deep-innovation-loop

Automating autonomous ML method evolution requires running a multi-round research loop that diagnoses root causes, searches literature, and synthesizes novel variants. You configure a research anchor and trigger Phase A to begin the iterative process.

Root-cause diagnosis in ML research identifies why a method fails rather than just detecting what fails. This mechanism informs literature-driven design to synthesize novel method variants for the next evolution round.

Integrating literature reviews into ML method design is handled by the deep-innovation loop's literature research phase. It automatically searches and synthesizes literature to propose new techniques for method variant design.

Autonomous experimentation supports long-term ML research projects through state persistence via innovation-logs and modular knowledge files. It tracks method evolution across 40+ rounds, maintaining a stored state and audit trail.

The ML method evolution loop requires configuring a research anchor with VENUE, DOMAIN, and PRIMARY_BASELINE parameters before triggering Phase A diagnosis. This setup ensures autonomous design and evaluation aligns with your research context.