result-to-claim

To evaluate experimental results against intended claims, assemble key metrics, baselines, and deltas from sources like wandb.run().history() and EXPERIMENT_LOG.md, then delegate assessment to determine what results support, what they do not, and missing evidence.

Identifying missing evidence in research workflows requires assessing experimental results and project logs to flag what data supports the claims, what data contradicts them, and what specific evidence gaps remain before suggesting claim revisions.

Yes, you can use W&B run history to determine if experiment results support your hypothesis by extracting metrics and deltas, evaluating them against intended claims, and generating a confidence level with a final verdict recorded in findings.md.

Routing next actions after interpreting experiment data relies on a verdict of no, partial, or yes, which directs the project to pivot, supplement with next_experiments_needed, or confirm the findings and guide subsequent writing tasks.

Experiment analysis for claims evaluation works without external dependencies by using a secondary Codex agent to parse local sources like EXPERIMENT_TRACKER.md and project logs, extracting baselines to produce suggested claim revisions and confidence scores.