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Results show that structured agentic debugging can address a key cyber-physical integration bottleneck in real-world robot deployment, and are shown to be more complete and efficient recovery than human operators using Claude Code.
A unified view of progress reward modeling for robotic learning is provided in three connected steps that connect what a progress model is, how it is built, and how its quality is validated.
From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop.
Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance.
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