Prime Agent: A Self-Improving RLM Harness

2026-08-24

AI Summary

为解决长周期任务处理问题，Prime Agent采用递归语言模型和持续计算环境，通过子代理协调与人类管理实现高效执行。

Abstract

Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-source harness for long-horizon evaluation and coding-agent workflows. A persistent IPython REPL follows the Recursive Language Model abstraction for programmatic context processing and test-time compute, while Continual Harness preserves histories, memories, skills, prompts, and subagent specifications across trajectories. Recursive subagents coordinate through direct agent-to-agent communication, and the Agents View lets humans inspect and manage daemon-backed sessions. Prime Agent standardizes execution, recovery, verification, and resource accounting while leaving strategy construction to the model. This low-friction, expressive membrane prevents harness failures from becoming model failures and pushes measurement toward the model's true maximal underlying capability. Prime Agent raises ARC-AGI-3 RHAE Best@1 from 30% to 95.5% and matches or exceeds native and popular harnesses across long-context coding, GPU-kernel generation, emulator construction, and autonomous nanoGPT speedruns. On Factorio, we find refinement allows for continuous technology progression and dedicated subagents enable parallelized work. Code is available at https://github.com/PrimeIntellect-ai/prime-agent.

Problem

• long-horizon agency
• external information
• computation beyond model weights
• programmatic context processing
• persistent IPython REPL

Innovation

• Recursive Language Model
• Continual Harness
• Persistent IPython REPL
• Agent-to-Agent Communication
• Agents View