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· Past edition · 8 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Research & academia
The paper recasts tool use as a code-conditioning problem: train models to fill in functions whose returned values are used later, then test whether that helps coding agents.
Why it matters: The reported gains suggest that training models to use information returned mid-task may improve agent performance while helping preserve coding and tool-use capabilities after agentic post-training.
Research & academia
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On the Woven Traffic Safety video task, NVIDIA reports Cosmos 3 Nano rising from 54.41% zero-shot accuracy to 93.35% after LoRA and TAO AutoML.
Why it matters: The workflow offers a practical way to test domain adaptation before committing to a larger sweep: the reported LoRA run takes around 30 minutes, while the higher-accuracy AutoML result required a 19.5-hour sweep. These are experiment results, not a guarantee for other datasets or hardware.