arXiv cs.AI· Junyuan Liu, Hongyue Lin, Jiale Hong, Yi Zhao, Hai Zhao·· 13 小时前AI 评分22
BriLLM-MetaPred：融合表示与预测的自测量语言模型
BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement
AI 导读
BriLLM-MetaPred 是 MetaPred 机器学习框架的首个工程实现，该框架由一套 AGI 数学理论推导得出，用于语言建模。模型用单一持续演化的动力学状态同时充当上下文表示与预测读出状态，通过持续自测量降低有限深度不一致性，并提供定点形式的内生自监督信号。其预测性能与同等规模的 GPT 基线相当，为 MetaPred 框架的工程可行性提供了初步证据。
正文 · 原文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34018 [cs.LG] |
| (or arXiv:2610.34018v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34018 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yi Zhao [view email]
[v1]
Sun, 25 Oct 2026 03:08:17 UTC (3,948 KB)
来源：arXiv cs.AI · arxiv.org