most citedWhen to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation

4 citations · 4 across the 12 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2026

Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy

Ruqing Ning, Haibo Meng, Zhishang Xiang +4

Long-term memory enables LLM-based agents to retain and reuse information across tasks and sessions, supporting personalization and long-horizon interactions. However, persistent m…

cs.AI2026

CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning

Bo Zeng, Linfeng Gao, Peiqin Lin +9

Multimodal language models achieve near-ceiling scores on food recognition benchmarks, yet it remains unclear whether this success reflects genuine cultural understanding or mere v…

cs.AI2026

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

Yunbo Tang, Chengyi Yang, Shiyu Liu +4

Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a criti…

cs.AI2026

Graph-based Agent Memory: Taxonomy, Techniques, and Applications

Chang Yang, Chuang Zhou, Yilin Xiao +15

Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…

cs.AI2026

BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search

Shiyu Liu, Yongjing Yin, Jianhao Yan +7

RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies op…