arXiv:2607.02050v1 Announce Type: new Abstract: Motivated by the challenge of stabilizing a general unknown linear dynamical system (LDS) from observations, we study the natural prerequisite of online prediction. Our goal is to achieve sublinear regret with a memory footprint that adapts to the intrinsic complexity of the dynamics rather than the full hidden -- state dimension. We focus on the practically central regime of systems with low instability complexity -- eigenvalues outside the real stable interval that do not decay rapidly, together with non-semisimple modes-potentially embedded in
Ongoing advancements in AI research continually push the boundaries of computational efficiency and algorithmic robustness, with a particular focus on online learning for complex systems.
A memory-efficient algorithm for online learning in linear dynamical systems could significantly reduce computational costs and enable AI applications in resource-constrained environments or for very large-scale systems.
The ability to stabilize unknown linear dynamical systems with adaptive memory footprints could accelerate the deployment of intelligent control systems and AI agents in real-world scenarios.
- · AI hardware developers
- · Robotics
- · Autonomous systems
- · Edge computing
- · Traditional control systems
- · Brute force computing approaches
More efficient and scalable online learning for AI control systems becomes feasible.
This could lead to faster adoption and better performance of AI agents and autonomous robotics in complex, dynamic environments.
Reduced resource requirements for sophisticated AI could lower barriers to entry for new AI applications and innovators.
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Read at arXiv cs.LG