TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
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 t...
| Authors | Yuto Suzuki, Farnoush Banaei-Kashani |
|---|---|
| Primary category | cs.IR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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