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The Free Inference Dimension: Complexity Measure for Zero-Collision Navigation under Hypothesis Mixtures

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Solomonoff induction frames prediction as a mixture over computable hypotheses, typically leading to identification of the true environment. In a finite meta-reinforcement learning setting with nested constraint families, in our previous work, we observe a different regime: a value-mixture (VM) agent achieves near-optimal, zero-collision navigation without identifying the true environment, a phenomenon we call Free Inference. This regime persists up to a sharp density threshold, beyond which performance degrades and posterior-mode selection (PMS) becomes preferable. We formalize this behavior

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