We develop methods, benchmarks, and training systems that turn expert data into frontier AI
building benchmarks and collaborating with
Featured research
Senior SWE-bench built by Snorkel with Princeton University and UW-Madison
Continual Learning Bench: Evaluating agents that adapt and improve over time
Learning from Less: Measuring the Effectiveness of RLVR in Low Data Compute Regimes
Agents' Last Exam: Challenge and measure AI agents on economically valuable and real-world tasks
SlopCodeBench: A community benchmark measuring code erosion
Harvey’s BigLaw Bench: Research
Terminal-Bench 2.0: Raising the bar for AI agent evaluation
Benchmarking Agents in Insurance Underwriting Environments
Agentic Coding benchmark: Evaluating AI Models on complex, real-world coding tasks
Vision and impact
We help labs advance frontier models by working with domain experts to design and build complex, realistic datasets that drive model performance.
Benchmarking & Evaluation
Build benchmarks that define and advance the AI frontier
Scaling Subject Matter Expertise
Define how subject matter experts encode their knowledge into data
RL, Training, & Data Valuation
Drive dataset development based on feedback from RL and model training
Community and open science
Open benchmarks, conversations, and research for real-world AI performance.
Open Benchmarks Grants
Backed by a $30M commitment, Open Benchmarks Grants funds new benchmarks, red-teams existing ones to find weaknesses, and supports researchers through a fellowship.
Benchtalks
Reading Group
DEEP RESEARCH Expertise
Technical advisors and distinguished affiliates
Browse research blogs and academic papers
As agents get more capable, the data and evaluations that measure them have to become a science in their own right. Here are the sessions, takeaways and videos from the Frontier Data Summit.
AI agents are increasingly trusted with long, open-ended work. They fix bugs across large repositories, operate terminals for hours, and chain numerous tool calls toward a single goal. The strongest models now solve a meaningful share of such tasks, yet on the hardest ones failure is still the common outcome, and closing that gap is one of the central problems…
Today, we’re excited to announce that we are expanding our Open Benchmarks Grants by 10x to a $30M commitment, to support researchers, domain experts, and open-source teams pushing the frontier of robust, open frontier AI evaluation. The expanded program includes: Through our initial $3M commitment to Open Benchmarks Grants (OBG) and our broader research collaborations, we’ve partnered with the teams…
Yuexing Hao presents MedPAIR, a dataset comparing which sentences physicians and LLMs find relevant in clinical questions. Humans and LLMs agree on only 50 to 60% of relevance labels.
Agents increasingly build on code written by other agents, and they reimplement rather than reuse, growing the codebases later agents must work in. To measure how well agents design libraries for other agents, we introduce LibraryDesignBench, a two-phase benchmark in which an agent implements a full-featured library from a specification that defines required capabilities and potential use cases without prescribing…
TLDR: An agent, by definition, can take actions and is more than just a language model. The model is only one part of the system, and is only one element that you are training. The environment determines what the agent can observe, what it can change, which actions are available, and what behavior receives a reward. For a coding agent,…
We’ve now run three generations of frontier models through the same expert-created terminal-bench style task set: Fable 5.1 in September, Opus 5 in July, and, now Opus 5.5. For this analysis, the task set contained 200 trajectories, of 24 tasks, with every failure traced to a judge-confirmed root cause. A generational climb On the task set, pass@1 is 61.5% for…
Today, I’m excited to announce Snorkel’s $350M Series E financing at a $3.5B valuation, led by Insight and S32, with participation from Third Point, March, Blumberg, Allegis, Standard VC, Frontline, and existing investors Addition, Lightspeed, Greylock, GV, P7, Wells Fargo, Walden Catalyst Ventures, and Factory.
Grok 4.7 was evaluated on Senior SWE-Bench, our benchmark for measuring whether coding agents work like senior engineers. It reaches a tasteful pass@3 of 40.0%, up from 38.9% for Grok 4.6, ranking sixth overall at $0.24 per trial, which is a fraction of the cost compared to Opus 4.8. Benchmark Senior SWE-Bench evaluates agents as compared to a senior engineer….
