Prime Intellect's Open-Source Harness Lifts Claude Opus 5 to 95.5% on ARC-AGI-3 Without Touching the Model

Prime Intellect has published the technical report behind Prime Agent, the open-source agent harness it released in early August, and the results argue that a large share of current agent capability resides outside the model itself. Running Anthropic's Claude Opus 5 without any additional training, the harness raised ARC-AGI-3 performance from about 30 percent to 95.5 percent on the benchmark's Best@1 measure, slightly above the 95.4 percent human expert baseline the benchmark reports.

The metric matters as much as the number. ARC-AGI-3 scores agents on Relative Human Action Efficiency, which compares the number of actions an agent takes in an interactive environment against a human baseline and squares the ratio, so an agent that needs twice as many moves keeps only a quarter of the credit. The benchmark gives no natural-language instructions and requires the agent to infer goals from interaction. Frontier systems scored below one percent on it earlier this year.

Prime Agent's two ideas are a Recursive Language Model, in which the model works inside a persistent IPython kernel and calls sub-agents as ordinary function calls rather than packing everything into one context window, and a Continual Harness that stores prompts, memories, executable skills and sub-agent specifications as typed, versioned state carried across runs. The report also describes an 85.5-hour autonomous nanoGPT training run with 19 validated records, and says the harness matches or beats the vendors' own scaffolds, including Claude Code and Codex, on long-context coding, GPU-kernel generation and emulator construction.

Two caveats belong next to that figure. The runs are self-reported and have not been independently reproduced by the ARC Prize organizers, and Prime Intellect, which sells decentralized training and inference capacity, benefits directly if the industry concludes that the harness rather than the checkpoint sets the ceiling. The Russian-language channel AI ML Big Data summarized the report as an operating system for long-lived agents, which is closer to the claim than "smarter model" is.