AIarXiv
Ranked by formulaVerdict: NotableSurvICL: a Foundation Model for in-context Survival Analysis
Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression.
Key numbers
- 82.9 % of pairwise comparisons in
- 71.3 % for the runner-up
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression. Yet in survival analysis, which predicts when an event occurs from censored data, often on small clinical cohorts, we find that no existing TFM beats the half-century-old Cox model. We address this issue by introducing SurvICL, a TFM that predicts survival curves in-context. SurvICL is pretrained on purely synthetic datasets generated from a novel flexible proportional-odds prior that outperforms other prior types in our ablation study. Unlike most other survival models, SurvICL is trained with a plain quantile regression loss, which is possible because its synthetic pre-training data generator can provide uncensored event times. Additionally, we introduce the largest survival benchmark to date spanning 103 datasets. On the 63 datasets held out from all design decisions, SurvICL outperforms 20 tree-based, deep, and foundation-model baselines without any tuning: it wins 82.9% of pairwise comparisons in integrated Brier score, against 71.3% for the runner-up, the Cox model, and is the first model to rank above Cox on small datasets.
The editor's rubric
- Leverage4 / 5
- A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
- Magnitude3 / 5
- Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
- Evidence3 / 5
- Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
- Novelty3 / 5
- A genuinely new approach to an open problem.
- Trajectory3 / 5
- A clear path to scale.
- Stakes2 / 5
- Benefits a professional community (practitioners, clinicians, engineers).
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, we report); breadth (many tasks, zero/few-shot); gains (outperforms); firsts (first); novelty (alternative to status quo); verification (multiple benchmarks, ablations, held-out test); stakes (mortality).
How the score was computed
Desks place stronger verdicts first, then use this score to order work within the same verdict.
- Merit
- 6.3 / 10
- Adjusted merit
- 5.1 / 10
- Attention
- 0%
- Freshness
- 40%
- Citations0 (reference 15, via semantic-scholar, Oct 18, 2026, 13:49 UTC)
- Influential citations0 (reference 3, via semantic-scholar, Oct 18, 2026, 13:49 UTC)