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A-SR achieves nearly a doubling of accuracy in symbolic regression tasks, showcasing a transformative approach to LLM-guided formula discovery.
Verified data synthesis can dramatically elevate the skill-use performance of language models, producing thousands of executable trajectories that enhance agent capabilities.
Despite LLMs excelling at identifying reviewer concerns, they falter in verifying if revisions truly resolve those issues, with the best achieving only a 0.501 score in evidence-based checks.
LabVLA achieves unprecedented success rates in executing complex laboratory protocols, outperforming all existing models in both familiar and novel settings.
