{
  "id": 9081599,
  "title": "Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization",
  "url": "https://urgent.news/2026/09/21/exactness-at-inference-a-representational-criterion-for-out-of",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T17:39:51.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.24942v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}