STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering

2026-04-19

AI Summary

This work addresses the limitations of existing multi-hop question answering methods, which are prone to lexical ambiguity and often neglect the logical dependencies among reasoning steps, leading to incoherent inference. To overcome these issues, the authors propose the STRIDE framework, which decouples reasoning structure planning from entity grounding: a meta-planner first generates an abstract, entity-agnostic reasoning skeleton, and a dependency-aware supervision module dynamically schedules subtasks, adaptively selecting between retrieval and reasoning while integrating cross-branch information to enhance robustness. Furthermore, they introduce STRIDE-FT, a modular fine-tuning approach that requires no human annotations and leverages self-generated reasoning trajectories to optimize individual components. Experimental results demonstrate that STRIDE substantially improves multi-hop QA accuracy, and STRIDE-FT effectively enhances the reasoning capabilities of open-source large language models.

Technology Category

• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multi-hop question answering (MHQA) enables accurate answers to complex queries by retrieving and reasoning over evidence dispersed across multiple documents. Existing MHQA approaches mainly rely on iterative retrieval-augmented generation, which suffer from the following two major issues. 1) Existing methods prematurely commit to surface-level entities rather than underlying reasoning structures, making question decomposition highly vulnerable to lexical ambiguity. 2) Existing methods overlook the logical dependencies among reasoning steps, resulting in uncoordinated execution. To address these issues, we propose STRIDE, a framework that separates strategic planning, dynamic control, and grounded execution. At its core, a Meta-Planner first constructs an entity-agnostic reasoning skeleton to capture the abstract logic of the query, thereby deferring entity grounding until after the reasoning structure is established, which mitigates disambiguation errors caused by premature lexical commitment. A Supervisor then orchestrates sub-question execution in a dependency-aware manner, enabling efficient parallelization where possible and sequential coordination when necessary. By dynamically deciding whether to retrieve new evidence or infer from existing facts, it avoids redundant queries and error propagation, while fusing cross-branch information and reformulating failed queries to enhance robustness. Grounded fact extraction and logical inference are delegated to specialized execution modules, ensuring faithfulness through explicit separation of retrieval and reasoning. We further propose STRIDE-FT, a modular fine-tuning framework that uses self-generated execution trajectories from STRIDE, requiring neither human annotations nor stronger teacher models. Experiments show that STRIDE achieves robust and accurate reasoning, while STRIDE-FT effectively enhances open-source LLMs.

Problem

• multi-hop question answering
• retrieval-augmented generation
• lexical ambiguity
• reasoning structure
• logical dependencies

Innovation

• reasoning skeleton
• dependency-aware execution
• entity-agnostic planning
• modular fine-tuning
• retrieval-reasoning separation