aiqbench analysis
This research proposes a principle of constraint-enhanced physical search where matching temporal exploration correlations to spatial constraint dynamics improves efficiency beyond standard randomness. By leveraging conservation laws to convert local observations into differential evidence, the study identifies the update-noise-to-contrast ratio as the critical parameter governing search limits. The findings suggest a general organizing principle for physical systems where efficient exploration emerges from aligning structured spatiotemporal correlations with update scales.
Constraint-Enhanced Physical Search through Correlation Matching
arXiv:2606.03554v1 Announce Type: cross Abstract: Physical systems do not merely add noise to search processes; they impose constraints that generate structured correlations. We propose a principle of constraint-enhanced physical search in which temporal correlations in exploration are matched to constraint-induced spatial correlations in the update dynamics. Using a minimal tug-of-war bandit model (TOW), we show that a conservation law converts local observations into differential evidence across alternatives, while a temporally correlated drive controls the order of exploration. Search efficiency is improved not by stronger randomness or by maximal anti-correlation, but by matching the temporal correlation to the physical update scale that converts feedback into evidence. A scaling estimate identifies the update-noise-to-contrast ratio as the leading parameter that limits how strongly temporal anti-correlation can be used. The results suggest a general organizing principle for physical search: constraints and fluctuations can generate structured spatiotemporal correlations, and efficient exploration emerges when these correlations are matched to the update dynamics.
Abstract:Physical systems do not merely add noise to search processes; they impose constraints that generate structured correlations. We propose a principle of constraint-enhanced physical search in which temporal correlations in exploration are matched to constraint-induced spatial correlations in the update dynamics. Using a minimal tug-of-war bandit model (TOW), we show that a conservation law converts local observations into differential evidence across alternatives, while a temporally correlated drive controls the order of exploration. Search efficiency is improved not by stronger randomness or by maximal anti-correlation, but by matching the temporal correlation to the physical update scale that converts feedback into evidence. A scaling estimate identifies the update-noise-to-contrast ratio as the leading parameter that limits how strongly temporal anti-correlation can be used. The results suggest a general organizing principle for physical search: constraints and fluctuations can generate structured spatiotemporal correlations, and efficient exploration emerges when these correlations are matched to the update dynamics.
Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO); Computational Physics (physics.comp-ph)
arXiv:2606.03554 [cond-mat.stat-mech]
(or arXiv:2606.03554v1 [cond-mat.stat-mech] for this version)
From: Song-Ju Kim Dr. [view email] [v1] Tue, 2 Jun 2026 12:15:21 UTC (2,200 KB)
Full text under the publisher's open licence · 188 words