178 citations · 186 across the 17 of their papers we have counts for
7 papers · 1 filter
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks
Ziyi Liu, Tzu-Sheng Kuo, Jiale Liu +3
As large language models are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing diverse group dynamics across…
MGAL: A Multilingual Granularity-Aware Long-Context Benchmark
Chunhan Li, Chenglin Xu, Zongyang Zhang +8
Evaluation of long-context Large Language Models (LLMs) has advanced rapidly. However, most existing benchmarks are limited to the document level and focus mainly on high-resource…
Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
Jiale Liu, Huan Wang, Weicheng Wang +3
Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and…
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…
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…