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[2602.18581] Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
Summary
The paper explores a novel framework for autonomous systems that enables learning without explicit objectives, focusing on self-regulation of internal dynamics through a stress-gated mechanism.
Why It Matters
As artificial systems evolve towards greater autonomy, traditional optimization methods may become inadequate. This research proposes a new paradigm that allows systems to self-assess and adapt without predefined goals, potentially leading to more robust and flexible AI applications.
Key Takeaways
- Introduces a framework for learning without explicit objectives.
- Proposes a two-timescale architecture for internal dynamics regulation.
- Demonstrates the potential for self-organized learning episodes.
- Highlights the importance of intrinsic health evaluation in autonomous systems.
- Suggests a pathway towards more adaptive and resilient AI systems.
Computer Science > Machine Learning arXiv:2602.18581 (cs) [Submitted on 20 Feb 2026] Title:Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems Authors:Sheng Ran View a PDF of the paper titled Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems, by Sheng Ran View PDF HTML (experimental) 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 architect...
