Research to Advance AI
Scale Labs advances AI through research. Our research focuses on agents, post-training, reasoning, safety, evaluation, and alignment, and the science of data.
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Benchmarks for frontier, agentic, and safety capabilities
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Model-preference rankings from real-world usage.
[PAPERS]
Research papers and publications covering agents, post-training, reasoning, safety, evaluation, and alignment, and the science of data.
CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR
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Insights, analysis, and updates from Scale Labs
CliniCARE-Bench: Clinical AI Agents Can Be Right for the Wrong Reasons
We evaluated 16 agentic systems on 25 clinical care scenarios over 750 real patient cases. Every single system committed to an answer more often than the evidence allowed, and up to one in five correct verdicts rested on an investigation the case authors had explicitly prohibited.
Can Robots Learn from Watching Us?
Human demonstrations allow for diverse data collection in the real world, but pose a difficult learning problem due to the large embodiment gap between robots and humans. Being able to effectively learn from high volumes of human interaction data can unlock the future of robotics. Our research demonstrates that adding a limited amount of human data to robot datasets can nearly double performance on unseen environments while only sacrificing a nominal amount of in-distribution performance.
TERMINAL-BENCH 3.0: Harder Tasks for Better Agents
TERMINAL-BENCH 3.0 is now live with broader coverage, harder tasks, and frontier tracking. Scale contributed the most tasks of any single organization in the launch set.
From Video Review to Measurement: Building Better Robot Data QC
How Scale builds automated quality control for robot training data: why VLMs alone fall short, and why treating QC as measurement works better.
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