Researchers from several universities and tech companies developed AutoTTS, a system where Claude Code autonomously discovers test-time scaling algorithms for LLMs rather than having humans design them manually. Operating within a pre-built simulated environment, the AI agent iteratively writes and refines control algorithms, ultimately producing one that reduces token usage by ~70% compared to standard methods while maintaining accuracy. The entire discovery process cost around $40 and took under three hours, and the resulting algorithm uses a dynamic confidence-tracking logic that the authors say would have been nearly impossible for humans to design themselves.
