Research Paper Cockpit

Daily Digest - 2026-09-10

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Latest digest: 2026-09-22.

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huggingface Score 21.4

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

2026-09-08 · Yuan Gao, Sebastian Müller, Mattia Piccinini, Marc Kaufeld, Yuchen Zhang, Finn Rasmus Schäfer, Qunying Song, Johannes Betz

General AI

Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by s…

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arxiv Score 21.2

ConvMem: Convolutional Memory for Long-Context Reasoning

2026-09-09 · Hongming Zhang, Zhaozhen Gu, Fengshuo Bai, Ming Hao, Qingyang Zhang, Yuanyuan Wang, Shiyang Tang, Yanna Wang, Bo Xu

General AI

While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory. However,…

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arxiv Score 20.2

From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning

2026-09-09 · Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou, Yan Cao, Xin Shen, Dongcai Lu, Yi Zhou

General AI

Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally handle visuo-linguistic inputs, they are often computationally intensive and opaque. We demonstrate that a pure Large Language Model (LLM), when…

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arxiv Score 19.2

Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning

2026-09-09 · Mingbo Yang, Wenqiang Wang, Zhaolu Kang, Peng Chen, Yannan Chen, Sunshang Wang, Yan Xiao

Research Track A · General AI

In-context learning (ICL) is widely used in multimodal large language models (MLLMs) and achieves strong performance across a wide range of multimodal tasks. However, existing multimodal ICL methods often rely on surface level imitation of in-context demonstrations, making it difficult for MLLMs to align their response…

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arxiv Score 18.2

What Should an Agent Forget? Separating What Is Stored from What Is Used

2026-09-09 · Yuhang Li, Yuchen Li

Research Track A · General AI

Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question. A superseded fact can mislead a current-state answer and still be essential for a historical query. We present RD-Forget, a training-free framework that separates what …

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arxiv Score 16.2

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs

2026-09-09 · Haiji Liang, Pengfei Zhou, Zhenglin Wan, Wei Wang, Yang You, Wangbo Zhao

General AI

Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing vision token pruning methods mitigate this overhead, they implicitly assume that a single fixed pruning strategy can be applied uniformly across all inputs. Our analys…

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arxiv Score 15.2

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

2026-09-09 · Zixiang Chen, Yuheng Lu, Zihao Cheng, Zeming Liu, Jizeng Bai, Ziye Huang, Zhiyin Lin, Zihan Li, Yuhang Guo, Yunhong Wang, Haifeng Wang

Research Track B · General AI

Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defin…

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arxiv Score 15.2

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

2026-09-09 · Killian Steunou, Yannis Tevissen, Mounîm A. El Yacoubi

General AI

Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong performance on captioning, question answering, retrieval and temporal grounding comes at a co…

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arxiv Score 14.2

From Retrieval to Weights: Parametric Individualization of Small Language Models with Individual Text Corpora

2026-09-09 · Christoph Wigbels, Ali Abusaleh, Markus T. Jansen, Alexander Mehler, Markus J. Hofmann

General AI

We approach a cognitive simulation perspective on episodic and semantic memory in multiple-choice question answering by incorporating text from individual text corpora (ITC) into retrieval-augmented generation and DoRA fine-tuning. We web-crawl the search histories of 515 participants who answered 36 multiple-choice kn…

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arxiv Score 14.2

Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs

2026-09-09 · Kostia Kudriavtsev, Parvez Rafi, Sha Sundaram

General AI

Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames tw…

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huggingface Score 14.0

AgenticGen: Reward-Guided Agentic Video Generation for Advertising

2026-08-31 · Xingyuan Bu, Chengru Song, Hao Zhou, Tao Zhou, Dong Li, Wei Li, Shilong Li, Hao Shi, Yongxin Guo, Donghao Zhou, Qiangpeng Yang, Shilei Wen

General AI

Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video foundation models can generate realistic clips from multimodal conditions, yet they do not optimize how a product should be transformed int…

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huggingface Score 13.4

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

2026-09-08 · Jaewon Chu, Jinwoo Seo, Jaewon Cho, Jeehye Na, Yunyang Xiong, Youngdae Kim, Hyunwoo J. Kim

General AI

Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language feedback have emerged a…

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arxiv Score 12.4

BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

2026-09-09 · Junfeng Xia, Wenhao Ye, Junxiang Zhang, Jiayu Zuo, Mo Wang, Quanying Liu

Research Track A

fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying …

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huggingface Score 11.4

SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents

2026-09-08 · Pujun Zheng, Zixin Shang, Shufan Jiang, Wenhui Tian, Dongsheng Zhu, Zerun Ma, Dingbo Yuan, Qi Zhang

General AI

SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: reward hacking, enabled by leakage of gold solutions or hidden evaluation information, an…

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arxiv Score 11.2

AVSRBench: A Multi-Condition AVSR Benchmark

2026-09-09 · Rishabh Jain, Naomi Harte

General AI

While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true generalization or just domain adaptation. To investigate this gap, we evaluate three AVSR architectures across six conditions: controlled broadcast speech, fixed-grammar u…

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arxiv Score 11.2

Avatar: Toward Autonomous End-to-End Orchestration of Scientific Workflows using LLMs

2026-09-09 · Suman Raj, Hai Duc Nguyen, Haochen Pan, Ryan Chard, Kyle Chard, Ian Foster

General AI

Scientific workflow management (WMSs) systems automate execution, yet orchestrate using fixed, hand-tuned rules. LLM agents promise more autonomous orchestration, but it remains unclear where to introduce agentic reasoning, how to bound its risk, and when it actually helps. We present Avatar, an actor-based architectur…

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arxiv Score 11.2

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

2026-09-09 · Yiling Ma, Yilun Zhao, Sihong Wu, Manasi Patwardhan, Arman Cohan

General AI

A research idea may be novel, coherent, and scientifically plausible, yet its proposed method may remain insufficiently specified for faithful implementation. We study the codification readiness of implementation-facing research-method specifications, defined by whether they provide sufficient methodological informatio…

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arxiv Score 11.2

Programmable World Model

2026-09-09 · Zheng-Hui Huang, Guixu Lin, Jiacheng Lin, Yi-Chuan Huang, Ruihan Yu, Muyao Niu, Siqi Yang, Yu-Lun Liu, Yung-Yu Chuang, Kaipeng Zhang, Zhixiang Wang

General AI

Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent world state and enforcing programmable rules over extended interactions. We introduce Programmable World Model, a framework that decouples world-state evolution from visu…

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arxiv Score 11.2

Retrofitting Code Using LLMs to Support Exceptional Behavior

2026-09-09 · Linghan Zhong, Jiyang Zhang, Jayanth Srinivasa, Junyi Jessy Li, Milos Gligoric

General AI

Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is an essential component of software systems, allowing developers to detect and handle exceptional states that deviate from the expected program behavior. However, manually…

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arxiv Score 11.2

Towards Scalable and Cost-Efficient Vulnerability Detection: A Study on Automatic Query Generation

2026-09-09 · Ivana Clairine Irsan, Ratnadira Widyasari, Huihui Huang, Ting Zhang, Yue Liu, Ouh Eng Lieh, Shar Lwin Khin, Kang Hong Jin, David Lo

General AI

Static analysis remains a cornerstone of software security, yet the effectiveness of tools such as CodeQL is often limited by the substantial manual effort required to develop high-coverage query suites. While large language models (LLMs) have emerged as a potential solution for automated code reasoning, their practica…

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arxiv Score 11.2

Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

2026-09-09 · Yiwei Fang, Yichen Liu, Ze Jin, Haoqiang Wang, Qixu Liu, Luyi Xing

General AI

Logic flaws pose significant challenges in the design and implementation of modern, semantically rich systems and applications, impacting security, privacy, and trust. These flaws are inherently tied to business-specific semantics and threat models, making their discovery and reasoning difficult and hard to scale. Real…

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huggingface Score 11.0

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

2026-09-04 · Ji Soo Lee, Xilun Chen, Pierce Chuang, Ashish Shenoy, Jason Wei, Dohwan Ko, Hyunwoo J. Kim, Benoit Corda

General AI

Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-choice questions const…

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huggingface Score 11.0

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

2026-09-06 · Yubin Wang, Xingjian Wei, Jiang Wu, Yinfan Wang, Boyu Zhu, Lin Zhang, Jianing Yu, Huazheng Zeng, Ruiyi Ding, Junyuan Gao, Jiaxing Sun, Lingli Ge, Haote Yang, Jingchao Wang, Aijia Guo, Qian Jiang, Yurui Zhao, Wenjian Zhang, Chen Zhu, Lijun Wu, Xiaolei Yang, Haodong Chen, Junjie Yuan, Zichao Ye, Shaowei Hou, Jing Ye, Jia Yu, Shan Wang, Jiantao Qiu, Chao Xu, Yuqiang Li, Guangyu Wang, Bowen Zhou, Dahua Lin, Conghui He

General AI

High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform built via a full…

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arxiv Score 10.2

Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response

2026-09-09 · Caden Chandra, Jerry Ng

General AI

This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable and effective behaviors over time, as demonstrated by converging loss trends, improved reward signals,…

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arxiv Score 10.2

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

2026-09-09 · Ayush Debnath, Ruelia Saha, Sudip Misra

General AI

Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial…

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arxiv Score 9.9

Training Trajectories Determine Circuit Removability in Annealable Soft-Prior Transformers

2026-09-09 · Zonglin Yang, Ziming Zhao, Wei Tang, Xunyu Jiang, Yihong Liu, Tailin Chen, Zifu Yu, Jiayu Liu

Research Track A · General AI

Soft positional priors can help small Transformers learn retrieval circuits, but it is unclear whether the resulting circuits remain functional once the prior is removed. We test this with an annealable soft-prior Transformer whose attention biases can be learned, faded, or zeroed during training and evaluation. On ass…

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arxiv Score 9.4

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

2026-09-09 · Ravi Ranjan, Olivera Kotevska, Agoritsa Polyzou

Research Track A · General AI

Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machine unlearning offers a practical alternative to full retraining, but many existing methods apply broad or fixed parameter updates that can de…

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arxiv Score 9.2

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

2026-09-09 · Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Berard, Thomas Euyang, Marzieh Fadaee, Julia Kreutzer

General AI

Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in English regardless of the language they are prompted in. This is inaccessible for non-English-speaking users, risks losing the intent of the or…

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arxiv Score 9.2

Isotropic Embedding Perturbations for Robust Vision Language Encoders

2026-09-09 · Hyesong Choi, Daeun Kim, Song Park, Taekyung Kim, Byeongho Heo, Sangdoo Yun, Dongbo Min, Dongyoon Han

General AI

Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer strong regularization effects, their combined use has saturated in performance due to overlapping functionalities, and aggressive pixel-level ma…

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arxiv Score 9.2

On-Policy Distillation for Vision-Language Model Adaptation, an Effective Paradigm on Low-Quality Multimodal Data

2026-09-09 · Hongyuan Zhang, Xianda Guo, Yanlun Peng, Qianlong Yang, Yubin Guo, Pinhan Fu, Mulin Chen, Xiaozhen Qiao, Ping Luo

General AI

Knowledge distillation offers an efficient route to transfer a task-adapted vision-language teacher to a compact student. The training target in current vision-language distillation methods is typically constructed from the teacher prediction and applied uniformly to all training samples, making it unreliable under cla…

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arxiv Score 8.4

How an Economy Shrinks in Space: Concavity-on-Jobs and Upward Consolidation under Demographic Decline

2026-09-09 · Tomoya Mori, Miki Ogawa

Research Track A

When a country's population declines, the aggregate economy appears to contract on the intensive margin: industrial diversity intact, every industry a little smaller. At the regional level, contraction is uneven and takes the extensive form: entire industries disappear, one after another. The relevant unit is the city:…

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arxiv Score 8.2

Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework

2026-09-09 · Md. Masudul Islam, Mirza Niaz Morshed, Md. Shafiqul Islam

General AI

AI literacy provides foundational competencies that support ethical, transparent, and sustainable technological development, although higher-order capabilities such as governance, critical evaluation, and strategic decision-making extend beyond basic literacy into advanced levels of AI competency. This study positions …

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arxiv Score 8.2

Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

2026-09-09 · Siddharth Gupta, Jitin Singla

General AI

In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a primary segmentation model and an independently trained referee on the same image.…

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arxiv Score 8.2

Show-Harness: Just a VLM Agent Can Play Robots

2026-09-09 · Yanzhe Chen, Zechen Bai, Zhijun Cao, Wenzheng Zeng, Kevin Qinghong Lin, Yiqi Lin, Guoqiang Liang, Kevin Yuchen Ma, Qiming Huang, Mike Zheng Shou

General AI

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness expos…

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huggingface Score 8.0

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

2026-09-05 · Andrej Andrejev

General AI

Large language models used for code editing can be trained and deployed in at least two output regimes: direct generation, where the model emits the entire modified file in one shot, and iterative diff-based generation ("steps"), where the model emits a sequence of localized search/replace edits applied one at a time u…

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huggingface Score 8.0

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

2026-09-07 · Jingjie Ning, Shanshan Zhong, Xiaochuan Li, Ji Zeng

General AI

AI research agents combine prior knowledge, public sources, and experimental feedback to produce useful results. The Discovery Certification Protocol (DCP) turns claims about these results into executable recovery and feedback tests. Gate 1 validates useful improvement on sealed evaluation. Gate 2 gives matched agents …

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huggingface Score 7.4

RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

2026-09-09 · Haichuan Hu, Yang Xiao, Mingni Tang, Jiawen Duan, Quanjun Zhang, Congqing He, Hao Zhang, Jiashuo Wang, Johan F. Hoorn, Wenjie Li

General AI

Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios underexplored. In this work, we introduce relation-aware emotional support conversation, a new task that evaluates whether LL…

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huggingface Score 7.4

Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States

2026-09-09 · Marek Jeliński, Jan Dubiński, Maciej Chrabaszcz, Sebastian Cygert

General AI

Existing bias auditing methods typically rely on model outputs, requiring costly benchmarks or judge models and potentially missing internal shifts that never appear in generated text. We propose a reference-based method that audits bias in hidden-state representations across related model variants, for example before …

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arxiv Score 7.2

Monitoring Hierarchies in Knowledge Production

2026-09-09 · Shota Ichihashi, Fei Li, Dihan Zou

General AI

We study peer monitoring design in knowledge production. A principal leads agents who work on different but related tasks. By working on their own tasks, agents acquire information that is useful for evaluating their peers' performance. We consider robust contracts under which effort by all agents is the unique rationa…

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huggingface Score 7.0

Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation

2026-08-31 · Vida Adeli, Soroush Mehraban, Jacob Rommann, Harrison Sanborn, Cole Clifford, Babak Taati

General AI

Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging. Prior speech-driven gesture models emphasize audio-gesture alignment but do not explicitly account for posture constraints or surrounding objects, failing to capture t…

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huggingface Score 7.0

DF26: We Cannot Tell Fake From Real Anymore

2026-09-07 · Severyn Shykula, Andrii Yermakov, Ivan Samarskyi, Dmytro Mishkin, Jan Cech, Anastasiia Mishchuk

General AI

We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-video models. The videos capture single-person public-speaking scenarios, spanning direct-to-camera recordings, official statements, and studio interviews - 271 real and …

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arxiv Score 6.7

Private communication via zero-private-capacity quantum channels

2026-09-09 · Chengkai Zhu, Xin Wang

General AI

Private communication over a noisy quantum channel requires reliable transmission to the receiver and secrecy from the environment. Whether two channels with zero private capacity can jointly enable private communication is a longstanding open problem in quantum information theory. Here we resolve this problem by exhib…

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arxiv Score 6.2

A Confidence-Aware Multimodal Fusion Framework for Industrial Human-Robot Collaboration

2026-09-09 · Xinyu Liu, Qiqi Dong, Boya Jia, Yi Zhang, Binbin Lian

General AI

A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboration. This framework fuses four heterogeneous modalities including object 6D pose, gaze, skeletal motion and IMU-based hand motion. It embeds a confidence-trend-driven dyn…

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arxiv Score 6.2

DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation

2026-09-09 · Nisarga Nilavadi, Ralf Römer, Moritz Reuss, Michael Krawez, Tobias Jülg, Angela P. Schoellig, Rudolf Lioutikov, Wolfram Burgard

General AI

Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are unreliable for full 7-DoF end-effector control. To address this gap, we introduce DUET-DINO, a simu…

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arxiv Score 6.2

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

2026-09-09 · Jonathan A. Handler, Marlene I. Robles-Granda, Jacob E. Mefford, Jeremy S. McGarvey, Gregory S. Podolej, Colleen J. Klein, Matthew D. Dalstrom, William F. Bond

General AI

Background: Emergency Department (ED) return visits are commonly reviewed for quality assurance, but are often limited (e.g., to revisits within 48-72 hours) to increase actionable finding yield while minimizing chart review burden. Those limitations may lead to missed quality improvement opportunities. Methods: We con…

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arxiv Score 5.2

Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization

2026-09-08 · Nour Jamoussi, Marios Kountouris

General AI

Divergence-based regularization and Sharpness-Aware Minimization (SAM) are two prominent approaches for improving generalization in deep learning, both motivated by robustness to perturbations. However, their relationship has remained largely unexplored. Building on classical second-order expansions of $f$-divergences,…

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