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The key insight is that future visual predictions support action generation and therefore need not preserve every visual detail required for high-fidelity video synthesis, and CF-Prune, a training-free framework for pruning future-frame tokens in pretrained WAMs, is proposed.
This work introduces a policy-aligned predictive verifier (PAV) that anticipates the consequences of candidate action prefixes and evaluates their compatibility with successful continuation before execution, and combines trajectory-level survival supervision with a localization prior that preserves useful behavior with...
Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input. We introduce Conditional Program (CP), a compressed representation that reconstructs only the rows an input requests. CP represents each row by a short code and a loc...
Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often leave these choices inside predefined tools or require agents to manage detailed execution code and its growing history. We introduce RobotUs...
This work studies the effect of extrinsic reliability and pose conditioning on feed-forward 3DGS, and proposes a calibration-aware pipeline that anchors reconstructed scenes to the robot's metric workspace and presents a renderable, metric-pose-anchored dataset with scene-level reliability information for robot manipul...
This paper introduces Completion Aware Guidance (CAG), a training-free sampling method that guides generation toward task completion, and shows that this failure is not inherent to the world model backbone, but emerges when adapted for short-chunk control.
The results demonstrate the feasibility of harness distillation for robotics: intervention experience can be accumulated, refined through execution, and reused across agents to improve manipulation.
This work proposes LandingAgent, a three-phase agentic framework that profiles the target, constructs a reference-guided wireframe, and refines the page through critique-guided polishing, and evaluates it against direct prompting on faithfulness, conciseness, readability, aesthetics, and structural diversity.
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