Zhishang Xiang
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LegalGraphRAG introduces a multi-agent, hierarchical graph retrieval-augmented generation framework to overcome the limitations of traditional RAG in legal domains, achieving state-of-the-art reliable legal reasoning.
The paper proposes SAAS, a novel RL framework that equips LLM agents with self-awareness to precisely regulate search behavior, significantly mitigating costly over-search without sacrificing accuracy.
MemGraphRAG introduces a novel memory-based multi-agent system to construct globally consistent and structurally sound knowledge graphs, significantly improving retrieval-augmented generation for complex, large-scale corpora.
Proposed MemSyco-Bench benchmark for evaluating memory-induced sycophancy in agent systems, measuring when and how valid memories should be used.
Papers
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Zhishang Xiang, Zerui Chen, Yunbo Tang, Zhimin Wei +4 more
Proposed MemSyco-Bench benchmark for evaluating memory-induced sycophancy in agent systems, measuring when and how valid memories should be used.