Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
Boumal,An Introduction to Optimization on Smooth Manifolds
4 Pith papers cite this work. Polarity classification is still indexing.
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Proves global exponential convergence of PI and feedback linearization Lagrangian flows for non-convex equality-constrained optimization under a manifold-restricted convexity property.
Applies movable IRS to ISAC with joint position and beamforming optimization via Riemannian methods to reduce power consumption.
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.
citing papers explorer
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Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
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Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization
Proves global exponential convergence of PI and feedback linearization Lagrangian flows for non-convex equality-constrained optimization under a manifold-restricted convexity property.
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Movable IRS-Aided ISAC Systems: Joint Beamforming and Position Optimization
Applies movable IRS to ISAC with joint position and beamforming optimization via Riemannian methods to reduce power consumption.
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Low-Rank Adaptation Redux for Large Models
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.