MRareBench: A Multimodal Rare-Disease Benchmark for Evidence–Diagnosis Correspondence

Benchmark data accompanying the paper, released for review.

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	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. 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:

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.

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 contain newline characters inside their text fields, so the physical line count far exceeds the record count.

import pandas as pd
t1 = pd.read_csv('diagnosis/diagnosis_opened.tsv', sep='\t', dtype=str)   # 300 rows
t2 = pd.read_csv('evidence_verification/evidence_verification_opened_FULL609.tsv',
                 sep='\t', dtype=str)                                     # 608 rows

Decoding an image:

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 diagnostic validity.

Source articles are open access and carry their own per-article licenses.