让AI学会从论文中提炼可迁移的科学原理，精准匹配研究目标。
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
- 基于目标条件抽象，从候选论文中提取针对性可迁移原理。
- 在ResearchBench上比MOOSE-Chem提升超10个百分点的命中率。
- 生成的抽象可解释性强，适合需要机制理解的研究者使用。
AI for Science中的科学假说生成通常包含科学灵感检索（SIR）与假说构建两步。现有SIR方法仅按主题相似性排序论文，未显式表征候选灵感如何迁移到目标问题，对远距离灵感尤其不力——其价值常在于可复用的问题求解原则而非主题重叠。受人类抽象并映射源问题到新目标的启发，我们提出将SIR重新定义为目标条件抽象（TCA）。检索对象是针对目标特定提取的可迁移抽象原则。本文提出TCA-SIR，学习生成目标条件抽象，并用其表示预测可迁移性。在ResearchBench上，TCA-SIR优于先前SIR方法和直接大模型检索，相较MOOSE-Chem在top4%命中率上提升超过10个百分点。学习得到的抽象比未训练的TCA提示更能清晰揭示目标相关机制，兼具更强检索性能与可解释性。
原文摘要 · Abstract (English)
Scientific hypothesis generation for AI for Science typically involves Scientific Inspiration Retrieval (SIR) followed by hypothesis composition. Existing SIR methods rank papers by topical similarity and do not explicitly represent how a candidate inspiration transfers to a target problem. This is especially limiting for remote inspirations, whose value often lies in reusable problem-solving principles rather than topical overlap. Motivated by how humans abstract transferable aspects of a source and remap them to a new target, we reformulate SIR as target-conditioned abstraction (TCA). The retrieval object is a transferable abstract principle extracted from a candidate specifically for the target. We present TCA-SIR, which learns to generate target-conditioned abstractions and uses their representations to predict transferability. On ResearchBench, TCA-SIR outperforms prior SIR methods and direct LLM retrieval, improving HitRate@top4% over MOOSE-Chem by more than 10 percentage points. Learned abstractions also recover target-relevant mechanisms more clearly than an untrained TCA prompt, yielding both stronger retrieval and an interpretable rationale for scientific inspiration.
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