Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks
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Computer Science > Machine Learning
Title:Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks
Abstract:Scientific discovery saturates when new hypotheses cease to provide independent information, even if the nominal hypothesis space remains large. We study hybrid discovery systems that combine structured local search with LLM-generated non-local proposals and pose the Search Compression Hypothesis: non-local exploration helps only when three geometric conditions co-occur: spectral compression, orthogonal escape from the explored span, and residual signal alignment with the target. We formalize these conditions, derive necessary conditions for hybrid advantage, and test the mechanism in controlled synthetic environments, large-scale A-share factor discovery, and symbolic-regression benchmarks; a public tabular operational sanity check tests the associated budget-allocation implication. Signal-planting and directed-versus-random experiments show that novelty alone is insufficient: random orthogonal jumps expand coverage but do not improve yield without predictive alignment. Across compression sweeps, real factor archives, and LLM-SRBench tasks, hybrid gains concentrate in weakly represented but target-bearing directions and vanish as the hypothesis space approaches full rank. The framework turns LLM-guided discovery from generic novelty search into a diagnostic procedure for deciding when directed non-local exploration is warranted.
| Comments: | 23 pages, 1 figure, 27 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM) |
| Cite as: | arXiv:2606.14386 [cs.LG] |
| (or arXiv:2606.14386v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14386
arXiv-issued DOI via DataCite (pending registration)
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