2 citations · 6 across the 37 of their papers we have counts for
9 papers · 1 filter
ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
Bolin Shen, Yushun Dong
In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repe…
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another
Wangshu Zhu, Qianwen Guo, Yushun Dong
LLM agents increasingly operate over multiple entities, where reliable response generation requires not only retrieving relevant evidence but also preserving its association with t…
HazardWeaver: Scientific Route Selection for Hazard Analysis Agents
Wangshu Zhu, Xueqi Cheng, Liang Wu +1
Understanding and assessing natural hazards is essential for disaster preparedness and risk reduction. Recent advances in large language models have spurred growing interest in AI…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Tianming Sha, Yue Zhao, Lichao Sun +1
Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but corre…
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Xiaopeng Yuan, Haibo Jin, Ye Yu +4
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottlen…
Self-Correction Can Amplify Hallucinations: Fact-Level Repair with Graph-Based Evidence Routing in Multimodal Generation
Kaixiang Zhao, Tianrun Yu, Shawn Huang +3
We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inference-time repair methods o…
EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4
Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, t…
LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer?
Xueqi Cheng, Yushun Dong
Multimodal large language models (MLLMs) have heterogeneous strengths across OCR, chart understanding, spatial reasoning, visual question answering, cost, and latency. Effective ML…
Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making
Xiaopeng Yuan, Xingjian Zhang, Ke Xu +5
Large language models (LLMs) are increasingly used for tasks that require complex reasoning. Most benchmarks focus on final outcomes but overlook the intermediate reasoning steps -…