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.
[LEADERBOARDS]
Benchmarks for frontier, agentic, and safety capabilities
[SHOWDOWN]
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.
DrugDiscoveryBench: Can Coding Agents Assist Early-Stage Drug Discovery?
[BLOG]
Insights, analysis, and updates from Scale Labs
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.
MCP vs. CLI: Does an AI Agent’s Tool Interface Still Matter?
CLI isn't a better default than MCP for AI agents. We ran a controlled 50-task comparison on identical backends across four frontier models. Interface choice matters, but less than you'd think, and the gap closes fast as models improve.
Insights Generator: Automated Failure Mode Analysis for Agents
Insights Generator (IG) analyzes thousands of agent execution traces at once and surfaces the behavioral patterns behind agent failures, with grounded evidence and prevalence estimates for each finding.
Can Coding Agents Tackle Early-Stage Drug Discovery?
Across 66 expert-curated drug-discovery tasks, three frontier coding agents each show distinct strengths but share one weakness: the long, multi-step pipelines that demand high-level planning rather than scientific knowledge.
View allAll posts