Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
On information and sufficiency.The Annals of Mathematical Statistics, 22(1):79–86
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In kinship-dominant agent swarms, adding logical agents increases stability of erroneous trajectories, leading to logic saturation with zero internal entropy but unit factual error.
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Is Flow Matching Just Trajectory Replay for Sequential Data?
Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
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An Efficient Algorithm for Minimizing Ordered Norms in Fractional Load Balancing
A randomized (1+ε)-approximation algorithm for ordered-norm load balancing uses O((n+d)(ε^{-2} + log log d) log(n+d)) linear-oracle calls via follow-the-regularized-leader prices and martingale progress analysis.
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The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms
In kinship-dominant agent swarms, adding logical agents increases stability of erroneous trajectories, leading to logic saturation with zero internal entropy but unit factual error.