Co-Evolving Graph and Text Memory for Training-Free Multi-Hop Question Answering

2026-07-25

AI Summary

This work addresses the challenge of effectively integrating knowledge graphs and textual evidence for multi-hop question answering. To this end, it proposes a training-free, open architecture that dynamically coordinates structured relational knowledge and unstructured contextual information through a synchronous bidirectional graph-text working memory mechanism. The core innovation lies in a co-evolutionary process between graph and text memories, which enables continuous alignment and mutual enhancement during both retrieval and generation stages. This is achieved via synchronized recurrent integration, relation triple extraction, and graph fact injection strategies. Evaluated on six mainstream multi-hop QA benchmarks, the method substantially outperforms existing training-free baselines and achieves performance comparable to larger-scale or trainable systems.

Abstract

Multi-hop question answering requires coordinating relational and textual evidence across reasoning steps, a combination neither a text corpus nor a knowledge graph can supply alone. Prior work often emphasizes only part of this loop: graph-augmented RAG retrieves from a pre-built or query-updated graph, KGQA systems search within topic-centered subgraphs, and memory-augmented agents maintain evolving memories without continuously reconciling graph memory with textual context. We propose Co-E, a training-free system built around synchronized bidirectional graph-text working memory. A synchronization cycle consolidates textual memory, extracts relational triples into graph memory, and injects graph facts back into the generation context. Because both memories are maintained, they shape subsequent retrieval and generation. Evaluated on six multi-hop QA benchmarks, Co-E improves over comparable training-free open-backbone baselines and is competitive with larger or trained systems.

Problem

• multi-hop question answering
• graph-text memory
• training-free
• knowledge graph
• textual context

Innovation

• training-free
• graph-text memory
• multi-hop QA
• synchronized bidirectional memory
• co-evolving memory