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Home/Authors/Yunbo Tang

Yunbo Tang

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

AI×3Info Retrieval×2Multiagent×1NLP×1ML×1

Frequent co-authors

Zhishang Xiang3×
Zerui Chen3×
Qinggang Zhang3×
Jinsong Su3×
Zhimin Wei1×
Ruqin Ning1×

Research Timeline

2026
SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search

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: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

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.

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

Proposed MemSyco-Bench benchmark for evaluating memory-induced sycophancy in agent systems, measuring when and how valid memories should be used.

Highlighted terms show continued research focus across papers

Papers

cs.IRcs.AIEmpiricalRecentJul 1, 2026

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.

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cs.IRcs.AIcs.MARecent
May 30, 2026

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen +2 more

MemGraphRAG introduces a novel memory-based multi-agent system to construct globally consistent and structurally sound knowledge graphs, significantly improving retrieval-augmented generation for comp…

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cs.AIcs.CLcs.LGRecentMay 28, 2026

SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search

Yunbo Tang, Chengyi Yang, Shiyu Liu, Zhishang Xiang +3 more

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…

View →