Prime Intellect's self-improving agent harness hits 95.5% on ARC-AGI-3 in 2.5h deep dive
yacinelearning · x · 2026-10-26
Prime Intellect's Prime Agent (22.3K GitHub stars) combines three pieces: Recursive Language Models (RLM), a continual harness that the agent itself can modify, and persistent sub-agents that message each other directly. The author sat down with Princeton's Seth Karten and Prime Intellect for a 2.5-hour deep dive.
Key points
- The team productionized @a1zhang's recursive language models: the main agent writes code to organize context, delegate subtasks, and edits its own skills, memories, and prompts while running.
- The design reads like an OS paper rather than an ML one (confirmed intentional by Seth): weights, context, REPL, and disk form a memory hierarchy, each level with its own update rule — a limited set of primitives the agent figures out how to run.
- Results: 95.5% on the ARC-AGI-3 public set with Opus 5 (vs 30% in the official harness); a week-long Factorio run used 633 sub-agents.
- Notable findings: letting an agent edit its full system prompt makes it delete itself; discussion of reward hacking and frontier models' exploration behavior.
- Early-2026 assumption that scaffolding would be absorbed into weights as models improve hasn't held — a solid, self-steerable harness remains essential.
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