phase3-main-experiment

To scale a validated experiment slice into a full experimental matrix, use an orchestration pipeline that coordinates spec upgrades, code upgrades, and matrix execution across multiple datasets, LLMs, and baselines while preserving upstream validations.

Orchestrating end-to-end research pipelines across multiple datasets and LLMs requires a locked EXPERIMENT_SETUP and validated phase_1 specs, utilizing a robust Runner, Analyst, and Story Advisor loop to ensure reproducible results and traceable decisions.

Coordinate code upgrades and spec upgrades for a research matrix by applying an Architect-Confirmer loop to expand coverage and a Coder-Fidelity loop to execute the matrix, ensuring narrative checkpoints via a Story Advisor for traceable outputs.

Yes, you need a locked EXPERIMENT_SETUP and validated phase_1 specs before starting Phase 3 experiment orchestration to enforce requirements and ensure reproducible results when expanding a minimal matrix to full coverage.

A Story Advisor loop ensures narrative checkpoints in experimental research by evaluating traceable outputs and decisions during matrix execution, producing the coherent narrative structure required for a Main Results table in a paper.

Experiment matrix execution fails to preserve upstream validations if the pipeline lacks a locked EXPERIMENT_SETUP or bypasses the structured Runner, Analyst, and Story Advisor loop required to maintain traceable decisions across datasets.