| # RareBench-BR Trajectory v2 — Leaderboard |
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| 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. |
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| ## Headline: GEMEO leads every novelty & long-context task |
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| | 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** | |
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| 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). |
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| ## T2 / T4 / T5 — long-context tasks (linear probe) |
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| 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. |
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| | 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** | |
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| 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. |
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| ## New-onset — predicting genuinely new events |
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| 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. |
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| ## T1 — Next-procedure @ transition (1-step Markov) |
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| Candidate space: procedure tokens. Repeat-last is the autocorrelation oracle — |
| a healthy benchmark keeps it LOW. |
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| | 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 ✓ | |
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| 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. |
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| ## Interpreting these results |
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| - 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. |
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| *Maintained by Raras AI. Contact: dimas@raras.ai.* |
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