AI RESEARCH
Constraint-Enhanced Physical Search through Correlation Matching
arXiv CS.AI
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ArXi:2606.03554v1 Announce Type: cross 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.