4 papers
Millions of -s: Extending GraphRAG to Millions of Documents
Zhili Shen, Chenxin Diao, Pascual Merita +2
Recent studies have explored graph-based approaches to retrieval-augmented generation, leveraging structured or semi-structured information -- such as entities and their relations…
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…