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

We present BriLLM-MetaPred, the first engineering realization of the MetaPred primitive—posited by an AGI mathematical theory as both necessary and sufficient for general cognition—instantiated for language modeling. Rather than rederiving the underlying theory, this work reports its first full engineering instantiation for this task. MetaPred operates on a single, continuously evolving dynamical state that simultaneously serves as the context representation and the substrate from which predictions are read. It maintains internal coherence by iteratively measuring and reducing its own finite-depth inconsistencies, yielding endogenous, fixed-point-based self-supervision signals for training. This unified-state design intrinsically couples training and inference, obviating the need for separate pipelines. Consequently, the MetaPred paradigm exhibits symmetry across nearly all core machine-learning primitives—a hallmark of mathematically optimal solutions within the space of viable learning frameworks. Crucially, BriLLM-MetaPred is neither a conventional recurrent language model nor a scaled-up BriLLM variant. It constitutes a concrete bridge between two prior contributions: the structural requirements and theoretical motivation for MetaPred from the AGI theory, and the SiFu learning paradigm introduced in BriLLM zhao2025brillm. SiFu’s node-and-signal-flow design provides the executable substrate that enables MetaPred to run on commodity software stacks and existing hardware accelerators. Our implementation matches GPT-1’s language generation performance using only ∼1/10 of its parameters. This provides preliminary validation of the engineering feasibility of the MetaPred framework derived from first principles. BriLLM-MetaPred stands as the first machine-learning paradigm instantiated directly from rigorous mathematical derivation—and may well prove to be the last such theory-driven realization in the field.