HyBIRD: 双曲桥检索与诊断用于方法论灵感检索
HyBIRD: Hyperbolic Bridge Retrieval and Diagnosis for Methodology Inspiration Retrieval
AI总结：
提出HyBIRD框架，将方法论灵感检索建模为双曲桥检索，通过轻量级双曲桥变体保持密集检索性能，并利用LLM辅助方法块进行事后解释和证据选择，实现可检查的查询需求分析。
AI中文摘要：
方法论灵感检索（MIR）要求系统检索出那些方法能够启发新研究提案的先前论文。与一般科学检索不同，核心挑战不在于主题相似性，而在于候选论文是否提供能够实例化抽象方法论需求的具体机制。现有的MIR密集检索器提供了强大的论文级排名，但返回的列表并未揭示提案需求如何被检索到的方法所桥接、证据薄弱之处，或哪些互补片段可能有所帮助。我们提出HyBIRD，一个冻结锚点框架，将MIR视为双曲桥检索和事后方法诊断。HyBIRD保持强大的MIR密集检索器固定，学习轻量级的点、锥和分解双曲桥变体，并利用LLM辅助方法块进行事后解释和证据选择。在MIR基准上，分解桥在保持密集锚点强检索行为的同时达到了59.034 mAP。更重要的是，HyBIRD将排名论文转换为可检查的查询需求概况、因子覆盖、成熟度视图和互补证据包。结果表明，双曲几何最有用的是作为密集锚点上的校准结构，而不是作为密集检索的独立替代品。
英文摘要：
Methodology Inspiration Retrieval (MIR) asks a system to retrieve prior papers whose methods can inspire a new research proposal. Unlike general scientific retrieval, the central challenge is not topical similarity but whether a candidate paper provides concrete mechanisms that can instantiate an abstract methodological need. Existing MIR dense retrievers provide strong paper-level rankings, but the returned lists do not expose how proposal needs are bridged by retrieved methods, where evidence is weak, or which complementary snippets may help. We propose HyBIRD, a frozen-anchor framework that treats MIR as hyperbolic bridge retrieval and post-hoc method diagnosis. HyBIRD keeps a strong MIR dense retriever fixed, learns lightweight point, cone, and factorized hyperbolic bridge variants, and uses LLM-assisted method blocks for post-hoc explanation and evidence selection. On the MIR benchmark, the factorized bridge reaches 59.034 mAP while preserving the dense anchor's strong retrieval behavior. More importantly, HyBIRD converts ranked papers into inspectable query need profiles, factor coverage, maturity views, and complementary evidence bundles. The results suggest that hyperbolic geometry is most useful as calibrated structure over a dense anchor, rather than as a standalone replacement for dense retrieval.
