ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA

ACL ARR 2025 May Submission2050 Authors

Abstract:

Multi-hop question answering (QA) remains challenging, as solutions must reliably integrate and reconcile evidence from multiple sources without succumbing to error propagation. While large language models (LLMs) have achieved substantial improvements via chain-of-thought (CoT) prompting and retrieval-augmented generation, these methods typically adopt a forward-only workflow—early mistakes persist throughout inference, and contradictions discovered later cannot systematically trigger re-evaluation.
To address this limitation, we present ReAgent, a reversible multi-agent reasoning framework. Specifically, ReAgent enables agents to backtrack to earlier valid states when conflicts arise, thereby isolating and rectifying flawed assumptions before they undermine subsequent reasoning. Our approach combines explicit local and global rollback protocols with modular role specialization, resulting in a flexible and error-tolerant pipeline. Empirical evaluation on three multi-hop QA benchmarks demonstrates consistent performance gains of approximately 6% over forward-only baselines, in addition to enhanced interpretability. These findings highlight the value of non-monotonic, backtracking-driven inference in complex QA scenarios and point to broader implications for multi-agent collaboration in knowledge-intensive tasks.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: agentic reasoning, answer generation, LLMs, knowledge augmented QA

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Keywords: agentic reasoning, answer generation, LLMs, knowledge augmented QA

Submission Number: 2050