State-conditioned commitment depth in a vision-language policy Pareto-dominates fixed-depth baselines on Sliding Puzzle and Sokoban, raising solve rates by up to 12.5 points while using 25% fewer actions and beating larger models.
Constrained model predictive control: Stability and optimality.Automatica, 36(6):789–814
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For convex losses in nested evolving feasible sets, a lazy algorithm balances O(T^{1-β}) regret with O(T^β) movement for any β; for strongly convex or sharp losses, Frugal achieves zero regret with O(log T) movement, shown optimal by matching lower bound.
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When to Re-Commit: Temporal Abstraction Discovery for Long-Horizon Vision-Language Reasoning
State-conditioned commitment depth in a vision-language policy Pareto-dominates fixed-depth baselines on Sliding Puzzle and Sokoban, raising solve rates by up to 12.5 points while using 25% fewer actions and beating larger models.
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Convex Optimization with Nested Evolving Feasible Sets
For convex losses in nested evolving feasible sets, a lazy algorithm balances O(T^{1-β}) regret with O(T^β) movement for any β; for strongly convex or sharp losses, Frugal achieves zero regret with O(log T) movement, shown optimal by matching lower bound.