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tsungyi 
posted an update 5 days ago
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Cosmos Reason just topped Physical Reasoning Leaderboard on Hugging Face. 👏🔥

Cosmos Reason is an open, customizable, commercial-ready 7B-parameter, reasoning vision language model (VLM) for physical AI and robotics. The VLM empowers robots and vision AI agents to reason like humans, leveraging prior knowledge, physics understanding, and common sense to understand and operate intelligently in the real world.

This model unlocks advanced capabilities for robotics, autonomous vehicles, and real-world operations—from cities to high-tech factories.

Key use cases include:
Data curation & annotation: Automate high-quality dataset curation and annotation at scale.
Robot planning & reasoning: Serve as the "brain" for deliberate, methodical decision-making with vision language action (VLA) models.
Video analytics AI agents: Extract actionable insights and perform root-cause analysis on massive video datasets.

Ready to build the next generation of physical AI? Get started 👉 nvidia/Cosmos-Reason1-7B
Try the preview here: https://build.nvidia.com/nvidia/cosmos-reason1-7b

di-zhang-fdu 
posted an update 3 months ago
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Our new paper Moose-chem 3 introduces experiment-guided hypothesis ranking, a novel setting where candidate hypotheses are prioritized based on experimental feedback from previously tested hypotheses.

To support research in this area, the work proposes a simulator grounded in three domain-informed assumptions that can generate simulated experimental feedback without requiring costly real-world trials.

The simulator is validated on a curated dataset of 124 chemistry hypotheses, and the resulting method outperforms strong pre-experiment baselines. This enables scalable research on feedback-driven hypothesis discovery strategies in scientific domains where empirical validation is expensive or slow.
MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback (2505.17873)