Recursive

Recursive is the RSI-oriented startup discussed in 171: 【AI季报 26Q2】从 coding 到 RSI，强者愈强的未来？. The source says the team reported early results on NanoChat Auto Research, NanoGPT Speed Run, and GPU Kernel Benchmark, and treats those results as evidence that automated research loops can improve algorithms, training speed, and hardware utilization rather than only write application code.

Recursive’s importance is not only its source-reported benchmark position. The episode uses it to argue that Auto Research and Recursive Self-Improvement are not technically settled, leaving room for startup ideas around research loops, evaluation, and ML Coding even when frontier labs have more compute.

Connections

• Auto Research — immediate research-automation mechanism.
• Recursive Self-Improvement — broader self-improvement loop the source associates with Recursive’s direction.
• ML Coding, AI Coding Verification, and Research Taste — practical ingredients for useful research automation.
• AI For Science and Discovery Model — adjacent scientific-discovery branch.