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When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence — arXiv:2610.20764

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20764
License (as archived): CC0-1.0
Archived: 2026-10-16T08:59:26.136Z
SHA-256 of the archived text: f3ccad60922db8122c97337d2f6812bb766128017c33448f646d84b46d7c65e7
Stable URL of this page: https://metraipolis.com/documento/dc_146dfd2d45fd

How to cite

When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence — arXiv:2610.20764 — arXiv (en), https://arxiv.org/abs/2610.20764 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_146dfd2d45fd (Metraipolis archive dc_146dfd2d45fd, sha256 f3ccad60922db8122c97337d2f6812bb766128017c33448f646d84b46d7c65e7)

BibTeX

@misc{dc146dfd2d45fd,
  title = {When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence — arXiv:2610.20764},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20764},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_146dfd2d45fd; sha256 f3ccad60922db8122c97337d2f6812bb766128017c33448f646d84b46d7c65e7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20764. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive, reason, and act? We formulate this as a theory- and model-agnostic challenge in which systems must infer reliable actions for increasingly distant future states across a growing observation--reality gap. We operationalize this challenge through the effective Speed of Coherent Cognition (\(S_C^{\mathrm{eff}}\)), defined as the highest tested Speed of Reality (\(S_R\)) through which a system continuously satisfies a predefined behavioural reliability criterion from baseline. As a proof of concept, reinforcement-learning agents are trained at the baseline \(S_R\) and evaluated, without retraining, as \(S_R\) is increased by up to \(50\times\) while computational resources remain fixed. We compare Transformer and gated-MLP baselines with \(Co^4\), an architecture inspired by cellular mechanisms implicated in conscious processing and awake thought. Across the tested scenarios, \(Co^4\) achieves substantially higher \(S_C^{\mathrm{eff}}\) than the baselines, motivating \(S_C^{\mathrm{eff}}\) as a new benchmark and the scaling of coherent cognition as a principle for reliable AI.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.20764 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:17.651Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).