Turing is a large incumbent supplier of expert human data to frontier AI labs. It claims 5M+ experts and work with '9/9 frontier labs' (both self-claimed). In 2026 it moved further into verifiable agent environments and evaluations. It launched Turing Frontier in April, set up the Turing Frontier Research Lab, and published benchmarks including CyberStrike (Harbor format, public on Hugging Face), KernelQuest, CEO Bench, SciCode++ and PLSQLBench (with Oracle, per Turing). It also contributed tasks to Terminal-Bench 3.0 and hired a research-heavy CTO, Ece Kamar (ex-Microsoft Research).
An expert-reviewed, test-validated scientific coding dataset that extends SciCode. LoRA SFT of Qwen 3.5 9B on it raised scores by 10.3% relative to baseline on SciCode and 9.1% on SciCode-Verified. The dataset is proprietary; samples are available on request.
Why it matters: A published post-training result showing the data's effect on a model, not only an evaluation.
Previously VP of Security & IT at Invisible Technologies. Earlier he held security and compliance roles at Dropbox and Coinbase.
Why it matters: A dedicated security executive matters to data-ops and procurement teams. The release names no certifications (SOC 2/ISO).
Long-horizon financial and operational tasks inside a simulated direct-to-consumer company, Cirrus Sleep, Inc., graded against expert rubrics. The best model, GPT-5.6 Terra, averaged 41.3/100.
Why it matters: Extends Turing's environments into enterprise knowledge work, which is a growing target for agent RL.
Previously Corporate Vice President leading the AI Frontiers Lab at Microsoft Research. She holds a Harvard CS PhD. She will lead technology and research strategy across Turing's frontier AI and enterprise AI divisions.
Why it matters: Hiring a senior research leader from a major lab adds to Turing's research credibility with frontier-lab buyers.
Engineer-written Triton kernel tasks graded through structural, correctness, policy and performance gates. Claude Opus 5 led with a 67.0% (±5.3) pass rate. Of 1,200 agent runs, 565 were rejected before speed was counted, and 170 of those had already met their speed target.
Why it matters: Shows the verifier design behind Turing's agentic coding environments, built to resist reward hacking.
The hub lists Open-MM-RL, SciCode++, SDLCbench, Terminal-Bench 3.0, CEO Bench, CompanyBench, EKWbench, Medical Bench and OdysseysMega, with update dates from 2026-05-01 to 2026-09-29.
Why it matters: One place to see Turing's evaluation coverage across STEM, SWE, enterprise knowledge work and browser/computer-use tasks.
A benchmark of whether LLM systems can write executable PL/SQL, graded by execution-based tests and run on eight frontier models. Models struggled with schema grounding, dialect fidelity, procedural control flow, exception handling and consistency across turns. Turing's newsletter calls it 'new research from Turing in collaboration with Oracle'. The arXiv page does not show affiliations.
Why it matters: This is the only enterprise-vendor research collaboration found in the window. The collaboration is self-described by Turing.
Turing says 'some of the tasks' in Terminal-Bench 3.0 were 'authored and verified by our team'. Its analysis of 3,600 trials found that almost all failures produced well-formed artifacts with wrong content, and that agents' self-checks repeated their own misreading of the task.
Why it matters: Turing is a named task contributor to a public agent benchmark (leaderboard at frontierbench.ai). It contributed tasks; it does not own the benchmark.
Expert-written offensive, defensive and DFIR tasks in Harbor format: an instruction, a containerized environment, a hidden deterministic verifier and an oracle solution. The top model, GPT-5.6 Sol, had a 31.5% mean per-task pass rate. The dataset is public on Hugging Face under CC BY-NC-ND 4.0.
Why it matters: Turing's first openly downloadable set of Harbor-format agent environments. It shows Turing builds verifiable RL-style environments, not only human data.
Previously CRO at Weights & Biases, where he built a global field organization selling to AI labs and enterprises and 'saw the company through its successful acquisition by CoreWeave'. Earlier he was CEO of Figure Eight, which Appen acquired.
Why it matters: He has long experience selling training data and tooling to AI labs, which points to a push on frontier-lab and enterprise sales.
Two sets of expert-written terminal tasks graded by verifiers. One covers ML, data analysis and scientific computing; the other covers the STEM sciences. Turing publishes frontier-model pass rates for both: GPT-5.5 scored 48.6% on Terminal-Bench Data, and Claude Opus 4.8 scored 43.7% on Agentic STEM.
Why it matters: Turing now sells verifier-graded agentic training data in a terminal-environment format, which matters directly to buyers of RL environments.
Turing's press page lists a BusinessWire release with this headline dated Apr 9, 2026. The release itself returned 403, so only the headline and date are verified.
Why it matters: Makes Turing's expert-data work for frontier labs a named product line.