Pith Number

pith:UGCKUJDE

pith:2026:UGCKUJDELLQRCJ4VSYBMQ7GX2A

not attested not anchored not stored refs resolved

Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks

Daniel Rueckert, Dmitrii Seletkov, Georgios Kaissis, Paul Hager, Raphael Rehms, Rickmer Braren

A model pretrained only on synthetic survival data delivers Bayesian individualized predictions in one forward pass on real datasets.

arxiv:2603.29475 v2 · 2026-03-31 · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{UGCKUJDELLQRCJ4VSYBMQ7GX2A}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

SIC is trained to approximate Bayesian posterior predictive inference under the synthetic survival prior, enabling individualized survival prediction in a single forward pass, requiring no task-specific training or hyperparameter tuning.

C2weakest assumption

The rich synthetic survival prior with explicit control over covariates and time-event distributions is representative enough of real-world data distributions that the amortized posterior predictive generalizes without task-specific adaptation.

C3one line summary

SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.

References

19 extracted · 19 resolved · 2 Pith anchors

[1] Stat Med , author = 2005 · doi:10.1002/sim.5452

[2] On the Opportunities and Risks of Foundation Models 2059 · doi:10.1002/sim.2059

[3] cc/paper_files/paper/2020/file/ 1457c0d6bfcb4967418bfb8ac142f64a-Paper 2020

[4] Regression Models and Life-Tables 2023 · doi:10.1111/j.2517-6161.1972.tb00899.x

[5] lifelines: survival analysis in python.Journal of Open Source Software, 4(40): 1317 2019 · doi:10.21105/joss.01317

Formal links

2 machine-checked theorem links

Cited by

3 papers in Pith

Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference

TabPFN-3: Technical Report

Receipt and verification

First computed	2026-05-18T02:44:30.595831Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a184aa24645ae11127959602c87cd7d00cee92376dadc7a80b40d8342127e7a4

Aliases

arxiv: 2603.29475 · arxiv_version: 2603.29475v2 · doi: 10.48550/arxiv.2603.29475 · pith_short_12: UGCKUJDELLQR · pith_short_16: UGCKUJDELLQRCJ4V · pith_short_8: UGCKUJDE

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UGCKUJDELLQRCJ4VSYBMQ7GX2A \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a184aa24645ae11127959602c87cd7d00cee92376dadc7a80b40d8342127e7a4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4d90155b049e6cb59585f9e34db6cd730ea74c633cce9c6683b90be0e1de7bed",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-31T09:22:52Z",
    "title_canon_sha256": "9dfd7855259fb3daa2e766b9ce29ca63c8021e584f6c1c8329edbfd3c0e2ba97"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.29475",
    "kind": "arxiv",
    "version": 2
  }
}