YC Paper Club reveals how the same Claude Opus model scores 30% on ARC-AGI bare but reaches 95% with proper Harness, and NVIDIA's AVO hits 100%, proving agent runtime—not model weights—determines the performance ceiling.
Prime Agent is an open-source coding agent that eliminates manual harness writing by using a persistent IPython environment, recursive language model, and continual harness to self-improve, spawn parallel subagents, and execute long-term goals autonomously while reducing token usage.
Prime Agent, an open‑source AI agent framework, achieves a 95.5% score on the ARC‑AGI‑3 benchmark—surpassing the human baseline—by introducing Recursive Language Model (RLM) and a Continual Harness that enable persistent sessions, self‑improvement, and long‑task execution, while the article also examines controversies, risks, and practical deployment guidance.
Prime Agent, an open‑source agent framework, claims a 95.5% score on ARC‑AGI‑3 by engineering the RLM harness and a Continual Harness for self‑improvement and long‑running tasks, yet critics question the depth of its recursion, potential reward‑hacking, and whether the benchmark results reflect genuine general intelligence.
