Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization
Paper recorded by Signals 4 on 2026-09-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22
Category: cs.AI · 人工智能 · first seen 2026-09-22
Abstract
A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an approximation fitted to it. Such equivalence is necessary for exactness in and out of distribution, and extrapolation is governed by this exactness at inference, whatever its realization. Tensor Logic shows this: a zero-temperature contraction is
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Cite this page: Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization: the #11 most recent of 340 cs.AI papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/exactness-at-inference-a-representational-criterion-for-out-of-distribution-gene.html
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