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diff --git a/.agents/skills/results-analysis/examples/example-figure-catalog.md b/.agents/skills/results-analysis/examples/example-figure-catalog.md new file mode 100644 index 0000000..d9222f1 --- /dev/null +++ b/.agents/skills/results-analysis/examples/example-figure-catalog.md @@ -0,0 +1,33 @@ +# Figure Catalog + +## Figure 1 — Main comparison +- **Filename**: `figures/figure-01-main-comparison.pdf` +- **Purpose**: Compare WER across Full fine-tuning, Subject Adapter, and Frozen Encoder. +- **Data source**: `metrics/summary.csv` +- **Plot type**: Bar + scatter overlay of seed-level points. +- **Error bars**: 95% CI. +- **Caption must include**: + - metric direction, + - number of seeds, + - meaning of error bars, + - whether significance markers are corrected. +- **Key observation**: Subject Adapter closes most of the freezing gap. +- **Interpretation checklist**: + - Is the adapter improvement consistent across seeds? + - Is the gap practically meaningful, not just statistically significant? + - Does the figure support a design decision? + +## Figure 2 — Convergence dynamics +- **Filename**: `figures/figure-02-training-dynamics.pdf` +- **Purpose**: Compare optimization stability over epochs. +- **Data source**: `logs/train_curves.csv` +- **Plot type**: Mean line + uncertainty ribbon. +- **Caption must include**: + - smoothing rule if any, + - whether ribbon is std or CI, + - training/eval split. +- **Key observation**: Frozen Encoder shows larger oscillation after epoch 8. +- **Interpretation checklist**: + - Is instability transient or persistent? + - Does curve shape match final metric variance? + - Does this explain why one method is harder to tune? |