Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems

TL;DR

A dynamical framework for learning without an explicit objective is proposed that evaluates the intrinsic health of its own internal dynamics and regulates structural plasticity accordingly, and suggests a possible route toward autonomous learning systems capable of self-assessment and internally regulated structural reorganization.

Abstract

Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has been extraordinarily successful for well-defined tasks where goals are fixed and evaluation criteria are explicit. However, if artificial systems are to move toward true autonomy-operating over long horizons and across evolving contexts-objectives may become ill-defined, shifting, or entirely absent. In such settings, a fundamental question emerges: in the absence of an explicit objective function, how can a system determine whether its ongoing internal dynamics are productive or pathological? And how should it regulate structural change without external supervision? In this work, we propose a dynamical framework for learning without an explicit objective. Instead of minimizing external error signals, the system evaluates the intrinsic health of its own internal dynamics and regulates structural plasticity accordingly. We introduce a two-timescale architecture that separates fast state evolution from slow structural adaptation, coupled through an internally generated stress variable that accumulates evidence of persistent dynamical dysfunction. Structural modification is then triggered not continuously, but as a state-dependent event. Through a minimal toy model, we demonstrate that this stress-regulated mechanism produces temporally segmented, self-organized learning episodes without reliance on externally defined goals. Our results suggest a possible route toward autonomous learning systems capable of self-assessment and internally regulated structural reorganization.

Figures (4)

• Figure 1: Two-Timescale Dynamical Framework. Fast dynamics $x(t)$ evolve within a structural landscape defined by slow parameters $\theta$. The system continuously performs intrinsic evaluation of its own dynamical health (e.g., freezing, non-ergodicity, irreversibility), generating an accumulated stress signal $Z(t)$.When stress exceeds a critical threshold $Z_c$, structural update is triggered, leading to a reconfiguration of the landscape $\theta \rightarrow \theta'$. The updated structure reshapes the effective landscape in which subsequent fast dynamics unfold.
• Figure 2: Intrinsic dynamical failure modes. (Left) Freezing. The system becomes confined to a narrow region of the landscape. (Middle) Non-ergodicity. The trajectory explores only a subset of the accessible landscape, failing to visit other basins over long timescales. (Right) Irreversibility. The trajectory undergoes a directed drift or structural bias such that previously accessible regions cannot be revisited.
• Figure 3: Stress-gated cognitive dynamics. (a) Badness and stress accumulator $Z$ as a function of the global time. (b) Plastic rent and plastic update cost as a function of the global time. (c) A zoomed view of the gating signal. The red dots represent the gate trigger. (d) The norm of the connectivity matrix $|W|$ as a function of the global time. (e) Gate-aligned heatmaps of badness. The horizontal axis denotes time relative to the gate, and the vertical axis indexes individual episodes. (f) Gate-aligned heatmaps of stress.
• Figure 4: Continuous Plasticity Dynamics. (a) Badness and stress accumulator $Z$ as a function of the global time. (b) Gate, plastic rent and plastic update cost as a function of the global time. (c) The norm of the connectivity matrix $|W|$ as a function of the global time. (d) Gate-aligned heatmaps of badness. The horizontal axis denotes time relative to the gate, and the vertical axis indexes individual episodes. (e) Gate-aligned heatmaps of stress.