fpa-research-looplisted

# Company Research Loop ## Purpose Run an AutoResearch-style loop against the company's own forecast history. The AI may generate, test, and discard challengers autonomously. Only promotion to the active champion requires human approval. ## Memory And State - `.fpa/research/objective.yaml`: company-specific metrics, weights, hard checks, minimum improvement, and complexity penalty. - `.fpa/research/*.epoch.yaml`: every hypothesis and evaluated epoch, including discarded candidates. - `.fpa/models/registry.yaml`: current champion, challengers, retired champions, and human-approved promotion history. - `.fpa/index.yaml`: rebuildable lexical memory index. - `.fpa/context-pack.md`: temporary task-specific retrieval output, never canonical memory. ## Workflow 1. **Discover the company command.** Run `openfpa entrypoint-list <company-root> --kind research`. Use a registered research runner when one exists. 2. **Retrieve context.** Rebuild memory with `pyfpa.build_memory_index(".fpa")`, then create a context pack for the miss being investigated. Read prior failed epochs before proposing a repeated hypothesis. 3. **Load the objective and registry.** The objective is CFO-specific. It should include forecast-error metrics by decision importance, hard accounting checks, a minimum improvement, and a complexity penalty. 4. **Run bounded epochs.** Default to at most five challengers in one run. For each: - state one falsifiable financial hypothesis;