PathwiseRAG: Multi-Dimensional Exploration and Integration Framework

ACL ARR 2025 May Submission4760 Authors

Abstract: Conventional retrieval-augmented generation (RAG) systems employ rigid retrieval strategies that create: (1) knowledge blind spots across domain boundaries, (2) reasoning fragmentation when processing interdependent concepts, and (3) contradictions from conflicting evidence sources. Motivated by these limitations, we introduce PathwiseRAG, which addresses these challenges through: intent-aware strategy selection to eliminate blind spots, dynamic reasoning networks that capture sub-problem interdependencies to overcome fragmentation, and parallel path exploration with adaptive refinement to resolve conflicts. The framework models query intent across semantic and reasoning dimensions, constructs a directed acyclic graph of interconnected sub-problems, and explores multiple reasoning trajectories while continuously adapting to emerging evidence. Evaluation across challenging benchmarks demonstrates significant improvements over state-of-the-art RAG systems, with average accuracy gains of 4.9\% and up to 6.9\% on complex queries, establishing a new paradigm for knowledge-intensive reasoning by transforming static retrieval into dynamic, multi-dimensional exploration.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: multihop QA,open-domain QA,reasoning,knowledge base QA,interpretability,generalization

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Theory

Languages Studied: English

Keywords: Retrieval-Augmented Generation, Reasoning, Large Language Models, Complex Question Answering

Submission Number: 4760