1 citations · 1 across the 4 of their papers we have counts for
5 papers
Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis +2
Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR)…
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…
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…
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.…
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…