11 papers
Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?
Jiale Liu, Huajun Xi, Shaokun Zhang +6
Automated failure attribution uses LLMs to identify where and why agentic systems fail. As agents become more capable, their failures become subtler, making automated attribution i…
OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration
Xinchen Zhang, Bowei Liu, Jiale Liu +7
Visual outcomes are increasingly central to multimodal large language models, making reliable and fine-grained verification essential for scaling generalist foundation models. In t…
TeamFusion: Supporting Open-ended Teamwork with Multi-Agent Systems
Jiale Liu, Victor S. Bursztyn, Lin Ai +4
In open-ended domains, teams must reconcile diverse viewpoints to produce strong deliverables. Answer aggregation approaches commonly used in closed domains are ill-suited to this…
SWAA: Sliding Window Attention Adaptation for Efficient and Quality Preserving Long Context Processing
Yijiong Yu, Jiale Liu, Qingyun Wu +2
The quadratic complexity of self attention in Transformer based LLMs renders long context inference prohibitively expensive. While Sliding Window Attention (SWA), the simplest spar…
Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs
Chi Zhang, Wenxuan Ding, Jiale Liu +3
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their robustness against textual misinform…
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang +4
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving stron…