ln-811-algorithm-optimizer

Automated function performance optimization uses an autoresearch loop to generate 3-7 hypotheses, apply edits in isolated worktrees, and keep or discard changes based on benchmark test results.

Baseline benchmarking is automated as the first step of the optimization workflow, establishing initial performance metrics for a target function before generating research-driven improvement hypotheses.

Yes, this performance optimization workflow requires a codebase with existing tests and benchmarks to validate changes and guide keep or discard decisions during the automated hypothesis cycles.

Isolated worktree safety ensures automated performance edits are applied in separate workspace branches, allowing traceable commits and safe keep or discard decisions without affecting the main codebase.

Yes, the workflow provides detailed experiment logging and traceable commits for each hypothesis, documenting whether automated edits were kept or discarded based on performance benchmark outcomes.

Limitations include dependency on existing test coverage quality and benchmark accuracy, as the automated hypothesis cycle can only keep or discard edits based on available validation metrics.