Stanford and NVIDIA have open-sourced CLM-8B, a model comparable to Jev, delivering up to 9x faster performance.

Insight Beating AI News Flash: Researchers from Stanford University and NVIDIA Research have open-sourced CLM-8B, a System One model in the same category as Jev, designed specifically to help AI agents make quick judgments and selections. CLM and Jev serve the same core function: both enable agents to skip long text generation and directly choose from a set of candidate actions. CLM’s key distinction lies in its underlying architecture: it separates the calculation of "current state" and "candidate actions", allowing fixed actions to be precomputed and reused, resulting in faster performance in scenarios requiring sequential decision-making. In official tests, CLM-8B performs on par with Jev overall, with latency reduced by up to approximately 9 times. The team also tested CLM’s ability to select answers for coding agents: Opus 5 and Fable 5 first generate multiple candidate solutions, then the fine-tuned CLM picks the optimal one. On DeepSWE, CLM boosted Opus 5’s single-pass success rate from 73.7% to 81.6%; on Terminal-Bench 2.1, it increased from 84.0% to 87.6%. CLM does not generate code itself; the test evaluates its ability to select superior solutions from existing candidates. CLM-8B is built on a frozen Qwen3-8B, with only state and action projection heads trained additionally. Its training data includes approximately 60 million question-answer pairs, 30 million hard negative samples, and around 1 million agent trajectories. The code and model weights are both open-source, released under the Apache 2.0 license.