Collinear AI runs a Simulation Lab of sandboxed, stateful environments with simulated users (NPCs), tools, tasks and verifiers for agent RL training and evaluation. It now positions itself mainly as a supplier of training data, environments and evals to frontier labs across cybersecurity, software engineering, computer use and complex agent behaviour. Its CWE-bench defensive-cyber benchmark is part of the Artificial Analysis Cyber Index.
v1 has 120 held-out tasks covering 73 CWEs, all 10 OWASP Top 10 2025 categories and 8 languages, tested with 13,000+ agent rollouts. Agents run with no internet access during tasks; the post notes that one model still tried to get out using its own API key. Fixes are judged by a three-judge panel from different model families (GPT-5.4, Claude Sonnet 5, Gemini 3.8 Flash). The blog reports Grok 4.7 and GPT-6 Astra at 68% pass@1 and Claude Opus 5.5 at 67% for about a quarter of the cost ($0.79 vs about $2.80 per rollout).
Why it matters: The held-out cyber eval is maturing. cwe-bench.com also offers a separate training corpus of 4,000+ tasks across 215 CWEs (vendor-stated) and says eval tasks never appear in training deliveries.
The launch version had 100 held-out agentic audit-and-patch tasks across 54 CWE types and all 10 OWASP Top 10 2025 categories. Fable 5 led at 47% pass@1 at maximum reasoning, and 18 of the 100 tasks were unsolved by every model. Some tasks contain newly introduced vulnerabilities that published fixes do not cover, so memorised patches are not enough.
Why it matters: This is Collinear's flagship public benchmark and marks its move into cyber RL environments and data.
Artificial Analysis credits Collinear AI with developing CWE-bench and runs its own implementation, CWE-Bench-AA. That is one of three equally weighted evaluations in its Cyber Index, alongside DeepsecBench-AA and CyberGym-E2E-AA. The AA page carries no date. Collinear's 2026-09-28 v1 post confirms the inclusion, and cwe-bench.com says results are 'independently evaluated and published by Artificial Analysis'.
Why it matters: A third party has adopted a Collinear benchmark independently, which is a meaningful credibility signal.
Exec dinners on multi-turn evals drew research leaders from Apple, IBM Research, Two Sigma, NVIDIA, Datadog Research, Wells Fargo and Google. A Q2 research social in Sunnyvale had researchers from GDM, NVIDIA, Apple and xAI. Collinear is building NPCs with behavioural traits, agency, secrets and limited context into Simulation Lab. It says frontier models that 'were comfortably solving our environments a quarter ago are now hitting walls' (self-claimed).
Why it matters: Shows the direction of the environment roadmap: multi-agent work and stateful, realistic NPCs. Attending an event does not make a company a customer.
Blog post announcing a self-serve SimLab CLI. It offers sandboxed, isolated containers, pre-built simulators (e.g. Workday, Salesforce) or custom tools, programmatic task and verifier generation for long-horizon workflows, deterministic (non-LLM-judge) evaluation, and parallel rollouts. The post points to github.com/collinear-ai/simlab, but that repo returned 404 on 2026-10-05, so whether it is publicly available now is unverified.
Why it matters: This is the first self-serve way to use Simulation Lab and opens the product beyond managed engagements. Teams should check availability with the vendor.
YC-Bench simulates one year of running a startup, which takes hundreds of turns, to test whether agents stay strategically coherent. Of 12 models, only 3 consistently ended above the $200K starting capital. Claude Opus 4.6 led with $1.27M in average final funds and GLM-5 was second at $1.21M with much lower inference cost. The paper says adversarial-client detection is the main failure mode, accounting for 47% of bankruptcies. The code is MIT-licensed at github.com/collinear-ai/yc-bench.
Why it matters: This is an open, reproducible long-horizon agent benchmark from the vendor. Researchers can use it to evaluate planning and consistency.