Can a Dynamic Internal Field Govern a Transformer's Cognition? Certifiability, not Superiority, in Homeostatic Compute Control
cs.AI, cs.LG, cs.SY, eess.SY
Submitted: 2026-08-25
Updated: 2026-08-25
Comments: 22 pages, 4 figures. Companion paper: "Where Cognition Lives" (arXiv:2608.22347). Code, preregistrations and results: https://github.com/fmarrabal/miuracognitive
Code: https://github.com/fmarrabal/miuracognitive
License: http://creativecommons.org/licenses/by/4.0/
The gist: An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate.
Terminology
Abstract
An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate. Can that role be played by a dynamic internal field - a low-dimensional homeostatic state with explicit physics and certified stability - that modulates cognition without performing it? Ours is a field on the module graph governed by a family of PDEs on the graph Laplacian, advancing with an adaptive-depth reasoner. We certify the stability of the integrator of the whole family - an integrator certificate, not a closed-loop one. New, and proved here: a discrete Schur-Cohn criterion for Verlet with velocity coupling, necessary and sufficient per latent root, with no commutation hypothesis. The answer is threefold: substance no, structure only in part, certifiability yes. The type of the field's physics is irrelevant for accuracy: wave, diffusion, gated mixtures and a 2D Navier-Stokes substrate tie. A twenty-seed preregistered deconfounding campaign bounds the structural claim: at equalized caps the second-order effect is strong in one family (+0.087 [+0.042, +0.132], t=4.0) but is not detected in the other (+0.014 [-0.013, +0.040], n.s.), so part of the original contrast was capacity, not order; and a matched-interface GRU is indistinguishable in the first and nominally exceeds the field in the second (-0.035 [-0.067, -0.002]). What distinguishes the field is not capability but that its one-step operator admits an exact runtime stability check - a difference of kind, not of existence: learned recurrences carry certificates too, sufficient and conservative ones. A kill-gate with a positive control finds no evidence for the field as evidence accumulator (Delta AUC +0.0007 [-0.0065, +0.0079] vs a 0.03 threshold). A dynamic internal field is a viable, certifiable compute governor, but not an enhancer of cognition: it modulates, it does not think.
Sources
- Exploring Length Generalization in Large Language Models
- PonderNet: Learning to Ponder
- Universal Transformers
- Feature Transportation Improves Graph Neural Networks
- Looped Transformers for Length Generalization
- Looped Transformers as Programmable Computers
- Adaptive Computation Time for Recurrent Neural Networks
- Neural Turing Machines
- Length Generalization in Arithmetic Transformers
- Transformers Learn Shortcuts to Automata
- Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift
- The Illusion of State in State-Space Models
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
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