docs: add VLMEvalKit evaluation guide, input conditions, reproducibility notes

README.md

@@ -32,16 +32,115 @@ Items are built from open-access PubMed Central case reports. Imaging findings
 are withheld from the question context, so a model that ignores the image cannot
 recover the answer from the text alone.
 
+> MRareBench is a separate benchmark from `junzhin/MMrarebench`. The two differ
+> in tracks, item counts, and evaluation protocol. Scores are not comparable
+> across them.
+
 ## Files
 
-| File | Task | Items |
-|---|---|---|
-| `diagnosis/diagnosis_opened.tsv` | T1 — forward diagnosis, ranked differential | 300 |
-| `evidence_verification/evidence_verification_opened_FULL609.tsv` | T2 — evidence verification, open-ended | 608 |
+| File | Task | Items | Columns |
+|---|---|---:|---:|
+| `diagnosis/diagnosis_opened.tsv` | T1, forward diagnosis, ranked differential | 300 | 9 |
+| `evidence_verification/evidence_verification_opened_FULL609.tsv` | T2, evidence verification, open-ended | 608 | 14 |
 
 Images are embedded in the `image` column as a JSON list of base64 strings, so
 the files are self-contained.
 
+## Input conditions
+
+Each track is evaluated under several input conditions. A condition changes only
+what the model is shown. The items, the reference answers, and the scoring code
+are identical across conditions within a track, so comparing two conditions
+isolates one source of performance.
+
+**T1, diagnosis**
+
+| Dataset name | Imaging findings text | Image |
+|---|---|---|
+| `MRareBench_Diagnosis` | withheld | shown |
+| `MRareBench_Diagnosis_FD` | disclosed | shown |
+| `MRareBench_Diagnosis_TO` | withheld | withheld |
+
+**T2, evidence verification**
+
+| Dataset name | Diagnosis given | Image |
+|---|---|---|
+| `MRareBench_EvidenceVerif` | yes | shown |
+| `MRareBench_EvidenceVerif_TO` | yes | withheld |
+| `MRareBench_EvidenceVerif_NoDx` | no | shown |
+
+Three contrasts follow from these conditions:
+
+- `Δ_sub = FD − LC` measures how much of the answer the withheld findings text
+  would have supplied. A large value indicates textual leakage.
+- `Δ_vis = LC − TO` measures how much the image itself contributes.
+- `Δ_grd = Img − Txt` measures grounding on T2, the gap between reporting
+  evidence with the image and reporting it without.
+
+Contrast metrics are more stable than absolute scores across reruns. Run-to-run
+drift is largely common-mode and cancels in the difference.
+
+## Evaluation with VLMEvalKit
+
+MRareBench ships as a dataset module for
+[VLMEvalKit](https://github.com/open-compass/VLMEvalKit). The module implements
+prompt construction, deterministic scoring, and the LLM-judge rubric for both
+tracks. This repository holds the data only. The evaluation code lives in
+VLMEvalKit, which keeps a single source of truth for scoring.
+
+The upstream integration is under review. Until it lands, install from the fork
+that carries the module:
+
+```bash
+git clone https://github.com/junzhin/VLMEvalKit_official.git VLMEvalKit
+cd VLMEvalKit && pip install -e .
+```
+
+### Running
+
+The module resolves data local-first. Point `LMUData` at a directory that
+already holds the files under `MRareBench/`, and nothing is downloaded:
+
+```
+$LMUData/MRareBench/diagnosis/diagnosis_opened.tsv
+$LMUData/MRareBench/evidence_verification/evidence_verification_opened_FULL609.tsv
+```
+
+Point `LMUData` at an empty directory instead, and the module fetches the same
+two files from this repository on first use.
+
+```bash
+export LMUData=/path/to/LMUData
+export OPENAI_API_KEY=...        # used by the judge, and by API-served models
+
+python run.py --data MRareBench_Diagnosis     --model <model> --judge gpt-5.4-mini --mode all
+python run.py --data MRareBench_EvidenceVerif --model <model> --judge gpt-5.4-mini --mode all
+```
+
+Substitute any of the six dataset names above for `--data`. All names within a
+track read the same TSV, so switching conditions costs no extra download.
+
+Omitting `--judge` runs inference and the judge-free metrics only. On T1 that
+still yields the headline Recall and rank metrics, which are computed by matching
+against the reference diagnosis and its aliases. On T2 it yields the
+deterministic `det_*` metrics but not the rubric score.
+
+To resume an interrupted run, add `--reuse --reuse-aux all`. Per-item results are
+checkpointed, so completed items are skipped rather than re-inferred.
+
+### What is scored
+
+**T1** is judge-free at its core. The model returns a ranked list of ten
+diagnoses. Scoring reports `recall@{1,3,5,10}`, `MRR`, and `MR`, the mean rank of
+the first hit. An optional judge adds complementary per-dimension scores.
+
+**T2** asks the model to report the evidence visible in the images. The headline
+metric `t2_hierarchical_required_recall` comes from an LLM judge that marks each
+required evidence point as present or absent, then gates cross-image and
+diagnostic credit on the visual level below it. A parallel family of `det_*`
+metrics scores the same predictions without a judge, by lexical and embedding
+overlap against the reference rationale.
+
 ## Reading the files
 
 Use a CSV parser with quoting enabled. Do not split on newlines: most records
@@ -55,6 +154,32 @@ t2 = pd.read_csv('evidence_verification/evidence_verification_opened_FULL609.tsv
                  sep='\t', dtype=str)                                     # 608 rows
 ```
 
+Decoding an image:
+
+```python
+import base64, io, json
+from PIL import Image
+
+images = json.loads(t1.iloc[0]['image'])
+img = Image.open(io.BytesIO(base64.b64decode(images[0])))
+```
+
+## Reproducibility notes
+
+**Check the inference failure rate before reading any score.** Requests that
+carry images occasionally fail at the API layer, and a failed request is recorded
+as a wrong answer. In our runs the rate stayed near 1% and appeared only in
+image-bearing conditions; the text-only conditions had none. The failures
+concentrate on a fixed handful of multi-image items whose payloads are large.
+Inspect `infer_fail_rate` in the result summary. A non-zero value calls for a
+rerun with `--reuse --reuse-aux all` to fill the gaps.
+
+**`temperature=0` does not give bit-identical reruns.** The nondeterminism sits
+on the serving side: batching boundaries, nondeterministic kernels, MoE routing,
+and silent model-snapshot updates. Absolute scores drift by one to three points
+between runs of the same model. The contrast metrics drift less, because the
+same shift enters both terms and cancels.
+
 ## Notes
 
 **Not for clinical use.** A score here is not evidence of clinical safety or