activity
20242026
most citedGeAR: Graph-enhanced Agent for Retrieval-augmented Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

From Version Conflicts to Decision Conflicts: Selective Revalidation for Long-Running AI Agents

Yongjian Lyu, Yang Ren, Ruofei Lai +1

Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified the action can change in the me…

cs.AI2026

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

Chiwang Luk, Matin Mohammad Najafi, Zhifeng Jia +6

Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long terminal outputs where usefu…

cs.AI2025

An Extensive Evaluation of PDDL Capabilities in off-the-shelf LLMs

Kaustubh Vyas, Damien Graux, Sébastien Montella +5

In recent advancements, large language models (LLMs) have exhibited proficiency in code generation and chain-of-thought reasoning, laying the groundwork for tackling automatic form…

cs.CL2024★ 1 cited

GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

Zhili Shen, Chenxin Diao, Pavlos Vougiouklis +12

Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios.…

cs.AI2024

From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle

Kaustubh Vyas, Damien Graux, Yijun Yang +8

In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for kn…