Foresight uses iterative VLM plan proposal and critique with RL from human feedback to raise navigation success 37% and cut interventions 52% in real-world tests.
Asymptotic evaluation of certain markov process expectations for large time, i
5 Pith papers cite this work, alongside 613 external citations. Polarity classification is still indexing.
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In every dimension d≥2 there exists a unique β_*^{(d)}>0 such that the uniform density on the sphere is the unique global minimizer of the USA free energy up to the linear-stability threshold K_# for β≤β_*, yielding a continuous transition, while for β>β_* the uniform density is not globally minimiz
Brownian occupation measures conditioned on large self- or mutual-intersections converge weakly to the square of a Gagliardo-Nirenberg optimizer via new large deviation principles.
Derives generalization bounds for quantum learning via quantum and classical Rényi divergences, with a new modified sandwich quantum Rényi divergence shown to outperform the Petz version analytically and numerically.
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
citing papers explorer
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Foresight: Iterative Reasoning About Clues that Matter for Navigation
Foresight uses iterative VLM plan proposal and critique with RL from human feedback to raise navigation success 37% and cut interventions 52% in real-world tests.
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Phase transitions for the noisy transformer model in arbitrary dimension
In every dimension d≥2 there exists a unique β_*^{(d)}>0 such that the uniform density on the sphere is the unique global minimizer of the USA free energy up to the linear-stability threshold K_# for β≤β_*, yielding a continuous transition, while for β>β_* the uniform density is not globally minimiz
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Asymptotics of Brownian occupation measures with unusually large intersections
Brownian occupation measures conditioned on large self- or mutual-intersections converge weakly to the square of a Gagliardo-Nirenberg optimizer via new large deviation principles.
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Generalization Bounds for Quantum Learning via R\'enyi Divergences
Derives generalization bounds for quantum learning via quantum and classical Rényi divergences, with a new modified sandwich quantum Rényi divergence shown to outperform the Petz version analytically and numerically.
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Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.