arxiv
Score 35.5
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…
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arxiv
Score 30.5
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…
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arxiv
Score 30.0
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,…
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arxiv
Score 26.5
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…
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arxiv
Score 26.5
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…
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- unreviewed
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arxiv
Score 26.4
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…
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arxiv
Score 26.0
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…
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- pending
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arxiv
Score 26.0
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…
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- pending
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- unreviewed
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arxiv
Score 25.8
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…
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- pending
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arxiv
Score 25.5
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…
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- pending
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- unreviewed
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arxiv
Score 25.0
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…
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- pending
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- unreviewed
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arxiv
Score 25.0
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…
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arxiv
Score 25.0
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 …
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arxiv
Score 24.5
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…
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arxiv
Score 24.5
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…
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- pending
- Role
- unreviewed
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arxiv
Score 24.5
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…
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- pending
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- unreviewed
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arxiv
Score 24.0
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, …
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arxiv
Score 23.5
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…
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- unreviewed
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arxiv
Score 23.5
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…
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- pending
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arxiv
Score 23.0
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…
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arxiv
Score 23.0
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 …
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arxiv
Score 23.0
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…
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arxiv
Score 22.0
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…
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arxiv
Score 22.0
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 …
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arxiv
Score 22.0
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…
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arxiv
Score 21.5
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…
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arxiv
Score 21.5
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…
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- pending
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arxiv
Score 21.4
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…
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arxiv
Score 21.3
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…
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- pending
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arxiv
Score 21.3
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 …
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- pending
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- unreviewed
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arxiv
Score 21.0
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…
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- unreviewed
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arxiv
Score 21.0
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…
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arxiv
Score 20.8
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 …
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arxiv
Score 20.8
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 …
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arxiv
Score 20.8
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…
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arxiv
Score 20.5
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…
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arxiv
Score 20.5
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…
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arxiv
Score 20.3
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 …
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arxiv
Score 20.0
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…
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- pending
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- unreviewed
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arxiv
Score 20.0
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…
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- pending
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arxiv
Score 20.0
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…
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arxiv
Score 19.5
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…
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arxiv
Score 19.5
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…
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arxiv
Score 19.5
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…
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arxiv
Score 19.4
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…
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arxiv
Score 19.0
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…
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- pending
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arxiv
Score 18.8
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…
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arxiv
Score 18.4
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 …
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arxiv
Score 18.3
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…
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arxiv
Score 18.3
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…
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arxiv
Score 18.2
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…
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arxiv
Score 18.0
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…
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arxiv
Score 18.0
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…
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arxiv
Score 18.0
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…
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arxiv
Score 17.9
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…
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arxiv
Score 17.5
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…
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arxiv
Score 17.4
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…
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arxiv
Score 17.3
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…
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arxiv
Score 17.3
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, …
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arxiv
Score 17.3
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…
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arxiv
Score 17.0
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…
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- unreviewed
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arxiv
Score 17.0
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…
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- unreviewed
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arxiv
Score 17.0
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…
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- unreviewed
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arxiv
Score 17.0
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…
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- pending
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- unreviewed
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arxiv
Score 16.8
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 …
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- pending
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- unreviewed
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arxiv
Score 16.5
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…
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- pending
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- unreviewed
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arxiv
Score 16.5
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…
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- pending
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- unreviewed
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arxiv
Score 16.5
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…
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- pending
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- unreviewed
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arxiv
Score 16.5
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 16.5
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 16.3
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 …
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- pending
- Role
- unreviewed
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arxiv
Score 16.3
2026-04-22 · Yuxuan Cai, Jie Zhou, Qin Chen, Liang He
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 …
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- pending
- Role
- unreviewed
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- now
arxiv
Score 16.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 16.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 16.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 16.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 16.0
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…
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- pending
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- unreviewed
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arxiv
Score 15.9
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…
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- pending
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- unreviewed
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- now
arxiv
Score 15.9
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.8
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 …
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.8
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.5
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…
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- unreviewed
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- now
arxiv
Score 15.5
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…
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- pending
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- unreviewed
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arxiv
Score 15.3
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…
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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…
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- unreviewed
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- now
arxiv
Score 15.0
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 …
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 15.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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 …
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- pending
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- unreviewed
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- now
arxiv
Score 15.0
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 …
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- unreviewed
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arxiv
Score 15.0
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.…
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- unreviewed
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arxiv
Score 15.0
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…
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- unreviewed
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arxiv
Score 14.8
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…
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- pending
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- unreviewed
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- now
arxiv
Score 14.5
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…
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- pending
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- unreviewed
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- now
arxiv
Score 14.5
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…
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- pending
- Role
- unreviewed
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arxiv
Score 14.5
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…
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- pending
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- unreviewed
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arxiv
Score 14.5
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…
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- unreviewed
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arxiv
Score 14.4
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.3
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 14.0
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…
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- pending
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- unreviewed
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- now
arxiv
Score 13.8
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.8
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…
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- pending
- Role
- unreviewed
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arxiv
Score 13.8
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
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- now
arxiv
Score 13.8
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.5
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.5
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 …
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.5
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.3
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.3
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.0
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 …
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- unreviewed
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- soon
arxiv
Score 13.0
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 …
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 13.0
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…
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arxiv
Score 13.0
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…
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huggingface
Score 13.0
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…
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arxiv
Score 13.0
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…
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arxiv
Score 12.8
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…
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arxiv
Score 12.8
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…
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- unreviewed
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arxiv
Score 12.5
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…
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- pending
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- unreviewed
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huggingface
Score 12.5
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…
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arxiv
Score 12.5
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…
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arxiv
Score 12.4
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…
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arxiv
Score 12.4
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…
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arxiv
Score 12.4
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…
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arxiv
Score 12.3
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…
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- unreviewed
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arxiv
Score 12.3
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 …
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- unreviewed
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arxiv
Score 12.3
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:…
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arxiv
Score 12.2
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…
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arxiv
Score 12.0
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 (…
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arxiv
Score 12.0
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…
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- unreviewed
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arxiv
Score 11.8
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…
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arxiv
Score 11.8
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…
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arxiv
Score 11.8
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…
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arxiv
Score 11.5
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…
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arxiv
Score 11.5
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…
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arxiv
Score 11.5
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…
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arxiv
Score 11.5
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…
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- pending
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- unreviewed
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arxiv
Score 11.5
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…
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- pending
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- unreviewed
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arxiv
Score 11.5
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…
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- pending
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- unreviewed
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- now
arxiv
Score 11.5
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…
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- pending
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- unreviewed
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arxiv
Score 11.5
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…
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- unreviewed
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arxiv
Score 11.3
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…
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- pending
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- unreviewed
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arxiv
Score 11.0
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…
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arxiv
Score 11.0
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…
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arxiv
Score 11.0
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,…
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arxiv
Score 10.5
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…
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arxiv
Score 10.5
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…
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- unreviewed
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arxiv
Score 10.5
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…
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arxiv
Score 10.5
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…
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- pending
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- unreviewed
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- now
huggingface
Score 10.5
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…
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- pending
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- unreviewed
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arxiv
Score 10.3
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 10.3
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…
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- pending
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- unreviewed
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- now
arxiv
Score 10.0
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…
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arxiv
Score 10.0
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…
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- pending
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- unreviewed
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huggingface
Score 10.0
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…
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- unreviewed
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arxiv
Score 10.0
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…
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- unreviewed
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arxiv
Score 10.0
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…
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- pending
- Role
- unreviewed
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arxiv
Score 9.8
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 …
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arxiv
Score 9.8
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…
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- pending
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arxiv
Score 9.5
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…
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- pending
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arxiv
Score 9.5
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…
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- pending
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- unreviewed
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arxiv
Score 9.5
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 9.5
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…
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- pending
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- unreviewed
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arxiv
Score 9.5
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…
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- pending
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- unreviewed
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- now
arxiv
Score 9.3
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…
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- pending
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- unreviewed
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- now
arxiv
Score 9.3
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 …
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- pending
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- unreviewed
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arxiv
Score 9.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 9.0
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 …
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- pending
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- unreviewed
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arxiv
Score 9.0
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…
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- pending
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- unreviewed
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arxiv
Score 9.0
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…
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- pending
- Role
- unreviewed
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- now
arxiv
Score 9.0
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…
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- pending
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arxiv
Score 8.8
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…
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arxiv
Score 8.5
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…
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arxiv
Score 8.5
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,…
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arxiv
Score 8.5
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 …
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arxiv
Score 8.5
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…
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arxiv
Score 8.3
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…
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arxiv
Score 7.5
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…
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arxiv
Score 7.5
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…
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arxiv
Score 7.5
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…
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arxiv
Score 7.5
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…
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arxiv
Score 7.3
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…
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arxiv
Score 7.0
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…
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arxiv
Score 7.0
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…
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arxiv
Score 7.0
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…
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arxiv
Score 7.0
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…
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arxiv
Score 7.0
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…
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arxiv
Score 6.5
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…
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arxiv
Score 6.5
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…
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arxiv
Score 6.5
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…
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arxiv
Score 6.0
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…
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