AI models can get the facts right and still miss what matters for sensitive U.S. Government use cases. We tested nine frontier models on government mission reporting: only 3 of 540 reports met every criteria. Introducing MissionReport, a benchmark built by @micro1_ai with current and former U.S. government and military professionals to test whether AI can identify, prioritize, and communicate what commanders and senior leaders need to know for mission critical matters. More info in thread 🧵

Today we’re announcing the Baseten Environment Network, a group of the best RL environment companies that will help our customers build custom environments for post-training. The Network extends our applied research capacity, helping more customers build environments around their own tasks, data, and feedback. Partners work directly with customers to create the environments, while we continue to handle model training and production deployment. Learn more:

Introducing the Ivo-micro1 ContractBench: a new benchmark from Ivo and @micro1_ai for measuring the judgment behind contract review. Most legal AI benchmarks focus on the edits a model makes. But an effective review requires more than knowing what to change. It means understanding things like what to leave alone, when to adapt a standard position to the deal, and when to escalate an issue to a lawyer. ContractBench evaluates these decisions across 350 tasks, 35 scenarios, and five agreement types. We tested seven frontier models against five dimensions of contract-review judgment. What we found: → Restraint is the strongest skill across every model tested. → Models struggle to reconcile standard playbook rules with deal-specific context. → Escalation remains a major gap, even when models have clear approval boundaries. As AI takes on more of the review process, we need to measure not just whether a model can make an edit, but whether it can make the right decision. We’re publishing our methodology, results, and public dataset to provide a blueprint for how to improve models for contract work. Explore ContractBench: Read the full findings:

Most companies can’t determine whether their AI agents are genuinely improving and our findings reveal why. The Economics Research Unit (ERU) at @micro1_ai , which I am privileged to lead, has released its inaugural paper, co-authored with @liuzhang11 : “Reliability of AI Agents: Rater Effects, Drift, and the Return to an Evaluation Program.” Key Insights from 2,611 Real Production Interviews By tracking a live AI interviewing agent in production, we uncovered two major dynamics: 1) Your evaluation metrics may reflect reviewer bias rather than AI performance. The identity of the reviewer was a stronger predictor of scores than the timing of the interview. In fact, a single overly strict evaluator masked a genuinely improving agent, making it appear stagnant. Once adjusted, reliability metrics increased by half a standard deviation. 2) AI evaluations go stale after roughly five weeks. Performance drifted even without code updates or new deployments. Without ongoing testing, after five weeks you lose the ability to detect a change as significant as your largest release. A one-time evaluation tells you about the agent you had, not the agent you are running. Agents drift, so evaluation cannot be a launch-day event. It has to run continuously for as long as the agent is live. That is the principle behind Cortex, micro1's reliability layer for agents in production, and this paper shows why it matters. Measuring AI agents is not a mere compliance checkbox; it serves as core economic infrastructure. This research marks just the beginning for the ERU, with further studies on the horizon.