Horizon Estimation
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Adaptive arrival cost update for improving Moving Horizon Estimation performance
arXiv:2606.04163v1 Announce Type: new Abstract: Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic.
Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models
arXiv:2606.07157v1 Announce Type: new Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind,...
Dual Advantage Fields
arXiv:2606.04188v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that capture global goal reachability, but they do not directly specify which action should be preferred at a given state. We propose Dual Advantage Fields, a policy-extraction method that turns a bilinear dual value model into a local advantage signal.
SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
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Managing hydrogen emissions is key to maximizing climate benefits as hydrogen use expands, say researchers
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ViVa: A Video-Generative Value Model for Robot Reinforcement Learning
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Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction
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Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning
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Data- and Variance-dependent Regret Bounds for Online Tabular MDPs
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Planet nine mystery deepens as new discovery challenges hidden planet theory
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