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RESIN: Multi-Chain Reasoning with Entity-Structured Indexing

Xuanyu Meng, Jash Rajesh Parekh, Jiawei Han · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26782 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

多链推理是一个具有挑战性的任务,不仅是因为多链证据分散,而且是因为推理的不同部分可能需要不同的,相互作用的支持链。平板检索可以淹没一个模型与松散相关的文本,而早期选择单一推理路径可以丢弃的证据,只有在其他事实被考虑后才有用。我们引入RESIN(实体结构化索引有效推理推理推理),该缓冲使用结构来找到证据,同时保持原始来源作为推理的基础。RESIN将问题分解为集中的一步需求,检索一个小组的源基础单位,每个希望,并只将这些相关的证据放在一个压缩理由缓冲中。缓冲器保留替代支持,直到最终组成,使中间决策明确而不为破碎的路径。在完整

Machine translation (Meta M2M100); the English original follows.

Abstract

Multi-chain reasoning is a challenging task not only because the multi-chain evidence is dispersed, but also because different parts of reasoning may require distinct, interacting chains of support. Flat retrieval can flood a model with loosely related text, whereas early selection of a single reasoning path can discard evidence that becomes useful only after other facts are considered. We introduce RESIN (Reasoning with Entity Structured Indexing), which uses structure to locate evidence while keeping the original source as the basis for reasoning. RESIN decomposes a question into focused one-hop needs, retrieves a small set of source-grounded units for each hop, and places only this relevant evidence in a compact reasoning buffer. The buffer preserves alternative supports until final composition, making intermediate decisions explicit without forcing a brittle path. On the full HolisQA benchmark, RESIN achieves 82.92 context recall and 76.70 answer relevancy over all questions, with higher reported CR and AR across five domains and higher F1 in four domains than the published baselines. Ablations show that both typed evidence diversity and hop-wise decomposition are essential, supporting a clear conclusion: Precise evidence organization and delayed commitment make multi-chain reasoning effective and auditable. Our code is available at https://github.com/RamonMeng/RESIN.

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Topics: Computer science, Context (archaeology), Search engine indexing, Task (project management), Set (abstract data type), Recall

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