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A Self-Evolving Embodied System (SEES) that learns from failures and improves the VLA policy without additional expert demonstrations, and can be integrated with different VLA backbones to progressively improve their long-horizon performance.
Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classification. However, bag-level labels do not explicitly supervise the correspondence between candidate classes and temporal evidence. We propose {\ours}, which couples labe...
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formulation can only report how often the model policy succeeds, not which solution actually worked, and because probabilities sum to one, reinforc...
This work introduces a novel, scalable branched flow-matching algorithm designed to solve the branched optimal transport problem in high dimensions and demonstrates its effectiveness on challenging high-dimensional tasks in biology and image generation.
A phenomenon in which agent heterogeneity gradually decreases during simulation is revealed, and Prospect-State Propagation for Multi-Agent Systems (PspMAS) is proposed, Inspired by prospect theory, which decouples each agent's micro state into a compact Prospect State and an expressive Semantic State.
A capacity-headroom hypothesis is proposed, which states that PPO performance at the SLM scale depends on both a fluent supervised model and a discriminative reward signal, rather than on the number of model parameters.
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