Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
arXiv:2602.18581v1 Announce Type: new 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
arXiv:2602.18581v1 Announce Type: new 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.
Executive Summary
This article proposes a novel dynamical framework for learning in autonomous systems, which learns without an explicit objective function. The proposed method evaluates the intrinsic health of the system's internal dynamics and regulates structural plasticity accordingly, using a stress variable that accumulates evidence of persistent dysfunction. The two-timescale architecture separates fast state evolution from slow structural adaptation, enabling temporally segmented, self-organized learning episodes without reliance on externally defined goals. The authors demonstrate the efficacy of this approach through a minimal toy model, suggesting a possible route toward autonomous learning systems capable of self-assessment and internally regulated structural reorganization.
Key Points
- ▸ The authors propose a dynamical framework for learning in autonomous systems without an explicit objective function.
- ▸ The framework evaluates the intrinsic health of the system's internal dynamics and regulates structural plasticity accordingly.
- ▸ A two-timescale architecture separates fast state evolution from slow structural adaptation, enabling temporally segmented learning episodes.
Merits
Strength in Autonomous Systems
The proposed framework addresses the need for autonomous systems to operate over long horizons and across evolving contexts without explicit objectives. The ability to evaluate intrinsic health and regulate structural plasticity accordingly is a significant strength in this context.
Demerits
Scalability Limitation
The proposed framework may not be scalable to more complex systems, as the toy model used to demonstrate its efficacy is relatively simple. Further research is needed to explore its applicability to more complex systems.
Interpretability Challenge
The use of a stress variable to accumulate evidence of persistent dysfunction may raise interpretability challenges, as it is unclear how the system will interpret and respond to this variable.
Expert Commentary
The proposed framework is a promising approach to learning in autonomous systems, as it enables the system to evaluate its intrinsic health and regulate structural plasticity accordingly. However, further research is needed to explore its applicability to more complex systems and to address the scalability and interpretability challenges. Additionally, the implications of this framework for policy and regulation are significant, as it raises questions about the need for external supervision and evaluation in autonomous systems.
Recommendations
- ✓ Further research is needed to explore the applicability of this framework to more complex systems and to address the scalability and interpretability challenges.
- ✓ The implications of this framework for policy and regulation should be explored in more detail, including the need for external supervision and evaluation in autonomous systems.
