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BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement — arXiv:2610.34018

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.34018
License (as archived): CC0-1.0
Archived: 2026-10-27T08:00:37.673Z
SHA-256 of the archived text: ed864d57c54849b04ac0c5680ae44b888defb06694f6191ff7005c70ce333246
Stable URL of this page: https://metraipolis.com/documento/dc_64ff7331a956

How to cite

BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement — arXiv:2610.34018 — arXiv (en), https://arxiv.org/abs/2610.34018 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_64ff7331a956 (Metraipolis archive dc_64ff7331a956, sha256 ed864d57c54849b04ac0c5680ae44b888defb06694f6191ff7005c70ce333246)

BibTeX

@misc{dc64ff7331a956,
  title = {BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement — arXiv:2610.34018},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.34018},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_64ff7331a956; sha256 ed864d57c54849b04ac0c5680ae44b888defb06694f6191ff7005c70ce333246},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34018. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present BriLLM-MetaPred, the first engineering implementation of the MetaPred machine-learning framework established by a mathematical theory of AGI, instantiated for language modeling. The theory argues that MetaPred is both necessary and sufficient as a machine-learning primitive for general cognition. Taking this theoretical conclusion as a design premise, we report an implementation of the primitive for language modeling. A single continuously evolving dynamical state serves as both the context representation and the state from which predictions are read out. Continuous measurement of its own state reduces finite-depth inconsistency, maintains internal consistency, and provides an endogenous dynamical self-supervision signal in fixed-point form. The single dynamical state of MetaPred intrinsically couples training and inference. MetaPred therefore exhibits symmetry across almost all key elements of machine learning, an intuitive and observable property of the mathematical optimum over the space of all machine-learning paradigms. BriLLM-MetaPred is neither another recurrent language model nor an extension of BriLLM. It provides a bridge between two prior contributions: the construction and motivation of the MetaPred primitive provided by AGI theory, and the SiFu learning paradigm provided by BriLLM (Zhao et al., 2025). The latter's node-signal-flow design enables the primitive to run on available software and hardware accelerators. Our implementation of BriLLM-MetaPred achieves predictive performance comparable to a GPT baseline of the same size. This provides initial evidence for the engineering feasibility of the MetaPred framework established by the mathematical theory of AGI. MetaPred is the first machine-learning paradigm derived entirely from mathematical theory rather than assembled and incrementally improved through human design---and perhaps the last such paradigm.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.34018 — 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 2026-10-27T07:55:30.870Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).