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20242026
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cs.CL2026

Learning Transferable Topology Priors for Multi-Agent LLM Collaboration Across Domains

Taolin Zhang, Zijie Zhou, Jiuheng Wan +4

Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through structured communication. However,…

cs.CL2026

AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering

Taolin Zhang, Dongyang Li, Chen Chen +5

Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These diffic…

cs.CL2026

Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution

Taolin Zhang, Pukun Zhao, Qizhou Chen +5

Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall efficiency, existing methods o…

cs.CL2025

An Information-Theoretic Framework for Robust Large Language Model Editing

Qizhou Chen, Chengyu Wang, Taolin Zhang +1

Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdate…

cs.CL2025

UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models

Qizhou Chen, Dakan Wang, Taolin Zhang +4

Model editing aims to enhance the accuracy and reliability of large language models (LLMs) by efficiently adjusting their internal parameters. Currently, most LLM editing datasets…

cs.CL2025

QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs

Taolin Zhang, Haidong Kang, Dongyang Li +3

Recently, large language models (LLMs) have demonstrated impressive results but still suffer from hallucinations. Model editing has been proposed to correct factual inaccuracies in…