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Constraint-Enhanced Physical Search through Correlation Matching

arXiv cs.AI · 2026-06-04 Cached

This paper proposes a principle of 'constraint-enhanced physical search' where temporal correlations in exploration are matched to constraint-induced spatial correlations in update dynamics, demonstrated via a tug-of-war bandit model. The authors show that efficient search emerges not from maximal randomness but from matching temporal correlation to the physical update scale that converts feedback into evidence.

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