A Unified Dynamical Field Theory of Learning, Inference, and Emergence

TL;DR

This paper introduces a unified dynamical field theory that models learning, inference, and emergence in biological and artificial systems through stochastic dynamical equations, emphasizing collective modes and time-scale organization.

Contribution

It develops a comprehensive theoretical framework unifying various models of cognition and neural architectures via a dynamical field approach with novel diagnostics like TDOS.

Findings

Inference corresponds to saddle-point trajectories.

Fluctuation-induced loop corrections lead to emergent collective modes.

Reorganization of the TDOS stabilizes slow modes for stable inference.

Abstract

Learning, inference, and emergence in biological and artificial systems are often studied within disparate theoretical frameworks, ranging from energy-based models to recurrent and attention-based architectures. Here we develop a unified dynamical field theory in which learning and inference are governed by a minimal stochastic dynamical equation admitting a Martin--Siggia--Rose--Janssen--de Dominicis formulation. Within this framework, inference corresponds to saddle-point trajectories of the associated action, while fluctuation-induced loop corrections render collective modes dynamically emergent and generate nontrivial dynamical time scales. A central result of this work is that cognitive function is controlled not by microscopic units or precise activity patterns, but by the collective organization of dynamical time scales. We introduce the \emph{time-scale density of states} (TDOS) as a compact diagnostic of the distribution of collective relaxation modes governing inference dynamics. Learning and homeostatic regulation are naturally interpreted as processes that reshape both the effective potential and the underlying state-space geometry, thereby reorganizing the TDOS and selectively stabilizing slow collective modes that support stable inference, memory, and context-dependent computation despite stochasticity and structural irregularity. This framework unifies energy-based models, recurrent neural networks, transformer architectures, and biologically motivated homeostatic dynamics within a single physical description, and provides a principled route toward understanding cognition as an emergent dynamical phenomenon.