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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

Japanese

複数の連鎖の推論は、複数の連鎖の証拠が分散されるためだけでなく、異なる部分の推論が異なる相互作用のサポートチェーンを必要とするためにも困難な課題です。フラットリリーバは、単一の推論パスの早期選択が、他の事実が考慮された後に有用になる証拠を排除する一方で、フラットリリーバは、解釈のための基盤としてオリジナルのソースを保つ一方で、構造を用いて証拠を発見するために異なった、相互作用の連鎖を必要とする可能性があります。RESINは、問題を焦点を当てた1つのホップのニーズに分解し、各ホップのためのソースベースユニットの小さなセットを回収し、他の事実を考

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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