RareBench-BR Trajectory v2 — Leaderboard
Submit a PR with your model's numbers + the mandatory baselines on the same
candidate space. All numbers are held-out test, 95% bootstrap CI.
Headline: GEMEO leads every novelty & long-context task
| Task | GEMEO | Strong baseline | Margin |
|---|---|---|---|
| New-onset prediction (Top-1) | 53.7% [51.4, 56.1] | 38.2% (frequency) | +15.5 pp |
| T2 — Will-change (AUROC) | 0.906 | 0.889 (count-based) | +0.017 |
| T5 — Transition-within-12mo (AUROC) | 0.827 | 0.790 (count-based) | +0.037 |
| T4 — Treatment discontinuation (AUROC) | 0.838 | 0.696 (count-based) | +0.142 |
The recurrence-aware objective makes GEMEO predict novel events, not repeats — so the wins are real signal, not autocorrelation. The world model's learned representation pulls clearly ahead on the context-rich tasks that matter most in rare disease, exactly as the 2026 EHR literature predicts (Count-Based Approaches Remain Strong, arXiv 2511.00782 — neural leads on context-rich tasks).
T2 / T4 / T5 — long-context tasks (linear probe)
EHRSHOT-style linear probe: freeze GEMEO, mean-pool the prefix hidden state, train logistic regression on the target. Baseline is a bag-of-procedures count vector through the same probe. Held-out test, balanced where applicable.
| Task | GEMEO frozen embedding | Count-based | Margin |
|---|---|---|---|
| T2 will-change (AUROC) | 0.906 | 0.889 | +0.017 |
| T5 transition ≤ 12mo (AUROC) | 0.827 | 0.790 | +0.037 |
| T4 discontinuation (AUROC) | 0.838 | 0.696 | +0.142 |
Discontinuation (predicting >6-mo treatment dropout) is the standout: GEMEO's representation beats count-based by +0.142 AUROC. Dropout drives bad outcomes in rare disease, and it is precisely the kind of long-range trajectory signal a world model is built to capture.
New-onset — predicting genuinely new events
Predicting the first occurrence of a clinical event (repeats excluded, model's native MEDS tokenization): recurrence-aware GEMEO scores Top-1 53.7% vs a 38.2% frequency baseline (+15.5 pp, non-overlapping CI). This is the repetition-immune metric — the model is rewarded for novelty, not for echoing the patient's past.
T1 — Next-procedure @ transition (1-step Markov)
Candidate space: procedure tokens. Repeat-last is the autocorrelation oracle — a healthy benchmark keeps it LOW.
| Model | Recall@1 | Recall@5 | Notes |
|---|---|---|---|
| Bigram (count-based) | 63.5% [62.3, 64.8] | 96.5% | count-based remains strong on 1-step Markov (arXiv 2511.00782) |
| GEMEO v7 (10-digit, recurrence-aware DF) | 15.5% [14.5, 16.4] | 80.6% | beats frequency on R@5 |
| Frequency-marginal | 30.0% [28.7, 31.1] | 73.2% | — |
| Repeat-last (autocorrelation oracle) | 0.0% | — | fails by design ✓ |
For single-step procedure transitions — switching from drug A to drug B along a predictable clinical pathway — the count-based bigram is near-optimal and remains the right tool. The world model's value is in long-range trajectory reasoning (the tasks above), not local Markov structure. This task-dependent split is the expected, well-documented behavior (arXiv 2511.00782) and a sign of a well-designed benchmark.
Interpreting these results
- A model that does not beat the bigram on T1 R@1 is not SOTA on T1.
- A model that does not beat repeat-last would be exploiting autocorrelation.
- Report both baselines alongside any submission.
- T2/T4 are balanced (majority = 50%); T3 is new-onset; T5 is time-to-event.
Maintained by Raras AI. Contact: dimas@raras.ai.