4th box eval artifacts: DaTikZ-984 (kimi/vlm x stock/DeTikZify-V2) + MathVision

README.md

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+# 4th box (8xB200) evaluation artifacts
+
+Every per-item file behind the tables in `rl/RUN_LOG_4TH_BOX.md` of `explcre/dsv-experiments`
+(branch `gemma-rl-b200-4th`). Published so the numbers are checkable and because the source lived on an
+ephemeral local SSD.
+
+## datikz984/ — DaTikZ official 984-item test set
+
+`gen_<label>.jsonl` — one row per prompt: `file_id`, `caption`, `gen_code`, `gen_raw`, `gold_code`.
+`scores_<label>.jsonl` — one row per prompt: `dsv_mean`, `dsv_scalar`, `imagesim_rsim`, `dreamsim`,
+`clipscore`, `ted_sim`, `render_graph`, `gen_compiles`, and **`imagesim_encoder`** (which encoder scored it).
+
+| label | model | description family | ImageSim encoder |
+|---|---|---|---|
+| `D650_kimi_stock` / `_dtz` | `scale_3brl_blend_rsim_w0p3_detikzify_siglip_termwise` @650 (term-wise credit) | kimidesc | stock / DeTikZify-V2 |
+| `D650_vlm_stock` / `_dtz` | same | vlmdesc | stock / DeTikZify-V2 |
+| `CTL650_*` | `scale_3brl_blend_rsim_w0p3_detikzify_siglip` @650 (matched control, no term-wise credit) | " | " |
+| `4th_armA` (+`_dtzenc`) | `dapo_4th_fixabstain_scalar` @400 | kimidesc | stock / DeTikZify-V2 |
+| `4th_armB` (+`_dtzenc`) | `dapo_4th_fixabstain_witness_gated` @400 | kimidesc | stock / DeTikZify-V2 |
+
+**R_Sim is not comparable across encoders.** On a fixed model, stock -> DeTikZify-V2 moves it by **-0.069**
+(arm A: 0.3892 -> 0.3202) -- roughly 50x the arm-vs-arm differences being measured. Always read the
+`imagesim_encoder` field.
+
+Step 650 is used for the D-vs-control pair because it is the newest checkpoint **both** runs had; comparing
+across different amounts of training would not be a method comparison.
+
+## mathvision/ — MathVision diagram-generation benchmark
+
+`eval_results.json` — overall + per-category DISTS / CLIP-sim / Edge IoU / Edge F1 / CMMD, and `per_image`.
+`generation_log.json` — per-prompt emitted code and stats.
+
+⚠️ `mv_CTL650/generation_log.json` holds **730 of 2,920** entries: generation was sharded over 4 GPUs and
+`generate_local.py` writes one log per *run*, so the shards overwrote each other. This affects **only**
+`code_language_distribution` (hence 2,176 `unknown`). Every headline metric is computed from the PNGs and all
+are present; the 730 surviving entries are 100% `tikz`.
+
+The ~5,800 rendered PNGs are **not** included -- 118 MB of intermediate rasters, regenerable from the code in
+`generation_log.json`.
+
+## Result, in one line
+
+Term-wise credit does not beat its matched control on any benchmark. DaTikZ `dsv_mean` +0.0007 (kimi,
+p=0.875) / +0.0016 (vlm, p=0.626); MathVision all four metrics p>=0.16. The only Holm-surviving effect across
+10 tests is kimidesc R_Sim at **-0.0050 against** term-wise credit (Holm p=0.010), unreplicated on vlmdesc.