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Separability Is Not Alignability: Representations for Spectrum-to-Molecule Retrieval
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First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
Separability Is Not Alignability: Representations for Spectrum-to-Molecule Retrieval
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.37979
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.37979v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
Identifying a small molecule from its tandem mass spectrum is limited by paired data. Foundation models offer a way around this scarcity: pretrain a spectrum encoder on unlabeled spectra and a molecule encoder on a large corpus of structures, then align the two frozen representations, as in vision-language and as the Platonic Representation Hypothesis suggests. We audit this route on MassSpecGym with spectrum encoders retrained free of test-fold contamination. Alignment needs a metric relating distinct molecules, but the objectives that produce spectrum encoders never see molecular structure: we prove that some predictive objectives fix molecular identity without fixing that metric. Moreover, we find that a candidate-based objective rewards a spectrum-independent prior over the benchmark's decoys, which we isolate. What ultimately addresses data scarcity is not architectural: simulated spectra surpass frozen geometry.