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Treating natural state trajectories as self-supervised "actions" turns static scientific emulators into interactive, counterfactual world models without requiring manually labeled intervention data.
Recovery routing can outperform escalation strategies by leveraging execution feedback, achieving a higher solve rate at only 35% of the typical recovery cost.
WVM outperforms existing models by accurately assessing task progressions and improving robotic manipulation from both expert and suboptimal data.
Ling-2.6 and Ring-2.6 achieve unprecedented efficiency in agentic intelligence, enabling instant responses and deep reasoning at trillion-parameter scale.
Training LLMs on ultra-long contexts just got a whole lot easier: AutoSP automates sequence parallelism and activation checkpointing, boosting context length by up to 2.7x with negligible throughput cost.
Stop guessing about action spaces for robot manipulation: a massive empirical study reveals that predicting delta actions boosts performance, while joint vs. task space offers a stability vs. generalization tradeoff.
