Recursive built an AI agent that autonomously proposes, implements, and validates research ideas over long horizons. On the NanoChat benchmark it cut validation loss by 0.0263 BPB versus the community best and rediscovered and combined techniques like hashed bigram/trigram embeddings, optimizer tweaks, and architecture modifications.

• Recursive's autonomous AI research agent beat the community's best NanoChat result (0.9109 vs 0.9372 BPB) while also cutting training time 1.3x, running the full propose-implement-validate loop without human input.
• Starting from a bare Transformer+AdamW baseline (1.059 BPB), the system independently reached 0.9344 BPB, still beating the human-tuned public best.
• Two separate runs converged on similar tricks (hashed bigram/trigram embeddings, squared-ReLU MLPs) but also diverged with different novel additions (token shifting, weight averaging, byte-level embeddings), showing genuine exploration rather than repeating a fixed recipe.
• The winning approach added 1-2 billion sparse hashed n-gram parameters to a 50M-parameter model with minimal speed cost, using per-layer distinct hash primes to reduce collisions.