Papers
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors
Chenghao Yang, Yuning Zhang, Zhoufutu Wen et al.
When Background Matters: Breaking Medical Vision Language Models by Transferable Attack
Akash Ghosh, Subhadip Baidya, Sriparna Saha et al.
When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation
Xunyi Jiang, Dingyi Chang, Julian McAuley et al.
When Benchmarks Leak: Inference-Time Decontamination for LLMs
Jianzhe Chai, YU Zhe, Jun Sakuma
When Bigger Isn’t Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation
Nannan Huang, Iffat Maab, Junichi Yamagishi
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Abeer Badawi, Elahe Rahimi, Md Tahmid Rahman Laskar et al.
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Boyu Xiao, Xiuqi Tian, Xuwen Song et al.
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
Yibo Peng, James Song, Lei Li et al.
When Does Auxiliary Modality Matter in Solving Geometric Problems? A Comprehensive Study of Textual, Formal, and Visual Modalities
Hyuk Namgoong, Jeesu Jung, Yerim Han et al.
When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models
Xuan Dong, Zhe Han, Tianhao Niu et al.
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
Tongyao Zhu, Huang Chao Ming, Min-Yen Kan
When Do Language Models Endorse Limitations on Human Rights Principles?
Keenan Samway, Miu Nicole Takagi, Rada Mihalcea et al.
When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents
Liang-Bo Ning, Yuchen Zhu, Heqing Huang et al.
When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models
Xiaoxiao Ma, Kuofeng Gao, Zeyi Lu et al.
When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student Abstract)
Ravada Satyadev, Aditya Ganesh Kumar, Avinash Anand et al.
When Eyes and Ears Disagree: Can MLLMs Discern Audio-Visual Confusion?
Qilang Ye, Wei Zeng, Meng Liu et al.
When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation
David Tan, Pinzhen Chen, Josef Van Genabith et al.
When Genes Speak: A Semantic-Guided Framework for Spatially Resolved Transcriptomics Data Clustering
Jiangkai Long, Yanran Zhu, Chang Tang et al.
When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation
Lin Sun, Wangdexian, Jingang Huang et al.
When High Accuracy Hides Poor Calibration: Rethinking Confidence Evaluation in Transformer-Based Text Classification with Balanced Brier Score
Guilherme Fonseca, Gabriel Prenassi, Washington Cunha et al.
When Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF
Yifan Xu, Xichen Ye, Yifan Chen et al.
When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning
Hyeong Kyu Choi, Jerry Zhu, Sharon Li
When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing
Anwar Alajmi, Gabriele Pergola
When Instinct Guides and Insight Grounds: Staged RL Training for LLM Agents
Zijing Zhang, Boning Zhang