically process heterogeneous dialogue content through a uniform summarization and retrieval pipeline, leading to either excessive token consumption or irreversible loss o…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.2
2026-08-05 · Jai Malegaonkar, Rohan Patil, Henrik I. Christensen
Research Track A · General AI
In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies. Exploration bonuses and memory architectures are traditionally evaluated in isolation, leaving their interaction unmeasured, and…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.2
2026-08-07 · Yan Zhou, Yue Ouyang, Kaiyang Zheng, Suncheng Xiang
Research Track A · General AI
Long-term memory enables language agents to reuse past facts, preferences, and task experience. Persistence also creates a central falsifiability problem: when the world changes, stale memories can remain retrievable and pollute the prompt. We characterize this failure mode as memory pollution: degradation caused by ac…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-03-22 · Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
Research Track A · General AI
The continual learning literature has rapidly shifted from traditional class incremental learning (CIL) techniques to foundation model (FM)-based CIL methods without a clear understanding of how these newer approaches compare to strong, lightweight convolutional baselines. This abrupt transition has created a substanti…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-01 · Marwan Hassani, Tamara Verbeek, Sjoerd van Straten
Research Track A
Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept d…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-02 · Xinlei Yu, Zhangquan Chen, Yongbo He, Tianyu Fu, Cheng Yang, Chengming Xu, Yue Ma, Xiaobin Hu, Zhe Cao, Jie Xu, Guibin Zhang, Jiale Tao, Jiayi Zhang, Siyuan Ma, Kaituo Feng, Haojie Huang, Youxing Li, Ronghao Chen, Huacan Wang, Chenglin Wu, Zikun Su, Xiaogang Xu, Kelu Yao, Kun Wang, Chen Gao, Yue Liao, Ruqi Huang, Tao Jin, Cheng Tan, Jiangning Zhang, Wenqi Ren, Yanwei Fu, Yong Liu, Yu Wang, Xiangyu Yue, Yu-Gang Jiang, Shuicheng Yan
Research Track A · General AI
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-rea…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-03 · Linyu Li, Zhi Jin, Yichi Zhang, Dongming Jin, Yuanpeng He, Haoran Duan, Gadeng Luosang, Nyima Tashi
Research Track A · General AI
Real-world multimodal knowledge graphs (MMKGs) are dynamic, with new entities, relations, and multimodal knowledge emerging over time. Existing continual knowledge graph reasoning (CKGR) methods focus on structural triples and cannot fully exploit multimodal signals from new entities. Existing multimodal knowledge grap…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-13 · Wei Li, Hangjie Yuan, Zixiang Zhao, Borui Kang, Ziwei Liu, Tao Feng
Research Track A
Continual Learning (CL) aims to train neural networks on a dynamic stream of tasks without forgetting previously learned knowledge. Among optimization-based approaches, C-Flat has emerged as a promising solution due to its plug-and-play nature and its ability to encourage uniformly low-loss regions for both new and old…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-15 · Qianyu Chen, Shujian Yu
Research Track A
Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices providing powerful representations of large-scale neural interactions. However, existing diagnostic models are trained either on a single site or under full multi-site acce…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-16 · Peifeng Zhang, Zice Qiu, Donghua Yu, Shilei Cao, Juepeng Zheng, Yutong Lu, Haohuan Fu
Research Track A · General AI
In continual visual question answering (VQA), existing Continual Learning (CL) methods are mostly built for symmetric, unimodal architectures. However, modern Vision-Language Models (VLMs) violate this assumption, as their trainable components are inherently asymmetric. This structural mismatch renders VLMs highly pron…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-16 · Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi, Tsui-Wei Weng
Research Track A · General AI
Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especially pronounced in class incremental learning (CIL), which is the most challenging setting in continual learning. Existing methods to address …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-18 · Dongkyu Cho, Xiyue Li, Samrachana Adhikari, Rumi Chunara
Research Track A · General AI
Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require interpretability. In practice, models that adapt well (e.g., deep networks) are often opaque, while models that are interpretable (e.g., decision trees) are brittle un…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-22 · Beining Wu, Jun Huang
Research Track A
Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current approaches face several key challenges: 1) they use uniform protection strategies that do not account for the varying sensitivities to forgettin…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-22 · Zeyu Shen, Peter Henderson
Research Track A · General AI
Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows available GPU memory, this churn can render optimizations like offloading and pre-fetching ineffective. We make the case that the options framework in reinforcement learning …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-23 · Paul-Tiberiu Iordache, Elena Burceanu
Research Track A · General AI
Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime, defined by the trainable …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-27 · Sivajeet Chand, Kevin Nguyen, Peter Kuntz, Alexander Pretschner
Research Track A · General AI
Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change generation spanning multiple files and folder structures from a single natural-language (NL) instruction.…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-04-29 · Aditya A. Ramesh, Alex Lewandowski, Jürgen Schmidhuber
Research Track A · General AI
Continual learning agents with finite capacity must balance acquiring new knowledge with retaining the old. This requires controlled forgetting of knowledge that is no longer needed, freeing up capacity to learn. Weight decay, viewed as a mechanism for forgetting, can serve this role by gradually discarding information…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-05-02 · Maniru Ibrahim
Research Track A
Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and physical equilibrium constraints. We investigate sequential learning in differentiable resistor networks governed by Kirchhoff's laws. Although individual input--output map…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-05-12 · Phu-Quy Nguyen-Lam, Phu-Hoa Pham, Dao Sy Duy Minh, Chi-Nguyen Tran, Huynh Trung Kiet, Long Tran-Thanh
Research Track A · General AI
Object-centric representations promise a key property for few-shot learning: Rather than treating a scene as a single unit, a model can decompose it into individual object-level parts that can be matched and compared across different concepts. In practice, this potential is rarely realized. Continual learners either co…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-04 · Ayushman Trivedi, Bhavika Melwani
Research Track A
Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning. In this work, we investigate an alternative perspective: that forgetting may arise not from complete destruction of task representations but from a loss of accessibility to preserved …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-04 · Hongye Xu, Bartosz Krawczyk
Research Track A · General AI
Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data. Historically, prototype rehearsal, which samples around stored class prototypes and mixes them with current-task data, has been a popular strategy to reduce catastrophic forgetting. However, recent drift-com…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-04 · Hongye Xu, Bartosz Krawczyk
Research Track A · General AI
Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge. In exemplar-free class-incremental learning (EFCIL), this challenge is amplified because past data cannot be stored, making representation drift for old classes particularly harmful. Prototype-based EFCIL is attractive for its…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-06 · Daoqing Wang, Yuchen Xiao, Weixuan Huang, Zhilong Zhang, Shenghua Wan, Meng Li, Lei Yuan, Yang Yu
Research Track A · General AI
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Existing methods for multi-quadruped manipulation typically focus on predefined or closed task families, often relying on multi-agent reinforcem…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-11 · Minlin Zeng, Zhipeng Zhou, Yang Qiu, Martin J. McKeown, Zhiqi Shen
Research Track A · General AI
Gait-based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously. New sensors may arrive through device upgrades, protocol changes, or multi-center deployment, while historical patient data are often unavailable because of privacy …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-11 · Ayushman Trivedi, Bhavika Melwani
Research Track A · General AI
Catastrophic forgetting is often viewed as the destruction of previously learned knowledge during sequential learning. Building on the Accessibility Collapse framework, we investigate the geometric structure of recoverability in continual learning. Using Split CIFAR-100 and a sequentially trained ResNet-18, we analyze …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-06-12 · Salimeh Sekeh, Mary Wisell
Research Track A · General AI
Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acq…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-08-02 · Fei Li, Yue Yu, Yuran Wang, Xinghan Li, Jingjing Chen, Yu-Gang Jiang
Research Track A · General AI
AI-generated video (AIGV) detection aims to distinguish real videos from AI-generated ones. In practice, detectors trained on existing data often fail to generalize to newly emerging generative models, making this task challenging. Therefore, continual learning (CL) is essential for improving the adaptability. However,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 14.0
2026-08-12 · Sungwoo Goo, Hwi-yeol Yun, Sangkeun Jung
Research Track A · General AI
Copying short-term memory (STM) into a slower store can preserve state across a context boundary, but persistence alone does not ensure that the retained state influences subsequent memory access. We test this distinction in a Phasor Memory Network (PMNet) using Consolidator, a shared slot-local operator that transform…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.8
2026-06-30 · Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim" Fan, Guanzhi Wang
Research Track A · General AI
Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomousl…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.8
2026-08-03 · Paimon Goulart, Liang Wu, Kelly Wan, Evangelos E. Papalexakis, Liangjie Hong
Research Track A · General AI
As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retrieval methods treat each skill as one flat document by concatenating fields such as the name, description, and body. However, skills are naturally struct…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.8
2026-08-13 · Zixuan Lan, Yanhong Li, Jiawei Zhou
Research Track A · General AI
Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting informative sl…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.5
2026-03-31 · Xiaoyan Zhang, Jiangpeng He
Research Track A · General AI
Visual food recognition in real-world dietary logging scenarios naturally exhibits severe data imbalance, where a small number of food categories appear frequently while many others occur rarely, resulting in long-tailed class distributions. In practice, food recognition systems often operate in a continual learning se…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.5
2026-07-20 · Gabor Szucs, Samuel Jacsev, Marcell Nemeth, Davide Dalle Pezze, Gian Antonio Susto
Research Track A · General AI
Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must cope with distribution shifts over time, a challenge commonly addressed through Continual Learning (CL) techniques. However, existing CL methods face a critical limitation: real-…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.5
2026-08-02 · Mohammad Amanour Rahman
Research Track A · General AI
Continual learning in clinical imaging faces a dual challenge: a model must assimilate knowledge from new anatomical domains while retaining representations learned from prior tasks, a problem known as catastrophic forgetting. Existing mitigation strategies, including regularization and knowledge distillation, treat al…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.4
2026-08-05 · Xiaorong Zeng, Weiqiang Chen, Peng Shi, Liang Su, Zirui Wang, Xuewu Ji, Shuiwen Shen
Research Track A
Biological visual systems can perceive depth from monocular vision flow, continuously integrating temporal visual cues while maintaining a balance between stability and plasticity in dynamic environments. In contrast, artificial perception models deployed on resource-constrained edge devices are typically trained in a …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-03-27 · Shanglin Wu, Yuyang Luo, Yueqing Liang, Kaiwen Shi, Yanfang Ye, Ali Payani, Kai Shu
Research Track A · General AI
Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work has studied these dimensions separately, their interaction under realistic cost constraints remains unclear. In this p…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-04-04 · Ying Yao
Research Track A · General AI
Unsustainable land-use practices in ecologically sensitive regions threaten biodiversity, water resources, and the livelihoods of millions. This paper presents a deep reinforcement learning (RL) framework for optimizing land-use allocation in the Lake Malawi Basin to maximize total ecosystem service value (ESV). Drawin…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-04-08 · Bingxuan Li, Simo Du, Yue Guo
Research Track A · General AI
Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experie…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-04-09 · Shiwan Zhao, Zhihu Wang, Xuyang Zhao, Jiaming Zhou, Caiyue Xu, Chenfei Liu, Liting Zhang, Yuhang Jia, Yanzhe Zhang, Hualong Yu, Zichen Xu, Qicheng Li, Yong Qin
Research Track A · General AI
Post-training has become central to turning pretrained large language models (LLMs) into aligned and deployable systems. Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines. Yet th…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-04-23 · Buqiang Xu, Yijun Chen, Jizhan Fang, Ruobin Zhong, Yunzhi Yao, Yuqi Zhu, Lun Du, Shumin Deng
Research Track A · General AI
Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answering. Current approaches face a fundamental trade-off: flat memory is efficient but fails to model relational structure, while graph-based m…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-04-28 · Xueying Zeng, Youquan Xian, Sihao Liu, Xudong Mou, Yanze Li, Lei Cui, Bo Li
Research Track A · General AI
With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models (LLMs) demonstrate remarkable sem…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-05-12 · Zhong Li, Zihan Guo, Xiaohan Lu, Juntao Wang, Jie Song, Chao Shen, Jiageng Wu, Mingyang Sun
Research Track A · General AI
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may agree numerically while still changing the underlying optimization sema…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.3
2026-05-27 · Julia Hindel, Simon Bultmann, Houman Masnavi, Daniele Cattaneo, Abhinav Valada
Research Track A · General AI
Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation behavior toward safe and efficient trajectories. Existing approaches either rely on handcrafted proprioceptive traversability scores, limiting robot-agnosticism, or clus…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.2
2026-08-07 · MY Pitsane, Hope Mogale
Research Track A · General AI
Agentic coding faces growing problems of affordability and wasted tokens. We introduce Blast Radius, a predictive memory management layer that estimates an incoming prompt's reach through coupled context and code channels. NECROPHORESIS enables reversible eviction by archiving dead context verbatim, while Recurring Dea…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-04-01 · Xiao Zhang, Juntao Lyu, Tianyu Hu, Qianchuan Zhao, Huimin Ma
Research Track A · General AI
Large Language Models (LLMs) generalize across tasks via reusable representations and flexible reasoning, yet remain brittle in real deployment under evolving tasks and continual distribution shift. A common approach is Test-Time Adaptation (TTA), existing ones of which updates models with hand-designed unsupervised ob…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-04-15 · Karthik Singaravadivelan, Anant Gupta, Zekun Wang, Christopher MacLellan, Christopher J. MacLellan
Research Track A
Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but require extensive tuning and struggle with lifelong learning due to catastrophic forgetting and fixed capacity, while classical probabilistic models lack flexibility and a…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-04-17 · Guransh Singh
Research Track A
Adapting pre-trained vision-language models (VLMs) for robotic control requires injecting high-magnitude continuous gradients from a flow-matching action expert into a backbone trained exclusively with cross-entropy. This cross-modal gradient asymmetry - the spectral dimensionality mismatch between low-rank MSE regress…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-04-23 · Yi-Ling Liu, Melvin Laux, Mariela De Lucas Alvarez, Frank Kirchner, Rebecca Adam
Research Track A · General AI
Autonomous underwater vehicles are required to perform multiple tasks adaptively and in an explainable manner under dynamic, uncertain conditions and limited sensing, challenges that classical controllers struggle to address. This demands robust, generalizable, and inherently interpretable control policies for reliable…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-06-22 · Yuting Li, Weihang Fang, Haoyuan Gao, Linghe Kong, Yexin Li, Lichao Sun, Weiran Huang
Research Track A · General AI
The rapid deployment of Vision-Language Models (VLMs) in dynamic environments necessitates the ability to learn continuously without forgetting. However, traditional continual learning (CL) settings often rely on white-box paradigms, which is increasingly invalidated by the shift toward cloud-hosted models. In this pap…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-06-29 · Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer, Mukesh Prasad, Weiping Ding, Yew-Soon Ong
Research Track A · General AI
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity. This paper puts forward a relatively uncharted problem, namely, few-shot domain incremental learning (FSDIL), taking into account the probl…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-07-06 · Duc-Thanh Le, Doanh C. Bui, Maï K. Nguyen, Khang Nguyen
Research Track A · General AI
Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-rela…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-07-06 · Vu Minh Tran, Doanh C. Bui, Maï K. Nguyen, Khang Nguyen
Research Track A · General AI
Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs. In this st…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-07-27 · Tianyi Men, Zhuoran Jin, Kang Liu, Jun Zhao
Research Track A · General AI
Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired, shaped, and integrated. To address this challenge, we i…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-08-03 · Anusha Madan Gopal, Aras Pirbadian, Kristofor D. Carlson, M Anthony Lewis, Jonathan Tapson
Research Track A · General AI
Retrieval-augmented generation (RAG) imposes a prefill cost proportional to retrieved context length, and -- with Transformer backbones -- a KV-cache that grows with each generated token. State-Space Models (SSMs) avoid the second cost by construction; we eliminate the first, collapsing prefill from $O(L_{context})$ to…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-08-13 · Jiaqian Li
Research Track A · General AI
Implicit multimodal in-context learning compresses demonstrations into internal interventions, ranging from static task vectors to query-conditioned transformations and attention routing. Despite their common goal, these methods differ substantially in how the intervention depends on the query and where it modifies the…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 13.0
2026-08-17 · Zhiming Xu, Huiyu Yi, Zhen-Hao Xie, Baile Xu, Furao Shen, Jian Zhao, Suorong Yang
Research Track A
Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks. However, adapting well to each task does not ensure reliable inference over all learned tasks. Since task boundaries are often artificial and semantically …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.9
2026-08-05 · Xujia Chen, Xinyue Hu, Letian Chen, Yi Liu, Wenhui Fan
Research Track A · General AI
Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datasets. Parameterized PINNs (ParamPINNs) further take physical parameters as inputs, allowing a single model to represent a family of PDE soluti…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.8
2026-06-30 · Prakhar Dixit, Tim Oates
Research Track A · General AI
We propose Intelligent Schema Memory (ISM), a self-evolving memory-augmented system that improves mathematical reasoning for a frozen LLM under continual learning with hard episodic resets. ISM maintains a compact, self-refined bank of strategy schemas learned from both successful and failed episodes, with symbolic too…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.8
2026-07-01 · Jiatong Li, Samuel Yeh, Sharon Li
Research Track A · General AI
Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window. Despite their scalability, these agents exhibit a well-documented reliability problem: end-to-end performance degrades systematically as context length grows. We diagnose this failure by …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.8
2026-08-11 · Yueke Zhang, Zihan Fang, Kevin Leach, Yu Huang
Research Track A · General AI
Code large language models (LLMs) can generate syntactically plausible programs that nevertheless violate hidden semantic constraints. Existing execution-feedback training methods identify whether a completed program fails, but provide limited supervision about how a correct solution should be organized. We introduce G…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-03-22 · Shenghan Chen, Yiming Liu, Yanzhen Wang, Yujia Wang, Xiankai Lu
Research Track A · General AI
Balancing performance trade-off on long-tail (LT) data distributions remains a long-standing challenge. In this paper, we posit that this dilemma stems from a phenomenon called "tail performance degradation" (the model tends to severely overfit on head classes while quickly forgetting tail classes) and pose a solution …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 12.5
2026-04-01 · Xingxing Weng, Ruifeng Ni, Chao Pang, XiangYu Hao, Yishan Wang, Xiaokang Zhang, Wei Xu, Gui-Song Xia
Research Track A · General AI
Current remote sensing vision-language models (RS VLMs) demonstrate impressive performance in image interpretation but rely on static training data, limiting their ability to accommodate continuously emerging sensing modalities and downstream tasks. This exposes a fundamental challenge: enabling RS VLMs to continually …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-04-01 · Jie Mei, Li-Leng Peng, Keith Fuller, Jenq-Neng Hwang
Research Track A
For continual learning, text-prompt-based methods leverage text encoders and learnable prompts to encode semantic features for sequentially arrived classes over time. A common challenge encountered by existing works is how to learn unique text prompts, which implicitly carry semantic information of new classes, so that…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-04-17 · Fazeng Li, Gan Sun, Chenxi Liu, Yao He, Wei Cong, Yang Cong
Research Track A
Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learning-based calibration models suffer from catastrophic forgetting when adapting into unseen data amongst open-world scene changes, while simple rehearsal-based continual …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-04-17 · Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee, JiHyun Kim, Soojin Jang, YoungBin Kim
Research Track A · General AI
Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting real-world applicability where data arises from heterogeneous disciplines with imbalanced sample av…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-05-04 · Thanasis Pantsios, Dimitrios Karageorgiou, Christos Koutlis, George Karantaidis, Olga Papadopoulou, Symeon Papadopoulos
Research Track A · General AI
The rapid advancement of generative Artificial Intelligence (AI) has introduced significant challenges for reliable AI-generated image detection. Existing detectors often suffer from performance degradation under distribution shifts and when encountering newly emerging generative models. In this work, we propose a data…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-06-10 · Megha Manoj, Sue Ann Campbell
Research Track A
Neural assemblies, transiently coordinated groups of neurons, observed in the hippocampus are thought to underlie the formation of episodic memories. Acetylcholine (ACh), a neuromodulator, that is received by the hippocampus, plays a critical role in memory and learning. A well supported hypothesis suggests that high l…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-07-15 · Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Research Track A · General AI
Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimization is applied recursively as new fai…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.5
2026-08-13 · Fanfei Li, Jana Zeller, Manuel Prada-Corral, Thaddäus Wiedemer, Prasanna Mayilvahanan, Ryan Cotterell, Wieland Brendel
Research Track A · General AI
Modern language models are trained on heterogeneous web-scale text corpora. Consequently, studying knowledge and skill acquisition is difficult, as prior exposure to related content is hard to characterize. To address this challenge, we introduce LITTLECURRICULUM, a curated 88B-token pretraining corpus tailored to U.S.…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.4
2026-08-05 · Seyed Roozbeh Razavi Rohani, Khashayar Khajavi, Wesley Chung, Mandana Samiei, Mo Chen
Research Track A · General AI
Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their long-term adaptability. Drawing high-level inspiration from global neuromodulatory mechanisms in the brain, we introduce Neuromodulation and…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.4
2026-08-06 · Xi Chen, Xu Chen, Xiangyang Jia, Wei Wang, Xu Zhang, Zhenyuan Sun
Research Track A · General AI
With the rapid growth of Earth observation technologies, remote sensing archives are rapidly expanding, making remote sensing image-text retrieval (RS-ITR) increasingly important. However, continual RS-ITR remains challenging because scale variation and distribution shifts in RS aggravate cross-modal alignment space di…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-04-01 · Yutao Yang, Junsong Li, Qianjun Pan, Jie Zhou, Kai Chen, Qin Chen, Jingyuan Zhao, Ningning Zhou, Xin Li, Liang He
Research Track A · General AI
Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Li…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-04-09 · Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang
Research Track A · General AI
Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions. However, retrieval-based external memory systems incur low online overhead but suffer from unstable accuracy due to limited query construc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-04-13 · Buseong Kim, Heejun Gwon
Research Track A · General AI
In large language models performing long-form reasoning, the KV cache grows rapidly with decode length, creating bottlenecks in memory and inference stability. Existing reasoning-oriented KV compression has mostly followed an eviction-centered view: estimate token importance more accurately, then discard lower-ranked e…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-05-20 · Chongrui Ye, Yuxiang Liu, Yu Wang, Haofei Yu, Yining Zhao, Ge Liu, Julian McAuley, Jiaxuan You
Research Track A · Research Track B · General AI
Language agents increasingly operate over streams of related tasks, yet existing memory systems struggle to convert accumulated experience into reusable knowledge. Retrieval-augmented and structured memory methods record per-session observations effectively, but often couple acquisition and consolidation into a single …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-05-29 · Navin Sriram Ravie, Andrew Jong, Krrish Jain, John Liu, Omar Alama, Bijo Sebastian, Sebastian Scherer
Research Track A · General AI
In robotics, dangers and adversity modes are often embodiment-specific and relative to each agent. A frontier of autonomous mobile robotics is to enable agents to operate effectively in the wild in unseen unstructured environments. A significant challenge in unseen unstructured environments is that it may not be possib…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-05-29 · Rosario Forte, Giuseppe Lando, Antonino Furnari
Research Track A · General AI
Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchmarks provide limited tools for diagnosing what models remember and for how long. We introduce \egostream, a diagnostic benchmark for streaming episodic memory evaluation…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-06-03 · Jingwen Chen, Wenkai Yang, Shengda Fan, Wenbo Nie, Chenxing Sun, Shaodong Zheng, Yangen Hu, Lu Pan, Ke Zeng, Yankai Lin
Research Track A · General AI
Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration exper…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-06-08 · Mingqi Yuan, Xiaoquan Sun, Shihao Luo, Jiayu Chen
Research Track A · General AI
Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers. Existing online TFCL paradigms primarily rely on parameter-efficient prompt tuning or …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.3
2026-07-22 · Manoosh Samiei, Doina Precup, Paul Masset
Research Track A · General AI
Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences. In reinforcement learning (RL), this trade-off is typically controlled through a fixed discount factor, which imposes a single exponentially discounted temporal horizon. However, biological agents ex…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.2
2026-08-05 · Indraneil Paul, Falko Helm, Goran Glavaš, Iryna Gurevych
Research Track A · General AI
Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon agentic workflows. Existing long-context corpora, however, are dominated by books, academic articles, and code repositories, which are finite resources and often scarce…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.2
2026-08-05 · Yuezhang Peng, Yuxin Liu, Changfeng Gao, Zhifu Gao, Xiangang Li, Xie Chen
Research Track A · General AI
Spoken Language Understanding (SLU) is the core component of task-oriented dialogue systems and a pivotal link in achieving seamless human-agent interaction. While traditional SLU can effectively extract user semantics for closed-set tasks after in-domain supervised fine-tuning, it faces significant challenges in lever…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.2
2026-08-06 · Chen Yang, Jiashuo Tian, Ziqi Wang, Xinyin Liu, Meiru Ye, Junjie Chen
Research Track A · General AI
Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evolution as a sequence of local updates, overlooking relationships among skills and often producing overfitted skill updates that fail to generali…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-03-30 · Shivnath Tathe
Research Track A
Fixed representational capacity is a fundamental constraint in continual learning: practitioners must guess an appropriate model width before training, without knowing how many distinct concepts the data contains. We propose LACE (Loss-Adaptive Capacity Expansion), a simple online mechanism that expands a model's repre…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 12.0
2026-04-01 · Mohammad R. Abu Ayyash
Research Track A · General AI
We present Brainstacks, a modular architecture for continual multi-domain fine-tuning of large language models that packages domain expertise as frozen adapter stacks composing additively on a shared frozen base at inference. Five interlocking components: (1) MoE-LoRA with Shazeer-style noisy top-2 routing across all s…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-04-14 · Amar Gahir, Varshil Patel, Shreyank N Gowda
Research Track A · General AI
Deep neural networks are typically trained by uniformly sampling large datasets across epochs, despite evidence that not all samples contribute equally throughout learning. Recent work shows that progressively reducing the amount of training data can improve efficiency and generalization, but existing methods rely on f…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-04-16 · Guy Kaplan, Zorik Gekhman, Zhen Zhu, Lotem Rozner, Yuval Reif, Swabha Swayamdipta, Derek Hoiem, Roy Schwartz
Research Track A · General AI
Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning (SFT), which can increase hallucinations w.r.t. knowledge acquired during pre-training. In this work, we explore whether SFT-induced halluci…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-05-21 · Anuj Apte, Pranav Deshpande, Niraj Kumar, Shouvanik Chakrabarti, Junhyung Lyle Kim
Research Track A
Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading to strong path dependence and costly re-tuning as data availability changes. Schedule-Free (SF) methods address this by removing explicit schedules, yet SF-AdamW, the current state-of-the-art anytime optimizer, consisten…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-06-28 · Alex Kwon
Research Track A · General AI
LLM agents carry conclusions across steps and sessions in compressed memory, and memory products (e.g., mem0, LangMem) rewrite conversation into stored "facts" that later steps trust. We show this rewriting manufactures confidence: across our constructed agent settings, a casual, hedged remark becomes a confident, date…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-07-24 · Hao Wang, Kun Yuan, Wenlin Zhong, Minglei Zhang, Han Xiao, Ming Sun, Honggang Qi
Research Track A · General AI
Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through on-policy distillation (OPD). However, full-vocabulary OPD typically assumes a shared tokenizer, while existing cross-tokenizer methods may discard teacher probability ma…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 12.0
2026-07-24 · Valentin Tablan, Scott Taylor, Kristoffer Bernhem
Research Track A · General AI
AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the form of outcome ver…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.8
2026-06-29 · Cheng Gong, Haoyang Wang, Chao Lu, Zirui Li, Jianwei Gong
Research Track A · General AI
Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations. However, most learning-based policies are trained or fine-tuned on expert demonstrations and then rely largely on generalization to handle challenging closed-loop scenar…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-03-30 · Alkis Sygkounas, Rishi Hazra, Andreas Persson, Pedro Zuidberg Dos Martires, Amy Loutfi
Research Track A · General AI
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and generalization beyond the training distribution. We address this with COvolve, a co-evolutionary framework that leverages large language models (…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-05-07 · Yuxing Liu, Jianyu Wang, Tong Zhang
Research Track A · General AI
Optimizers play an important role in both pretraining and finetuning stages when training large language models (LLMs). In this paper, we present an observation that full finetuning with the same optimizer as in pretraining achieves a better learning-forgetting tradeoff, i.e., forgetting less while achieving the same o…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 11.5
2026-06-04 · Hanxu Hu, Zdeněk Šnajdr, Pinzhen Chen, Jannis Vamvas, Rico Sennrich
Research Track A · General AI
Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-06-09 · Can Lin, Tao Feng, Hangjie Yuan, Dan Zhang, Yifan Zhu, Zhonghong Ou
Research Track A · Research Track B · General AI
Graphical User Interfaces (GUIs) serve as the dominant medium for human-computer interaction, yet building GUI agents that generalize across the vast diversity of real-world interface environments, with the same flexibility and robustness that humans naturally exhibit, remains unsolved. Notably, GUI data are inherently…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-06-30 · Julien Lefebvre, Stefan Duffner, Mathieu Lefort
Research Track A · General AI
Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints. Existing methods rely either on replay buffers that exploit latent space structure, or on regularization alone. We present CLIMB …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-07-17 · Hyunin Lee, Jinglue Xu, Jeffrey Seely, Donghyun Lee, Matei Zaharia, Yujin Tang
Research Track A · General AI
Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of traces used for future …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-07-24 · Xiangjun Shi, Chong Mu, Jinchuan Zhang, Lizong Zhang, Yuefeng He, Shang Liu
Research Track A · General AI
Continual temporal knowledge graph (TKG) reasoning aims to continuously incorporate newly emerging facts while preserving previously acquired knowledge. Replay-based continual learning has achieved promising performance by revisiting historical representations. However, existing methods primarily focus on what to repla…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-07-27 · Luc McCutcheon, Evangelos Chatzaroulas, Saber Fallah
Research Track A · General AI
Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervised and reinforcement learning. Recently, neuron resets have been used to maintain gradient flow and restore plasticity. However, full unit re…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 11.5
2026-07-28 · Jinsen Su, Yongdong Luo, Yuexiao Ma, Yibo Hu, Meiguang Jin, Xiaowu Zheng
Research Track A · General AI
Existing token compression methods for omnimodal large language models typically rely on one modality to determine what to retain in the other. We show that this assumption often breaks down: for the same query, audio and video relevance often peaks at different moments. This cross-modal salience mismatch makes unidire…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-08-03 · Gusseppe Bravo-Rocca, Jordi Guitart, Ajay Dholakia, David Ellison, Puneet Jain
Research Track A · General AI
Data drift poses significant challenges for machine learning systems in production, requiring continuous model updates to maintain performance. We present KC-Agent, a dual-process cognitive architecture for automated ML model improvement that combines fast pattern recognition (System 1) with deliberate incremental upda…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.5
2026-08-14 · Alexy Skoutnev, Kirill Acharya, Gaston Longhitano, Madeleine Udell, Kevin Ellis, Iddo Drori
Research Track A · General AI
We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual learning tasks, such as ARC-AGI-3 games. Traditional approaches hand-engineer such models, one custom design per task. Each game hides its rules and goal, and our system…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 11.4
2026-08-05 · Xuying Ning, Dongqi Fu, Tianxin Wei, Hanqing Zeng, Yuanchen Bei, Bingxuan Li, Zihao Li, Qifan Wang, Xiang Shen, Yifan Wu, Jiayi Liu, Hong Li, Yinglong Xia, Xiangjun Fan, Hanghang Tong, Jingrui He
Research Track A · General AI
Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions. However, effective harness use raises two coupled challenges: state formation from noisy interaction traces and runtime control over externa…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.4
2026-08-07 · Mingxuan Zheng, Yujin Zhou, Chuxue Cao, Boqin Yin, Yuyao Zhang, Jiapeng Sun, Shuaishuai Gong, Sirui Han, Yike Guo
Research Track A · General AI
LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills. These skills are lightweight, reusable textual artifacts that are loaded into the agent's context without weight updates. Recent methods refine skills through iterative task execution, failure diagnosis, and trajectory-guid…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-03-25 · Zhuoran Li, Zhiyang Li, Kaijun Zhou, Jinyu Gu
Research Track A · General AI
Despite the promise of Vision-Language-Action (VLA) models as generalist robotic controllers, their robustness against perceptual noise and environmental variations in out-of-distribution (OOD) tasks remains fundamentally limited by the absence of long-term memory, causal failure attribution, and dynamic intervention c…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-04-04 · Hessen Bougueffa Eutamene, Abdellah Zakaria Sellam, Abdelmalik Taleb-Ahmed, Abdenour Hadid
Research Track A · General AI
Detecting AI-generated images remains a significant challenge because detectors trained on specific generators often fail to generalize to unseen models; however, while pixel-level artifacts vary across models, frequency-domain signatures exhibit greater consistency, providing a promising foundation for cross-generator…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-04-06 · Mingzhe Du, Luu Anh Tuan, Dong Huang, See-kiong Ng
Research Track A · General AI
The accelerating pace of scientific publishing makes it increasingly difficult for researchers to stay current. We present Paper Espresso, an open-source platform that automatically discovers, summarizes, and analyzes trending arXiv papers. The system uses large language models (LLMs) to generate structured summaries w…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-04-14 · Anne Lee, Gurudutt Hosangadi
Research Track A · General AI
The rapid advancement of AI has changed the character of HPC usage such as dimensioning, provisioning, and execution. Not only has energy demand been amplified, but existing rudimentary continual learning capabilities limit ability of AI to effectively manage HPCs. This paper reviews emerging directions beyond monolith…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-04-18 · Jinchang Zhu, Jindong Li, Cheng Zhang, Jiahong Liu, Menglin Yang
Research Track A · General AI
Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interactions. Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. This paradigm fails to …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.3
2026-04-19 · Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He
Research Track A · General AI
Large language models (LLMs) have demonstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has shifted toward meta-reasoning as a promising direction for further improvement. However, most existing meta-reasoning methods remain episodic:…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-03-24 · Xinyao Wu, Zhe Xu, Cheng Chen, Jiawei Ma, Yefeng Zheng, Raymond Kai-yu Tong
Research Track A · General AI
Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging disease categories, which is critical for scalable clinical deployment. This problem is particularly challenging due to heterogeneous data and privacy constraints that preven…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 11.0
2026-04-08 · Wonseon Lim, Jaesung Lee, Dae-Won Kim
Research Track A · General AI
Continual learning (CL) on edge devices requires not only high accuracy but also training-time efficiency to support on-device adaptation under strict memory and computational constraints. While prompt-based continual learning (PCL) is parameter-efficient and achieves competitive accuracy, prior work has focused mainly…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-04-15 · Aaron Pache, Mark CW van Rossum
Research Track A · General AI
Synaptic plasticity is metabolically expensive, yet animals continuously update their internal models without exhausting energy reserves. However, when artificial neural networks are trained, the network parameters are typically updated on every sample that is presented, even if the sample was classified correctly. Ins…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-05-12 · Phu-Hoa Pham, Chi-Nguyen Tran, Nguyen Lam Phu Quy, Dao Sy Duy Minh, Huynh Trung Kiet, Long Tran-Thanh
Research Track A · General AI
Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decision boundaries. Despite these, they still fail in online class-incremental learning due to two coupled miscalibrations: (i) their split criterion grows unreliable as th…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 11.0
2026-06-08 · Gianluca Barmina, Annemette Broch Pirchert, Andrea Blasi Núñez, Lukas Galke Poech, Peter Schneider-Kamp
Research Track A · General AI
As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-06-09 · Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan, Rajgopal Kannan, Viktor Prasanna
Research Track A · General AI
Federated continual learning (FCL) must learn from distributed task streams under limited resources, such as communication, computation, memory, and label availability. Existing FCL methods often rely on repeated local optimization, replay, and full supervision. Analytic alternatives avoid iterative training and replay…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-06-12 · Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar, Salimeh Yasaei Sekeh
Research Track A · General AI
Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible. We propose GRASP (Gradi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-06-22 · Subarnaduti Paul, Yohan Jung, Mohammad Emtiyaz Khan, Siddharth Swaroop, Thomas Möllenhoff, Martin Mundt
Research Track A · General AI
Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural mechanism is fast and slow adaptation to balance stability and plasticity. This mechanism has deep roots in neuroscience and biology, but the…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-06-23 · Animesh Animesh, Satheesh K Perepu, Kaushik Dey
Research Track A · General AI
In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task. In this work, we propose GCT-MARL, a transfer learning framework that builds on the multi-view graph contrastive backbone of MAIL and au…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-06-29 · Matan Schliserman, Gon Buzaglo, Itay Evron, Daniel Soudry
Research Track A
We characterize weakly regularized continual classification in homogeneous models as sequential projections onto task margin sets. This result generalizes prior analyses restricted to either stationary (single-task) deep models or continual linear models. We show that global convergence generally fails, even for simple…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-07-08 · Luke Baines, Anton Gonzalvez Hawthorne, Mariia Koroliuk, Irakli Shalibashvili, Clément Dumas, Konstantinos Voudouris, David Demitri Africa
Research Track A · General AI
Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them. Our central insight is to treat personas as positions in a space of behavioural traits, using the OCEAN framework to describe mod…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 11.0
2026-07-13 · Charles O'Neill
Research Track A
Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights. Whether weight writes can support accumulation remains undecided. We follow invented facts written into Qwen3 models from creation through sequences of twenty to one hundred later wri…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 11.0
2026-08-03 · Junhao Shen, Zhanqiu Zhang, Yiwen Guo, Hong Cheng
Research Track A · General AI
Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill generation across heterogeneous sources. However, learning-based skill generation …
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 11.0
2026-08-12 · Tianci Liu, Zihan Dong, Tianchun Li, Yi-Chung Chen, Qiming Cao, Xingchen Wang, Shiyang Wang, Zichen Miao, Linjun Zhang, Haoyu Wang, Jing Gao
Research Track A · General AI
Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world. This motivates knowledge editing (KE), which updates specific knowledge in an LLM without changing unrelated others. Re…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 11.0
2026-08-13 · Alexander Weiss, Albert Schulz, Michael Wittner
Research Track A
Structural coverage is widely used as evidence that testing is complete, yet in embedded projects it is predominantly collected at unit level, simply because that is where instrumentation and observability are inexpensive. This produces a mismatch. The most representative completeness signal would come from integration…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.9
2026-08-06 · Songpan Gao, Yajie Zhang, Guanxing Chen, Jiayu Qian, Zhenzhen Liu, Shijun Li, Xiaowei Zhu, Yao Hu, Kay Chen Tan, Yu-An Huang, Shiqi Wang, Zhi-An Huang
Research Track A · General AI
Deep learning models applied to medical image analysis suffer from severe catastrophic forgetting when continually adapting to new clinical tasks in dynamic environments. Mainstream incremental learning methods typically mitigate this by rehearsing raw historical images. However, this pixel-level rehearsal incurs signi…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.8
2026-06-24 · Yuxing Cheng, Yuan Wu, Yi Chang
Research Track A · General AI
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors an…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-03-25 · Adidev Jhunjhunwala, Judah Goldfeder, Hod Lipson
Research Track A
A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a "self," and if so how to differentiate the "self" from other cognitive structures. We propose that the "self" can be isolated by seeking the invariant portion of cognitive process th…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.5
2026-03-30 · Fiorenzo Stoppa, Stephen J. Smartt
Research Track A
We present SNID-SAGE (SuperNova IDentification-Spectral Analysis and Guided Exploration), a framework for supernova spectral classification with both a fully interactive graphical interface and a scriptable command-line pipeline for large-scale processing. The pipeline combines deterministic spectral preprocessing, FFT…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-16 · Jinchang Liu, Qingshan Zhou, Hongkan Chen, Yi Bu
Research Track A
Science advances not only by accumulating discovered patterns but by changing how new problems and solutions are expressed. While structural indicators track scholarly attention, they offer only an indirect proxy for the reorganization of meaning. We propose a semantic geometry based on the R-P-C (references, focal pub…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-18 · Jiaxin Zhang, Xiangyu Peng, Qinglin Chen, Qinyuan Ye, Caiming Xiong, Chien-Sheng Wu
Research Track A
On-policy distillation (OPD) is an increasingly important paradigm for post-training language models. However, we identify a pervasive Scaling Law of Miscalibration: while OPD effectively improves task accuracy, it systematically traps models in severe overconfidence. We trace this failure to an information mismatch: t…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-20 · Hyeonseo Jang, Hyuk Kwon, Kibok Lee
Research Track A
We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge using parameter-efficient methods, such as prefix-tuning or adapters, which facilitate model adaptation to downstream tasks by incorporating task-specific information int…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-22 · Shanshan Zhong, Yi Lu, Jingjie Ning, Yibing Wan, Lihan Feng, Yuyi Ao, Leonardo F. R. Ribeiro, Markus Dreyer, Sean Ammirati, Chenyan Xiong
Research Track A · General AI
Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, compris…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-29 · Karthik Charan Raghunathan, Christian Metzner, Laura Kriener, Melika Payvand
Research Track A · General AI
In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this dilemma has an architectural root. A finite network has limited representational and plastic resources, yet the required capacity depends on p…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-04-30 · Kathrin Korte, Joachim Winter Pedersen, Eleni Nisioti, Sebastian Risi
Research Track A
To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowledge, and stability. This stability-plasticity dilemma affects how representations can be reused across tasks: shared structure enables transfer when tasks are similar but…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-05-02 · Xiaoyu Yang, En Yu, Jie Lu
Research Track A
In the pursuit of autonomous learning systems, the foundational assumption of stationarity, the premise that data distributions and model behaviors remain constant, is fundamentally untenable. Historically, the research community has addressed non-stationary environments almost exclusively under the scope of concept dr…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 10.5
2026-05-07 · Xin Gao, Ruiyi Zhang, Meixi Du, Peijia Qin, Pengtao Xie
Research Track A · General AI
Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensiv…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-05-07 · Hao Ye, Jisheng Dang, Junfeng Fang, Bimei Wang, Yizhou Zhang, Ning Lv, Wencan Zhang, Hong Peng, Bin Hu, Tat-Seng Chua
Research Track A · General AI
Recent extensive research has demonstrated that the enhanced reasoning capabilities acquired by models through Reinforcement Learning with Verifiable Rewards (RLVR) are primarily concentrated within the rank-1 components. Predicated on this observation, we employed Periodic Rank-1 Substitution and identified a counteri…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-05-21 · Pilchen Hippolyte, Fabre Romain, Signe Talla Franck, Perez Patrick, Grave Edouard
Research Track A · General AI
Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, focusing specifically…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-06-08 · Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-Canedo
Research Track A
We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedded and resource-constrained environments. While most Class-Incremental Learning (CIL) methods rely on powerful hardware and long retraining cycles, real-world systems, su…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-06-08 · Andries Rosseau, Robert Müller, Ann Nowé
Research Track A · General AI
Continual training of deep neural networks under non-stationarity often leads to a progressive loss of plasticity, eventually limiting further learning. We relate plasticity to the empirical Neural Tangent Kernel, and identify dynamical isometry (the condition that layer-wise Jacobian singular values remain close to on…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-06-16 · Kathrin Korte, Christian Medeiros Adriano, Joachim Winther Pedersen, Eleni Nisioti, Sebastian Risi
Research Track A
Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tasks share structure and risk interference. We study how modular architecture, task similarity, and representational dimensionality jointly sh…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-07-02 · Xue Qin, Simin Luan, Cong Yang, Zhijun Li
Research Track A · General AI
Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity. Memory consolidation is conventionally treated as an agent-changing operation: a model is fine-tuned, a prompt rewritten, a policy distilled, or a reflection appended to the context that governs…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-08-01 · Malavika Suresh, Ikechukwu Nkisi-Orji, Nirmalie Wiratunga
Research Track A
Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) no access to training data from prior tasks (II) no access to task-id at inference time. We introduc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.5
2026-08-11 · Guanlin Liu, Shaobin Ling, Renyuan Liu, Zeying Gong, Junjie Hu
Research Track A · General AI
Zero-shot object-goal navigation (ZSON) in open-vocabulary scenarios is challenging, as it requires a robot to locate an arbitrarily specified object in an unseen environment without task-specific training. Currently, the task still suffers from high latency and limited accuracy due to redundant perception pipelines an…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.5
2026-08-17 · Tadeáš Těhan, Jaromír Kopeček, Elizaveta Iaparova, Eduardo Alarcón, Sneha Samal
Research Track A
Mechanical properties and dynamical mechanical analysis were performed on compact Spark Plasma Sinter samples. It has been observed that the sample with less porosity reflects the behavior of superelasticity response. Other samples show failure during first cycles that may be due to porosity. Compaction of metallic pow…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.5
2026-08-17 · Leopold Bertholet, Chloe Makdad, Stephen Mackes, Daniel Chew, Matthew Robinso
Research Track A
We propose \textit{two-level decorrelated coding} (TLDC), a novel coded modulation scheme for the $D_4$ lattice that combines Voronoi shaping with a two-stage decoding process to achieve lattice shaping and coding gains at low complexity. In TLDC, the decoded values of the first level allow the several random variables…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.3
2026-04-30 · Gyoung S. Na, Chanyoung Park
Research Track A · General AI
Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated substantial capabilities in function approximation, the discovery of explainable and extrapolatable equations remains a fundamental limitation of modern AI, posing a ce…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.3
2026-06-08 · Hao Shi, Weiye Li, Bin Xie, Yulin Wang, Renping Zhou, Tiancai Wang, Xiangyu Zhang, Ping Luo, Gao Huang
Research Track A · General AI
Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests th…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.3
2026-06-17 · Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin, Nikita Kurlaev, Daria Pugacheva, Albina Burlova, Mikhail Kolosov, Denis Shepelev, Andrey Kuznetsov, Elena Tutubalina, Aleksandr I. Panov, Alexey K. Kovalev, Vlad Shakhuro
Research Track A · General AI
Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalizat…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-03-22 · Alfred Shen, Aaron Shen
Research Track A · General AI
Current AI agent frameworks commit early to a single interaction protocol, a fixed tool integration strategy, and static user models, limiting their deployment across diverse interaction paradigms. To address these constraints, we introduce STEM Agent (Self-adapting, Tool-enabled, Extensible, Multi-agent), a modular ar…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.0
2026-04-02 · Kang-Sin Choi
Research Track A · General AI
We propose LSCP, a self-gated post-training framework for autonomous knowledge acquisition: learning only what a model does not already know, verified against what it does know, at a strength proportional to conviction, with no external oracle. When a passage produces anomalously high per-token loss, LSCP flags it, gen…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-04-08 · Jiaming Cheng, Duong Tung Nguyen
Research Track A · General AI
Deploying large language model (LLM) inference at scale requires jointly selecting base models, provisioning heterogeneous GPUs, configuring parallelism, and distributing workloads under tight latency, accuracy, and budget constraints. Exact mixed-integer linear programming (MILP) approaches guarantee optimality but sc…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-04-17 · Ulrich Tan
Research Track A · General AI
We introduce the Tan-HWG framework (Hebbian-Wasserstein-Geometry), a geometric theory of Hebbian plasticity in which memory states are modeled as probability measures evolving through Wasserstein minimizing movements. Hebbian learning rules are formalized as Hebbian energies satisfying a sequential stability condition,…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-05-07 · Bomin Wang, Hangqi Zhou, Yibo Gao, Xiahai Zhuang
Research Track A · General AI
Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve over time. However, continual segmentation still faces three main challenges. First, the scenarios for this task remain insufficiently stan…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-05-11 · Lungchuan Chen
Research Track A · General AI
Memory consolidation, the process by which transient experiences are transformed into stable, structured representations, is a foundational organizing principle in the human brain, yet it remains largely unexplored as a design principle for modern sequence models. In this work, we leverage established neuroscientific t…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-05-28 · Yilun Yao, Jiaming Pan, Elsie Dai, Peizhuang Cong, Yaoming Li, Tong Yang
Research Track A · General AI
Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training compression methods mainly shrink this cost by pruning experts or merging their weights. We formulate post-training MoE compression as exper…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 10.0
2026-07-02 · Rintaro Otsubo, Ryo Fujii, Reina Ishikawa, Taiki Kanaya, Kanta Sawafuji, Hiroki Kajita, Shigeki Sakai, Hideo Saito, Ryo Hachiuma
Research Track A · General AI
Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in specialized fields, where m…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-07-10 · Dan C. Hsu, Luke Lu
Research Track A · General AI
Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is updated from operational experience while the model, tools, and harness remain fixed. Over lon…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 10.0
2026-07-24 · Quyen Tran, Hai Nguyen, Quan Dao, Zhuowei Li, Nam Le, Trung Le, Dimitris Metaxas
Research Track A
Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursive Least Squares (RLS) and have achieved the state-of-the-art results compared to other alternatives. However, they falter significantly in Class-Incremental Learning sce…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 10.0
2026-07-30 · Yang Jiao, Kaixuan Jiao, Kai Yang, Nadjib Aitsaadi, Ilhem Fajjari, Renwei, Li
Research Track A
With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on full data incurs significant computational overhead and storage bottlenecks, rendering coreset selection a critical paradigm. Furthermore, given the privacy-sensitive na…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.8
2026-06-24 · Lea Roxanne Muth, Marian Margraf
Research Track A · General AI
This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge Language Model system (MLS) with Hybrid RetrievalAugmented Generation (HybridRAG). Facing the challenges of the Network and Information Security Directive 2 (NIS2) directive, a shortage of specialists, and…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.8
2026-06-29 · Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal, Yunzhong He
Research Track A · General AI
We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically provide complete requirements upfront and evaluate agents on autonomous implementation. In contrast, SWE-Interact places agents in a realis…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-03-04 · Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, Yuke Zhu
Research Track A · General AI
Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lacks a reproducible, large-scale benchmark for systematic evaluation. To fill this gap, we present Rob…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.5
2026-03-26 · Jiaqing Zhang, Hao Wang, Mingjia Yin, Bo Chen, Qinglin Jia, Rui Zhou, Ruiming Tang, ChaoYi Ma, Enhong Chen
Research Track A · General AI
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, retraining models on full historical data for architecture comparison or iteration is prohibitively expensive, severely slowing down model deve…
- Review
- pending
- Role
- unreviewed
- Read
- soon
huggingface
Score 9.5
2026-04-06 · Haoxuan Han, Weijie Wang, Zeyu Zhang, Yefei He, Bohan Zhuang
Research Track A · General AI
Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework tha…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-04-23 · Jebacyril Arockiaraj, Dhruv Parikh, Viktor Prasanna
Research Track A · General AI
On-device continual learning (CL) is critical for edge AI systems operating on non-stationary data streams, but most existing methods rely on backpropagation or exemplar-heavy classifiers, incurring substantial compute, memory, and latency overheads. Hyperdimensional computing (HDC) offers a lightweight alternative thr…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-06-04 · Seyed Arshan Dalili, Mehrdad Mahdavi
Research Track A · General AI
Sparse Autoencoders (SAEs) are widely used for mechanistic interpretability in large language models, yet their formulation assigns each latent feature a single decoder direction, implicitly assuming features to be one-dimensional. We show that this assumption mismatches with the multi-dimensional structure of model fe…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-06-16 · Nethmi Jayasinghe, Diana Gontero, Amit Ranjan Trivedi
Research Track A · General AI
Robots that learn over long deployments must add new skills without losing the shared policy structure that makes earlier skills reusable. We study sequential robot skill learning, where previous trajectories and task losses may be unavailable, and the deployed policy must remain a single shared controller without task…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-06-23 · J. Fernando Hernandez-Garcia, Tomás Figliolia, Beren Millidge
Research Track A · General AI
The loss of plasticity - the ability of a network to learn new information after having already learned older information - is a fundamental challenge in creating artificial neural networks capable of continual learning. Although this phenomenon has been known for decades, it has mostly been studied in older, relativel…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-06-29 · Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangyu Zeng, Kaituo Feng, Qunzhong Wang, Yang Shi, Xiaobin Hu, Xiangyu Yue, Jiaqi Wang, Shuicheng Yan
Research Track A · General AI
On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals. To furnish high-quality supervision sources and thereby elevate the performance frontier of distillation, an intuitive direction is to infuse privileged information to either teache…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-06-29 · Yiting Hu, Lingjie Duan
Research Track A · General AI
Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being adopted across key fields such as large language models and image recognition, yet it remains highly vulnerable to data poisoning that triggers learning divergence or severe excess risk. Despite these threats, a princip…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-07-01 · Yuting Zhang, Yanbei Liu, Zhitao Xiao, Lei Geng, Yanwei Pang, Xiao Wang
Research Track A · General AI
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supervised CGL methods rely on instance-level consistency objectives that enforce stability of individua…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-07-08 · T-H. Hubert Chan, Elaine Shi, Mengshi Zhao, Mingxun Zhou
Research Track A
Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interaction. Motivated by participation privacy, we study single-edit neighboring user streams, where one insertion/deletion shifts all subsequent updates and defeats standard H…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-07-11 · Tairan Huang, Yili Wang, Beibei Hu, Yiting Shi, Qiutong Li, Changlong He, Jianliang Gao
Research Track A · General AI
In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separ…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.5
2026-07-31 · Hang Yan, Zhangxuan GU, Beitong Zhou, Jiaxuan Chen, Runze Li, Yusong Hu, Shuheng Shen, Changhua Meng
Research Track A · Research Track B · General AI
Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, existing agents are typically domain-specific, limiting the deployment and user experience. This motivates the consolidation of specialized models into a single cross-env…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.3
2026-03-03 · Patrick J. Mineault, Thomas L. Griffiths, Sean Escola
Research Track A · General AI
We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call "cognitive dark matter" (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains-metacognition, cognitive flexibility, episodic …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.3
2026-04-19 · Ziao Zhang, Kou Shi, Shiting Huang, Avery Nie, Yu Zeng, Yiming Zhao, Zhen Fang, Qishen Su, Haibo Qiu, Wei Yang, Qingnan Ren, Shun Zou, Wenxuan Huang, Lin Chen, Zehui Chen, Feng Zhao
Research Track A · General AI
As the capability frontier of autonomous agents continues to expand, they are increasingly able to complete specialized tasks through plug-and-play external skills. Yet current benchmarks mostly test whether models can use provided skills, leaving open whether they can discover skills from experience, repair them after…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.3
2026-04-27 · Zhou Ziheng, Huacong Tang, Jinyuan Zhang, Haowei Lin, Bangcheng Yang, Qian Long, Fang Sun, Yizhou Sun, Yitao Liang, Ying Nian Wu, Demetri Terzopoulos, Xiaofeng Gao
Research Track A · General AI
Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence, yet evaluating this capacity has been hindered by the vast complexity gap between scientific discovery and real-world engineering. We introduce SciCrafter, a Minecraft…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.3
2026-07-18 · Hiroyuki Chuma, Kanji Otsuka, Yoichi Sato
Research Track A · General AI
In The Algebraic Mind, Marcus identified three cognitive components: operations over variables, recursively structured representations, and an individual/kind distinction. He left the neural substrate open. A companion paper solves this with VaCoAl, an architecture built on GF(2) XOR-and-shift. It offers exact reversib…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-03-24 · Connor Mclaughlin, Nigel Lee, Lili Su
Research Track A
Machine learning models often need to adapt to new data after deployment due to structured or unstructured real-world dynamics. The Continual Learning (CL) framework enables continuous model adaptation, but most existing approaches either assume each task contains sufficiently many data samples or that the learning tas…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-04-09 · Mu Nan, Muquan Yu, Weijian Mai, Jacob S. Prince, Hossein Adeli, Rui Zhang, Jiahang Cao, Benjamin Becker, John A. Pyles, Margaret M. Henderson, Chunfeng Song, Nikolaus Kriegeskorte, Michael J. Tarr, Xiaoqing Hu, Andrew F. Luo
Research Track A · General AI
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this goal is the substanti…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-04-14 · Chaoyao Shen, Linfeng Jiang, Yixian Shen, Tao Xu, Guoqing Li, Anuj Pathania, Andy D. Pimentel, Meng Zhang
Research Track A
Deep learning (DL) compilers rely on cost models and auto-tuning to optimize tensor programs for target hardware. However, existing approaches depend on large offline datasets, incurring high collection costs and offering suboptimal transferability across platforms. In this paper, we introduce TCL, a novel efficient an…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-04-15 · Ahmadreza Eslaminia, Kuan-Chieh Lu, Klara Nahrstedt, Chenhui Shao
Research Track A
Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume a…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-05-06 · William T. Redman, Erik C. Johnson, Brian Robinson
Research Track A · General AI
Identifying and exploiting common features across domains is at the heart of the human ability to make analogies, and is believed to be crucial for the ability to continually learn. To do this successfully, general and flexible computational strategies must be developed. While the extent to which Transformer neural net…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-05-07 · Wenhan Zheng, Yuyi Mao, Ivan Wang-Hei Ho
Research Track A
Channel state information (CSI)-based human activity recognition (HAR) is vulnerable to performance degradation under domain shifts across varying physical environments. Continual learning (CL) offers a principled way to learn new domains sequentially while preserving past knowledge, but existing CL solutions for CSI-b…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 9.0
2026-05-28 · Hesong Wang, Xin Jin, Lu Lu, Chenhaowen Li, Jian Chen, Qiang Liu, Huan Wang
Research Track A · General AI
Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is still hindered by the inefficiency introduced by processing massive amounts of visual tokens. Although recent approaches achieve extremely low token retention ratios while …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-05-29 · Xiaosong Han, Ke Chen, Xindi Dai, Di Liang, Minlong Peng, Wei Pang, Fausto Giunchiglia, Xiaoyue Feng, Yonghao Liu, Renchu Guan
Research Track A · General AI
In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. However, indiscriminately mixing tasks can dilute task specialization, while sequential fine-tuning (full-parameter or low rank adaptation) of…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-06-04 · Marius Dragoi, Ioana Pintilie, Alexandra Dragomir, Antonio Barbalau, Florin Brad
Research Track A
Parameter-efficient finetuning methods based on spectral decomposition have enabled progress in Continual Learning. In this paper we introduce TailLoR, which utilizes the singular bases U and V of the pre-trained weights as a fixed reference frame to learn a low-rank update applied to the singular value matrix. A soft …
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-07-20 · Alya Almsouti, Lotfi Mecharbat, Noha Aboukhater, Yousef Alabrach, Siddiq Anwar, Andre Kumar, Ibrahim Almakky, Mohammad Yaqub
Research Track A · General AI
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, automated LUS analysis remains challenging because of speckle noise, imaging artifacts, and operator-dependent acquisition variability. In this work, we present a deep lea…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-07-27 · Liwei Dong, Jiahao Zhao, Nan Xu
Research Track A · General AI
Large language models increasingly solve scientific-computing tasks, but executable feedback from one problem rarely becomes durable capability on subsequent problems. We study scientific-computing experience consolidation: converting verified runtime experience into transferable procedural knowledge and persistent mod…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 9.0
2026-07-28 · Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
Research Track A · General AI
Covariate shift across training-data partitions biases model selection and parameter estimation in cross-validation, lifelong learning, and federated learning. We propose \textit{Partition-Induced Covariate-shift Correction} (\texttt{PIcsC}), a Fisher information-based regularization framework that mitigates distributi…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-07-29 · Xiao Yue, Guangzhi Qu
Research Track A · General AI
Graph-text retrieval typically maps a graph and its description to a single embedding, even when a query concerns only one semantic aspect, such as a class label or molecular property. Multiple heads can separate these aspects, but a change in the query head may alter retrieval even when the wrong text is sent to that …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-08-02 · Pengxiang Wang, Hongbo Bo, Jun Hong, Weiru Liu, Kedian Mu
Research Track A
Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of architecture-based approaches have been proposed to address this problem. However, the architecture-based approaches suffer from another pr…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 9.0
2026-08-13 · Tiankuo Zhang, Jihye Jung, Paria Nourmohammadi, Benoit Montreuil, Alan Erera, Sahrish Jaleel Shaikh
Research Track A · General AI
The less-than-truckload (LTL) industry plays a vital role in enhancing the efficiency and sustainability of logistics systems, as LTL shipments offer greater consolidation opportunities than full-truckload shipments. Despite of this flexibility, the average cost of LTL shipments remains considerably higher due to less …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-03-30 · Zidi Tao, A. Agung Julius, John T Wen
Research Track A
Sleep is vital for maintaining cognitive function, facilitating metabolic waste removal, and supporting memory consolidation. However, modern societal demands, particularly shift work, often disrupt natural sleep patterns. This can induce excessive sleepiness among shift workers in critical sectors such as healthcare a…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.5
2026-04-14 · Tianchang Shen, Sherwin Bahmani, Kai He, Sangeetha Grama Srinivasan, Tianshi Cao, Jiawei Ren, Ruilong Li, Zian Wang, Nicholas Sharp, Zan Gojcic, Sanja Fidler, Jiahui Huang, Huan Ling, Jun Gao, Xuanchi Ren
Research Track A
Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative reconstruction approach combines the visual fidelity and creative capacity of video m…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.5
2026-04-15 · Julian Killingback, Ofer Meshi, Henry Li, Hamed Zamani, Maryam Karimzadehgan
Research Track A · General AI
Traditional Retrieval-Augmented Generation (RAG) approaches generally assume that retrieval and generation occur on powerful servers removed from the end user. While this reduces local hardware constraints, it introduces significant drawbacks: privacy concerns regarding data access, recurring maintenance and storage co…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-04-22 · Daniele Corradetti, Renato Corradetti
Research Track A · General AI
We present a biologically detailed extension of the classical Hopfield/Marr auto-associative memory model for CA3, implementing ten populations (two asymmetric pyramidal subtypes, eight GABAergic interneuron classes), forty-seven compartments, multi-rule plasticity (recurrent Hebb, BCM anti-saturation, mossy-fiber shor…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.5
2026-05-27 · Rajarshi Chowdhury, Akshay Shah, Zakaria Alrmaih, Chenhao Guo, Anubhav Singh, Sue Lee
Research Track A · General AI
Oracle Exadata consolidates thousands of tenant databases onto shared storage infrastructure deployed at hundreds of customer sites worldwide. Oracle Multitenant architecture enables this extreme density, with thousands of tenant databases sharing a single Exadata storage system -- but this creates a multi-level resour…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-05-28 · Zhenyu Sun, Zheng Xu, Ermin Wei
Research Track A · General AI
Reinforcement Learning from Human Feedback (RLHF) typically relies on static reward models to align Large Language Models with human preferences. However, human values are inherently diverse and heterogeneous, and a single reward model often lacks the robustness required to generalize to unseen preference domains. Whil…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-06-09 · Yu Lu, Junjie Yang, Piotr Koniusz, YuXin Song, Yi Yang
Research Track A · General AI
Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache methods reduce this cost with local windows, sink tokens, or compressed memory states, yet they usually assign fixed roles to different parts o…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 8.5
2026-06-16 · Zhexiao Xiong, Yizhi Song, Hao Kang, Qing Yan, Liming Jiang, Jenson Yang, Zhoujie Fu, Stathi Fotiadis, Angtian Wang, Zichuan Liu, Bo Liu, Yiding Yang, Xin Lu, Nathan Jacobs
Research Track A · General AI
Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions correspond to motion (e.g., walk, turn, look around), while interaction with objects in the scene (e.g., pick up plates, open doors, or trigger phy…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.5
2026-06-30 · Kaisen Yang, Zheng Jiang, Yuzhao Peng, Houde Qian, Boshi Zhang, Youjie Zheng, Shijin Hong, Qingle Liu, Ruoyu Han, Bohan Lyu, Bingxiang He, Eren Cai, Calvin Xiao, Qinhuai Na
Research Track A · Research Track B · General AI
Internet users collectively perform an enormous range of skilled work through web browsers, from software development and document editing to search, forms, and enterprise workflows, making human browsing a highly scalable but under-exploited source of reusable browser skills. We argue that the bottleneck for browser a…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-07-11 · Jin Li, Jiawei Chen
Research Track A
A persistent interactive world model keeps its running state resident on the GPU that serves it: a multi-gigabyte attention cache, almost all of it rewritten at every generation step. That state cannot be recomputed in interactive time or approximated without changing the world, so a live session pins its device. The p…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-07-13 · Landon Liu, Mary Kelly, Alan Tsang
Research Track A · General AI
Shared meaning in language requires people to learn and agree on categories. We ask how characteristics of agents' memories change the emergence and evolution of shared meaning. Without a coordination game, models of conceptual semantics cannot explain how shared meaning emerges and changes in groups of people; however…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.5
2026-07-27 · Joel Siegert, Edoardo Ghignone, Michele Magno
Research Track A · General AI
A key challenge in modern robotics is to adapt to changing environments, a challenge that is exacerbated when simulations cannot encompass every possible real-world configuration, and therefore Reinforcement Learning (RL) in the physical world becomes necessary. Continual Reinforcement Learning provides the tools to ad…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.5
2026-07-28 · Zeki Doruk Erden
Research Track A · General AI
Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure. A recently proposed developmental, gradient-free learning framework addresses these limitations by learning a discrete, topological model of its inputs through local variation and selecti…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.4
2026-08-05 · Talha Waqas, Mounir Lahlouh, Kawther Taibouni, Mahnoor Waqas, Salar Ahmed, Sébastien Mulé, Yasmina Leroul-Chenoune
Research Track A
Slice-wise 2D object detectors are increasingly applied to volumetric data due to their computational efficiency and scalability, yet they often yield fragmented and unstable predictions along the depth axis. We propose YOLO-PVC, a lightweight and model-agnostic framework for 2D-to-3D consolidation of slice-wise detect…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.4
2026-08-09 · Smadar Szekely, Judith Gal-Ezer, David Harel
Research Track A
Higher education stands at a turning point. In an era where knowledge is increasingly accessible and which is, more often than not, mediated by advanced Artificial Intelligence (AI), the value of traditional curricula models warrants reconsideration. This does not imply that one should replace thorough academic studies…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.3
2026-03-26 · Moiz Sadiq Awan, Muhammad Haris Noor, Muhammad Salman Munaf
Research Track A · General AI
Automated benchmarks dominate the evaluation of large language models, yet no systematic study has compared user satisfaction, adoption motivations, and frustrations across competing platforms using a consistent instrument. We address this gap with a cross-platform survey of 388 active AI chat users, comparing satisfac…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.3
2026-04-21 · Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, Gautam Biswas
Research Track A · General AI
Reinforcement learning (RL) offers a compelling data-driven paradigm for synthesizing controllers for complex systems when accurate physical models are unavailable; however, most existing control-oriented RL methods assume stationarity and, therefore, struggle in real-world non-stationary deployments where system dynam…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.3
2026-06-05 · Arthur Bouton, Tristan D. Hasseler, Michael Paton, Travis Brown, Jacob Levy, William Reid, Joshua Martin, Hari Nayar
Research Track A · General AI
This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution. A single neural network controller, trained to track a desired path across cha…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-03-31 · Shanxian Lin, Yuichi Nagata, Haichuan Yang
Research Track A
Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Evolutionary Algorithms (EA) excel at exploring solution spaces but lack mechanisms to accumulate and reuse procedural knowledge from successful search trajectories. This paper proposes Associative Constructive Evolution (ACE), a framework that enha…
- Review
- pending
- Role
- unreviewed
- Read
- now
arxiv
Score 8.0
2026-04-05 · Leonardo Bitzki, Diego Kreutz, Tiago Heinrich, Douglas Fideles, Leandro Bertholdo, Silvio Quincozes, Angelo Diniz
Research Track A
Cybersecurity research increasingly depends on reproducible evidence, such as traffic traces, logs, and labeled datasets, yet most public datasets remain static and offer limited support for controlled re-execution and traceability, especially in heterogeneous multi-protocol environments. This paper presents NetSecBed,…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-04-12 · Yuan Sun, Hong Yi, Jinyuan Liu
Research Track A
Personalized learning systems are almost universally designed around a single objective: help people acquire knowledge and skills more efficiently. We argue this framing misses the more consequential problem. The most damaging failures in human life-financial ruin, health collapse, professional obsolescence-are rarely …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-04-15 · Mohammad Nooraiepour, Zezhang Song, Wei Li, Sarah Perez
Research Track A
Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a phys…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-04-27 · Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh Hong Nguyen, Trung Le
Research Track A · General AI
Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coordination of how inform…
- Review
- pending
- Role
- unreviewed
- Read
- now
huggingface
Score 8.0
2026-05-07 · Pranav Mantini, Shishir K. Shah
Research Track A
We address the challenge of knowledge composition in Vision-Language Models (VLMs), where accumulating expertise across multiple domains or tasks typically leads to catastrophic forgetting. We introduce GeoStack (Geometric Stacking), a modular framework that allows independently trained domain experts to be composed in…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-05-11 · Wei Chow, Linfeng Li, Xian Sun, Lingdong Kong, Zefeng Li, Qi Xu, Hang Song, Tian Ye, Xian Wang, Jinbin Bai, Shilin Xu, Xiangtai Li, Junting Pan, Shaoteng Liu, Ran Zhou, Tianshu Yang, Songhua Liu
Research Track A · General AI
Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications to propagate into areas that should remain intact. We propose a fundamentally different approach by leveraging Masked Generative Transformers (MGTs), whose localized tok…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-05-14 · Gloria Fernández-Nieto, Kiyoshige Garcés, Mladen Raković, Tongguang Li, Xinyu Li, Linxuan Zhao, Dragan Gašević
Research Track A
Background: Abilities for effective self-regulated learning (SRL) are critical for lifelong learning, particularly during adolescence when these skills consolidate and strongly influence future learning. Their importance has grown with the rise of online and blended education. Yet, little is known about how secondary s…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-05-21 · Sayantani Ghosh, Rajashik Datta, Amit Kumar Das, Amlan Chakrabarti
Research Track A
Transformer-based language models such as BERT having 110M+ parameters have revolutionized natural language understanding, yet their internal mechanisms remain largely opaque to researchers and practitioners. Traditional attention-based interpretability methods often emphasize structurally important but semantically we…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-05-29 · Fatima Ahmad Muazu, Festus Adedoyin, Huseyin Dogan, Abiodun Adedeji, Melike Akca, Olumuyiwa Ayorinde
Research Track A · General AI
This study investigates how UX research (UXR) principles, combined with Large Language Model (LLM)-supported analysis, can be used to improve the quality of requirements for mobile learning systems designed for learners with cognitive disabilities. Using the UXR Point-of-View (PoV) pyramid as a methodological framework…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-06-11 · Xiaobin Zhang, Lefei Shen, Mouxiang Chen, Zhuo Li, Hongkai Li, Han Fu, Jianling Sun, Xiaoxue Ren, Chenghao Liu
Research Track A · General AI
Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-optimize paradigm has emerged to optimize consolidation by anticipating future demands. While emerging time series foundation models promise to…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-06-13 · Ida Momennejad, Roberta Raileanu
Research Track A
Open-ended intelligence is the capacity to adapt to novel problems and environments that are substantially different from those in training. A mathematics of open-ended intelligence requires two pillars: first, a minimal set of representational primitives (e.g., states, actions) and algorithmic primitives (e.g., neares…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-06-14 · Fendi Tsim, Alina Gutoreva
Research Track A
We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition. In SCAN, we systematize and formalize AI-human interaction by introducing a task-identif…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-07-17 · Damani Mguni-Coker
Research Track A · General AI
On-the-fly reconstruction is a key requirement for many applications in robotics and autonomous navigation. Variational Bayes Gaussian Splatting (VBGS) enables continual learning without replay buffers using Coordinate Ascent Variational Inference (CAVI), but its per-frame iterations over all observed points make it to…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 8.0
2026-08-03 · Wouter J. Schuttert, Mohammed Iqbal Abdul Rasheed, Bojana Rosić
Research Track A
Induction welding plays a central role in enabling lightweight, integrated structures made from fibre-reinforced thermoplastic composites. From a modelling perspective, the induction welding process can be approximated by one-way coupled electromagnetic and heat-transfer equations. In practice, material parameters such…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.8
2026-04-28 · Shuxiang Cao, Zijian Zhang, Abhishek Agarwal, Grace Bratrud, Niyaz R. Beysengulov, Daniel C. Cole, Alejandro Gómez Frieiro, Elena O. Glen, Hao Hsu, Gang Huang, Raymond Jow, Greshma Shaji, Tom Lubowe, Ligeng Zhu, Luis Mantilla Calderón, Nicola Pancotti, Joel Pendleton, Brandon Severin, Charles Etienne Staub, Sara Sussman, Antti Vepsäläinen, Neel Rajeshbhai Vora, Yilun Xu, Varinia Bernales, Daniel Bowring, Elica Kyoseva, Ivan Rungger, Giulia Semeghini, Sam Stanwyck, Timothy Costa, Alán Aspuru-Guzik, Krysta Svore
Research Track A · General AI
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-04-14 · Ramy E. Ali, Federico Penna
Research Track A
Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-04-24 · Hillary Mutisya, John Mugane
Research Track A · General AI
We investigate whether neural models trained exclusively on modern morphological data can recover cross-lingual lexical structure consistent with historical reconstruction. Using BantuMorph v7, a transformer over Bantu morphological paradigms, we analyze 14 Eastern and Southern Bantu languages, extract encoder embeddin…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-05-19 · Prashant Pandey, Himanshu Kumar, Devineni Sri Venkatraya Chowdary, Brejesh Lall
Research Track A
Evolving data streams induce joint nonstationarity in continual semantic segmentation, where semantic classes, input distributions, and supervision availability change simultaneously over time. This setting reflects practical structured prediction systems, yet remains largely unexplored in prior continual learning work…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-07-16 · Patrik Wolf, Thomas Kleine Buening, Andreas Krause, Celestine Mendler-Dünner
Research Track A
In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model's output is treated as an estimate of the corresponding conditional distribution. If this interpretation holds, then LLM estimates should satisfy basic probabilistic identities. In parti…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-07-21 · Vahid Satarifard, Fabian Baumann, Geetanjali Minsky, Laura Sisson, Lou M. Haux, Christophe Laudamiel, Nicholas A. Christakis
Research Track A
Perfumes are cultural artifacts and works of sensory art, composed from a finite, recombinable palette of notes that together evoke a distinctive scent impression. Here, we assemble the largest perfume corpus compiled to date, spanning multiple independent databases from 1900 to 2024, and study its evolution through a …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-07-31 · Xiyang Zhang, Yuanhe Tian, Hongzhi Wang
Research Track A
Simulator-based placement pipelines may inspect many repairs but deploy only when several reliability criteria improve together. Reusing search scenes to test the selected action invalidates nominal evidence, while scalarization can trade away the weakest criterion. We introduce Certificate-Aligned Recomposition (CARA)…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.5
2026-07-31 · Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori
Research Track A
This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medi…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.3
2026-04-13 · WonJin Yoon, Kangyu Zhu, Ian Bulovic, Autumn Sehy, Yanjun Gao, Dmitriy Dligach, Majid Afshar, Timothy A. Miller
Research Track A · General AI
With the recent progress of Large Language Models (LLMs), there is a growing interest in applying these models to solve complex and challenging problems. Modern LLMs, capable of processing long contexts and generating verbalized explanations, offer significant potential in addressing real-world applications. However, a…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.3
2026-05-28 · Yan Chen, Taojie Zhu, Meng Zhang, Xin Chen, Jiaqi Huang, Dongyang Xu, Yizhi Wang
Research Track A · General AI
Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers from catastrophic forgetting of earlier capabilities. Recent work shows that on-policy signals -- training on the model's own outputs -- reduce forgetting more reliably…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.3
2026-06-14 · Xiongjun Guan, Jianjiang Feng, Jie Zhou
Research Track A · General AI
Small-area fingerprint sensing on mobile devices creates a fundamental mismatch between acquisition and recognition: each touch captures only a tiny, pose-varying local patch, while reliable biometric matching ultimately requires a stable and sufficiently complete fingerprint representation. Existing pipelines largely …
- Review
- pending
- Role
- unreviewed
- Read
- soon
huggingface
Score 7.0
2026-06-15 · Hyungmin Kim, Minsoo Kim, Hongseok Kim, Jungwook Choi
Research Track A · General AI
Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memory -- not compute -- the binding constraint on throughput. Non-uniform KV compression, which allocates heterogeneous budgets across attention…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.0
2026-06-29 · Tianyu Wang, Gourav Rattihalli, Aditya Dhakal, Longfei Shangguan, Dejan Milojicic
Research Track A
As LLM inference becomes a major cloud workload, its growing energy footprint makes cluster-wide energy optimization increasingly important. Serverless LLM serving helps platforms absorb traffic volatility by elastically sharing GPU resources across models, but this sharing also makes energy optimization difficult. Mul…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.0
2026-07-10 · Li Hengyu
Research Track A · General AI
The attention matrix of a causal transformer is row-stochastic, iterated over depth, and non-normal by construction. For non-normal operators, eigenvalues control only asymptotic behavior; finite-depth behavior is controlled by resolvent quantities such as pseudospectra and Kreiss constants. We test, under pre-register…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.0
2026-07-14 · Lorenzo Busoni, Guido Agapito, Marco Bonaglia, Alfio Puglisi, Marco Xompero, Matteo Aliverti, Francesca Annibali, Carmelo Arcidiacono, Natalia Auricchio, Nicolò Azzaroli, Andrea Balestra, Alessandro Ballone, Louis Barbier, Andrea Baruffolo, Federico Battaini, Maria Bergomi, Andrea Bianco, Michele Cantiello, Giulio Capasso, Giulia Carlà, Enrico Cascone, Ed Chapin, Manal Chebbo, Simonetta Chinellato, Vincenzo Cianniello, Paolo Ciliegi, Mirko Colapietro, Jean-Jacques Correia, Giuseppe Cosentino, Elia Costa, Matteo D'ambrogio, Vincenzo De Caprio, Giuseppe De Luca, Nicholas Devaney, Ivan Di Antonio, Amico Di Cianno, Simone Di Filippo, Benedetta Di Francesco, Ugo Di Giammatteo, Chiara Di Prospero, Gianluca Di Rico, Andrea Di Rocco, Daphne Diretto, Christian Eredia, Simone Esposito, Jacopo Farinato, Italo Foppiani, Takashi Funakawa, Fulvio Gianotti, Laurence Gluck, Davide Greggio, Sylvain Guieu, Marco Gullieuszik, Yuuichi Harikane, Masahiro Ikoma, Laurent Jocou, Dan Kerley, Mikio Kurita, Salvatore Lampitelli, Tommaso Lapucci, Fulvio Laudisio, Yves Magnard, Demetrio Magrin, Hossein Mahmoodzadeh, Dheeraj Malik, Luca Marafatto, Laurence Michaud, Christophe Michel, Satoshi Miyazaki, Kentaro Motohara, David Mouillet, Thibaut Moulin, Matteo Munari, Kentaro Nagamine, Sylvain Oberti, Fabrice Pancher, Giorgio Pariani, Sophie Penger, Amedeo Petrella, Laurent Pinard, Cédric Plantet, Elisa Portaluri, Kalyan Radhakrishnan, Roberto Ragazzoni, Edoardo Redaelli, Edgar Renault, Colin Richardson, Marco Riva, Sylvain Rochat, Gabriele Rodeghiero, Luca Rosignoli, Bernardo Salasnich, Benoit Sassolas, Salvatore Savarese, Marcello Scalera, Pietro Schipani, Danilo Selvestrel, Mahshid Shiri, Mina Sibalic, Malcolm Smith, Sebastian Soler, Rosanna Sordo, Alessandro Tacchini, Alessio Taranto, Ludovico Teodori, Gabriele Umbriaco, Yoshinori Uzawa, Angelo Valentini, Jean-Pierre Véran
Research Track A
The Multiconjugate adaptive Optics Relay For ELT Observations (MORFEO) is a first-generation adaptive optics module for the Extremely Large Telescope (ELT), designed to deliver a diffraction-limited, highly uniform 53x53 arcsec field of view to the MICADO near-infrared camera. As the project advances toward its Final D…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.0
2026-07-14 · Zhenwen Miao, Honglin Wang, Mingheng Mi
Research Track A · General AI
As LLM technology advances, the space of model families, compute hardware, quantization schemes, parallelization strategies, and specialized optimization kernels continues to expand, sharply increasing the code complexity and maintenance cost of general-purpose inference frameworks. Conventional software engineering us…
- Review
- pending
- Role
- unreviewed
- Read
- soon
huggingface
Score 7.0
2026-07-23 · Chenhui Gou, Haoqin Tu, Yunhao Fang, Jianfei Cai, Hamid Rezatofighi
Research Track A · General AI
Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once that experience is re…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 7.0
2026-07-27 · Xinyang Wen
Research Track A
Dynamic applications, including optimal-transport Flow Matching, repeatedly solve related entropic optimal transport problems, yet conventional distributed Sinkhorn processes frames sequentially and synchronizes after every iteration. We present TemporalSinkhorn, a parallel-in-time executor that batches future candidat…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-03-26 · Mohamed Eltahir, Ahmed O. Ibrahim, Obada Siralkhatim, Tabarak Abdallah, Sondos Mohamed
Research Track A · General AI
Vision-Language Models (VLMs) are powerful open-set reasoners, yet their direct use as anomaly detectors in video surveillance is fragile: without calibrated anomaly priors, they alternate between missed detections and hallucinated false alarms. We argue the problem is not the VLM itself but how it is used. VLMs should…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-03-27 · Rangya Zhang, Jiaping Xiao, Lu Bai, Yuhang Zhang, Mir Feroskhan
Research Track A
Continual learning seeks to maintain stable adaptation under non-stationary environments, yet this problem becomes particularly challenging in object detection, where most existing methods implicitly assume relatively balanced visual conditions. In extreme-sparsity regimes, such as those observed in space-based residen…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-03-27 · Xuerui Zhang, Xuehao Wang, Zhan Zhuang, Linglan Zhao, Ziyue Li, Xinmin Zhang, Zhihuan Song, Yu Zhang
Research Track A
Lifelong learning aims to preserve knowledge acquired from previous tasks while incorporating knowledge from a sequence of new tasks. However, most prior work explores only streams of homogeneous tasks (\textit{e.g.}, only classification tasks) and neglects the scenario of learning across heterogeneous tasks that posse…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-03-29 · Chunmei Wang, Shangyou Zhang
Research Track A
This paper presents an auto-stabilized weak Galerkin (WG) finite element method for the Biot's consolidation model within the classical displacement-pressure two-field formulation. Unlike traditional WG approaches, the proposed scheme achieves numerical stability without the requirement of traditional stabilizers. Spat…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-04-13 · Rok Spruk
Research Track A
This paper develops a political-economy theory of statehood without capacity. I argue that under specific institutional and geopolitical conditions, a polity can become trapped in an equilibrium of nominal statehood: a state in which claims to sovereignty, external recognition, and symbolic legitimacy persist or even s…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-04-17 · Jason Cusati, Chris Brown
Research Track A
Software engineering research has experienced rapid growth in both output and participation over the past decades. Yet concerns persist about the field's ability to accumulate, integrate, and reuse knowledge in ways that support long-term progress. To better understand how the community itself perceives these challenge…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-04-18 · Daeun Hwangbo, Junyeong Park, Minjeong Jeon, Ick Hoon Jin
Research Track A
Computer-based assessments routinely generate detailed interaction logs -- commonly referred to as process data -- that record every action a respondent performs during task completion, yet systematic preprocessing guidance, integrated analytical workflows, and cross-method consistency checks remain scarce in the liter…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-04-20 · Stefan Tanevski
Research Track A
This paper asks how institutional stock-market integration reshapes the transmission of monetary policy through asset prices in small open economies. Motivated by the persistent segmentation of Western Balkan capital markets, we develop a two-stage counterfactual transmission framework to identify how stock-exchange co…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-05-07 · Zhengru Fang, Yanan Ma, Yu Guo, Senkang Hu, Yixian Zhang, Hangcheng Cao, Wenbo Ding, Yuguang Fang
Research Track A · General AI
When a chest X-ray shows consolidation but the question asks which finding is present, a medical vision-language model may answer "No consolidation." This is more than an incorrect choice: it is a polarity reversal that emits a clinical statement contradicting the image. We study this failure as negated-option attracti…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-05-10 · Roni Blushtein-Livnon, Tal Svoray, Itay Fischhendler, Havatzelet Yahel, Emir Galilee
Research Track A
In traditional rural societies, where social ties are embedded in physical space, the diffusion of emerging technologies may be amplified through socio-spatial contagion (SSC). Such processes may play a key role in accelerating residential PV adoption in off-grid regions. Yet empirical evidence on SSC in PV adoption re…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-05-29 · Arbaz Khan, Jeonghun J. Lee, Harpal Singh
Research Track A
In this paper, we propose and analyze a novel two-field symmetric formulation with solid displacement and fluid pressure as main unknowns for the Biot's consolidation model in poroelasticity. Firstly, we prove the well-posedness of the new formulation and then show the existence and uniqueness of optimal control where …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-06-10 · Stephen Kasica, Charles Berret, Tamara Munzner
Research Track A
Data journalists routinely integrate records across multiple independently published sources to support accountability reporting, yet no existing interactive wrangling tool treats the collection of tables -- rather than the single table -- as its primary unit of work. We present OpenRoundup, an open-source, browser-bas…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-06-11 · Víctor Blanco, J. Fernando Camacho-Vallejo, Yolanda Hinojosa
Research Track A
Urban waste management faces increasing operational and environmental challenges driven by population growth, heterogeneous waste streams, traffic congestion, and the need for sustainable collection infrastructures. We present an integrated optimization framework for the design of multi-type urban waste collection and …
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-06-14 · Qing Su, Kaiyang Li, Yuan Zhuang, Fei Miao, Shihao Ji
Research Track A · General AI
While video segmentation has advanced rapidly on short clips and closed-set benchmarks, open-world video segmentation remains largely unexplored. The challenge is twofold: (1) existing methods are not designed to support object discovery and identity maintenance in long videos of dynamic ego-motion, and (2) existing ev…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-06-16 · Xiongjun Guan, Jianjiang Feng, Jie Zhou
Research Track A
Fingerprint recognition is still dominated by task-specific pipelines, where enhancement, structural parsing, alignment, and matching are optimized in isolation. Although effective in narrow settings, this design limits representation reuse across sensors, qualities, and downstream applications. We therefore present Uo…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.5
2026-06-17 · Wenqi Jia, Zhewen Hu, Ying Huang, Yu Gong, Stavros Kalafatis, Yuke Wang, Wei Niu, Chengming Zhang, Ang Li, Sheng Di, Yuede Ji, Bo Fang, Miao Yin
Research Track A
3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gaussians across multiple GPUs. Existing distributed approaches either partition scenes into isolated regions, causing global inconsistency, or…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.3
2026-04-17 · Hitesh Mehta, Arjit Saxena, Garima Chhikara, Rohit Kumar
Research Track A · General AI
This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness Theory by Brown and Levinson and the Impoliteness Framework by Culpeper form the basis of experiments conducted across three languages (English, Hindi, Spanish), five mo…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.3
2026-06-11 · Jialin Gan, Xin Qiu, Guangzhe Chen, Xue Wang
Research Track A · General AI
Large language models (LLMs) have enabled time series (TS) analysis by jointly modeling numerical observations and textual context through a shared token interface. However, TS tokens and prompt tokens exhibit fundamentally different information structures, making uniform token processing inefficient. In this paper, we…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.0
2026-04-02 · William Hoy, Binxu Wang, Xu Pan
Research Track A · General AI
Evolution Strategies (ES) have emerged as a scalable gradient-free alternative to reinforcement learning based LLM fine-tuning, but it remains unclear whether comparable task performance implies comparable solutions in parameter space. We compare ES and Group Relative Policy Optimization (GRPO) across four tasks in bot…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.0
2026-04-02 · Zhanting Zhou, KaHou Tam, Ziqiang Zheng, Zeyu Ma
Research Track A · General AI
Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned. Approximate machine unlearning offers an efficient alternative to full retraining, yet existing methods for MRS mainly rely on a largely uni…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.0
2026-04-07 · Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao, Yohei Oseki, Masaru Isonuma
Research Track A · General AI
When introducing Large Language Models (LLMs) into industrial applications, such as healthcare and education, the risk of generating harmful content becomes a significant challenge. While existing machine unlearning methods can erase specific harmful knowledge and expressions, diverse harmful content makes comprehensiv…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 6.0
2026-04-16 · Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang, Wenpeng Lu
Research Track A
Over the past year, spatial intelligence has drawn increasing attention. Many prior works study it from the perspective of visual-spatial intelligence, where models have access to visuospatial information from visual inputs. However, in the absence of visual information, whether linguistic intelligence alone is suffici…
- Review
- pending
- Role
- unreviewed
- Read
- soon
huggingface
Score 6.0
2026-05-29 · Stine Lyngsø Beltoft, William Brach, Federico Torrielli, Jacob Nielsen, Annemette Brok Pirchert, Filippo Tonini, Peter Schneider-Kamp, Lukas Galke Poech
Research Track A · General AI
Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding human oversight. Here, we study the emergent languages on Moltbook. For this, we build upon the Moltbook Files dataset and apply a two-stage app…
- Review
- pending
- Role
- unreviewed
- Read
- soon
arxiv
Score 5.5
2026-03-29 · Zhongying Deng, Cheng Tang, Ziyan Huang, Jiashi Lin, Ying Chen, Junzhi Ning, Chenglong Ma, Jiyao Liu, Wei Li, Yinghao Zhu, Shujian Gao, Yanyan Huang, Sibo Ju, Yanzhou Su, Pengcheng Chen, Wenhao Tang, Tianbin Li, Haoyu Wang, Yuanfeng Ji, Hui Sun, Shaobo Min, Liang Peng, Feilong Tang, Haochen Xue, Rulin Zhou, Chaoyang Zhang, Wenjie Li, Shaohao Rui, Weijie Ma, Xingyue Zhao, Yibin Wang, Kun Yuan, Zhaohui Lu, Shujun Wang, Jinjie Wei, Lihao Liu, Dingkang Yang, Lin Wang, Yulong Li, Haolin Yang, Yiqing Shen, Lequan Yu, Xiaowei Hu, Yun Gu, Yicheng Wu, Benyou Wang, Minghui Zhang, Angelica I. Aviles-Rivero, Qi Gao, Hongming Shan, Xiaoyu Ren, Fang Yan, Hongyu Zhou, Haodong Duan, Maosong Cao, Shanshan Wang, Bin Fu, Xiaomeng Li, Zhi Hou, Chunfeng Song, Lei Bai, Yuan Cheng, Yuandong Pu, Xiang Li, Wenhai Wang, Hao Chen, Jiaxin Zhuang, Songyang Zhang, Huiguang He, Mengzhang Li, Bohan Zhuang, Zhian Bai, Rongshan Yu, Liansheng Wang, Yukun Zhou, Xiaosong Wang, Xin Guo, Guanbin Li, Xiangru Lin, Dakai Jin, Mianxin Liu, Wenlong Zhang, Qi Qin, Conghui He, Yuqiang Li, Ye Luo, Nanqing Dong, Jie Xu, Wenqi Shao, Bo Zhang, Qiujuan Yan, Yihao Liu, Jun Ma, Zhi Lu, Yuewen Cao, Zongwei Zhou, Jianming Liang, Shixiang Tang, Qi Duan, Dongzhan Zhou, Chen Jiang, Yuyin Zhou, Yanwu Xu, Jiancheng Yang, Shaoting Zhang, Xiaohong Liu, Siqi Luo, Yi Xin, Chaoyu Liu, Haochen Wen, Xin Chen, Alejandro Lozano, Min Woo Sun, Yuhui Zhang, Yue Yao, Xiaoxiao Sun, Serena Yeung-Levy, Xia Li, Jing Ke, Chunhui Zhang, Zongyuan Ge, Ming Hu, Jin Ye, Zhifeng Li, Yirong Chen, Yu Qiao, Junjun He
Research Track A
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical e…
- Review
- pending
- Role
- unreviewed
- Read
- soon