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Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstrations is costly. A practical self-improving system must decide both what to teach next and where to apply that supervision. We present ROBOCOACH, a world-model-guided coachin...
A unified theoretical framework is introduced that separates distribution modeling from matching discrepancy and connects global objectives to pointwise feature updates through Wasserstein gradient flow and motivates MGFlow, which models feature distributions with Gaussian mixtures at an adjustable granularity between...
Predicting smooth dynamics and collisions requires modeling continuous evolution and abrupt state changes. We introduce HamiFormer, a dual-expert diffusion field combining whole-window denoising with residual-corrected Hamiltonian propagation. Their mixed-state feedback attenuates the direct contribution of inherited a...
This framework minimizes maximum mean discrepancy in frozen self-supervised video representation spaces in frozen self-supervised video representation spaces, using a hybrid Nystr\"om--Monte Carlo estimator to balance approximation bias and sampling variance.
Completeness-aware Motion Correspondence (CMC), a ground-truth-anchored evaluation protocol that jointly measures localization, trajectory completeness, visibility, and continuity, counting missing predictions as failures on visible dynamic points, is introduced.
Transition-wise Rubric Credit Assignment (TRCA) is proposed, which derives step-level supervision directly from action-induced transitions without learned evaluators or successful anchors for long-horizon tasks with sparse successful anchors.
VERDI is proposed, a continual framework for evidence-licensed world model optimization that characterizes each world model through shared inference-time probes to construct an Optimization Fin- gerprint, retrieves relevant prior experience as ranked hypotheses, and validates every candidate under a frozen target-side...
This paper proposes ElasticTTT, a novel framework that preserves the prior generative distribution and rescues generative elasticity in standard TTT, achieving state-of-the-art performance on one-shot video editing.
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