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Xuanwang Zhang

3 accepted papers

2026

Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems

ICML 2026poster

The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration topology remains a critical point of fragility. To analyze this, we propose a Mean-Field Entropy Dynamics framework, modeling the orchestration proces…

Cited by 0SourceScholar
2026

TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them

ICLR 2026poster

The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundamental types of inconsistencies: (1) \textit{Score-Comparison Inconsistency}, where lower-rated responses outperform high…

Cited by 0SourcecodeScholar
2024

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

EMNLP 2024system demonstrations

Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck by equipping LLMs with…