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    <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.</abstract>
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%0 Conference Proceedings
%T PathwiseRAG: Multi-Dimensional Exploration and Integration Framework
%A Zhang, Hengrui
%A Huang, Pin-Siang
%A Zhang, Zhen
%A Lin, Peican
%A Yu, Yao-Ching
%A Hu, Bo
%A Du, Yulu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-pathwiserag
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1167/
%P 22915-22936

Markdown (Informal)

[PathwiseRAG: Multi-Dimensional Exploration and Integration Framework](https://aclanthology.org/2025.emnlp-main.1167/) (Zhang et al., EMNLP 2025)

• PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (Zhang et al., EMNLP 2025)

ACL

• Hengrui Zhang, Pin-Siang Huang, Zhen Zhang, Peican Lin, Yao-Ching Yu, Bo Hu, and Yulu Du. 2025. PathwiseRAG: Multi-Dimensional Exploration and Integration Framework. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22915–22936, Suzhou, China. Association for Computational Linguistics.