A censoring-aware target interface for tabular foundation models in survival prediction

Steven H. Lin; Xuelin Huang; Yue Lyu; Ziyi Li

REVIEW 2 major objections 5 minor 30 references

A censoring-aware target interface for tabular foundation models in survival prediction

T0 review · 2 major / 5 minor · reviewed 2026-07-13 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Jackknife pseudo-RMST targets convert right-censored survival outcomes into ordinary regression labels so tabular foundation models can predict restricted event-free time without survival-specific training.

desk verdict Solid methods bridge: jackknife pseudo-RMST as a portable target interface for frozen tabular foundation models, with known-truth recovery and honest multi-backbone benchmarks. read the letter →

arxiv 2607.09577 v1 pith:WOQ7PM65 submitted 2026-07-10 stat.ME stat.ML

Yue Lyu , Steven H. Lin , Xuelin Huang , Ziyi Li This is my paper

classification stat.MEstat.ML

keywords restrictedmeansurvivaltimepseudo-observationstabularfoundationmodelsrightcensoringpredictionjackknifeRMSTregressioncensoring-awaretargets

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Right-censored follow-up blocks ordinary regression because observed time is not a complete event-time label. This paper claims that the survival-specific work can live in the target rather than the model: construct jackknife pseudo-observation labels for restricted mean survival time (RMST) at a chosen horizon, then hand those labels to ordinary tabular foundation-model backbones. In simulations with known conditional RMST, those pseudo-RMST targets recovered restricted event-free time more accurately than naive restricted observed-time or event-only labels, and multiple SurvFM backbones stayed competitive with classical survival and RMST-regression methods on an eligible static SurvSet subset. Predicted RMST also sorted held-out patients into groups with ordered observed event-free time and event enrichment. A sympathetic reader would care because many biomedical tables are modest, heterogeneous, and censored, and a portable target interface reuses reusable tabular predictors instead of rebuilding a survival-specific model for every cohort.

What carries the argument

Jackknife pseudo-RMST targets: patient-level labels Zi(τ) = n θ̂τ(D) − (n−1) θ̂τ(D−i) formed from the full-sample and leave-one-out Kaplan–Meier RMST estimators. They convert a fold-level censored-data functional into regression responses whose conditional expectation approximates conditional RMST, so standard tabular regression becomes a meaningful horizon-specific survival task.

What would settle it

Build a controlled simulation with known conditional RMST but informative censoring (censoring depends on unobserved factors tied to the event). If pseudo-RMST targets then fail to cut oracle RMST error relative to naive min(Y,τ) labels and discrimination collapses, the claim that this target interface is a general portable bridge fails in that regime.

Watch

Extended reading notes

Core claim

SurvFM-RMST establishes that censoring can be handled through a target interface: jackknife pseudo-observations for RMST at a prespecified horizon turn right-censored follow-up into patient-level regression responses, so interchangeable tabular foundation-model backbones can perform horizon-specific restricted event-free-time prediction without survival-specific fine-tuning, risk-set losses, or new survival-output heads. Controlled simulations with known conditional RMST and a target-definition ablation support that the pseudo-RMST construction is substantive rather than cosmetic, and public-data benchmarks show competitive discrimination and RMST-scale error across several backbones, with r

Load-bearing premise

The method assumes that leave-one-out RMST pseudo-labels act as usable noisy patient-level targets for true restricted mean survival time when censoring is independent of the event process and finite-sample risk sets are not too thin.

Editorial extensions

If this is right

Censored survival tables can be fed to existing tabular foundation-model regressors by changing only the outcome construction, not the backbone architecture.
Horizon-specific RMST predictions give restricted event-free-time outputs on the original time scale that can stratify held-out patients into ordered risk groups with event enrichment.
The survival-specific component becomes a reusable target recipe, so multiple tabular backbones can be swapped under the same censoring-aware interface.
Pseudo-RMST labels outperform naive restricted observed-time and event-only labels for recovering known conditional RMST.
Relative model ranking remains endpoint-, horizon-, and constraint-dependent, so validation inside the intended setting still decides backbone choice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same target-side pattern could be applied to competing risks by building pseudo-observations for cause-specific restricted mean time or cumulative incidence, extending the interface without redesigning foundation backbones.
If the interface is truly portable, it should also drop into classical tabular regressors and AutoML stacks, not only foundation models—a low-cost check of how much of the gain is the target versus the backbone.
Refreshing pseudo-RMST at successive landmarks as new covariates arrive would test whether the static interface can become a lightweight route to time-updated prognosis.
A diagnostic that flags thin risk sets or near-informative censoring before target construction would be a practical guardrail for the load-bearing assumption.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The one thing worth knowing is that this is a clean target-side fix, not a new survival architecture. They convert right-censored outcomes into jackknife pseudo-RMST labels so ordinary tabular foundation-model regressors (TabPFN-v3, TabICL, TabDPT, TabH2O, MITRA) can do horizon-specific restricted event-free-time prediction without survival losses or fine-tuning.

What is actually new is the packaging and the evidence, not the ingredients. Pseudo-observation RMST regression is classical (Andersen et al.); tabular foundation models are recent. The contribution is showing that the same pseudo-RMST interface is portable across several frozen backbones, with known conditional-RMST recovery in 24 simulation settings and a same-backbone ablation where pseudo-RMST beats min(Y,τ) and event-only labels on both oracle RMSE and Uno C. Public results on 36 eligible static SurvSet datasets put several SurvFM backbones in close leading bands with RSF, AFT, DeepSurv and RMST-regression baselines, with explicit denominators for TabH2O column limits and non-evaluable IPCW blocks. They also report reduced-training ranks, practical runtime/coverage profiles, and held-out tertile stratification with ordered KM gaps. Citation pattern is appropriate; concurrent survival-native and representation-side work is positioned as complementary rather than ignored.

Soft spots are real but proportionate. The load-bearing premise remains the classical finite-sample pseudo-observation approximation under independent censoring (their Eq. 7); they state it, clip targets to [0,τ], and the ablation helps, but informative censoring or tiny risk sets would still break the labels. Scope is static baseline covariates and fixed horizons; the SurvSet subset is eligibility-filtered, not the full repository; configs are fixed rather than exhaustively tuned; public code/data are promised rather than fully released. None of that overturns the portability claim.

This is for people who already want tabular foundation models on modest censored cohorts and need a defensible label construction. It deserves a serious referee. I would engage with it and expect it to clear peer review with ordinary methods revisions.

Referee Report

2 major / 5 minor

Summary. The manuscript introduces SurvFM-RMST, a target-interface framework that converts right-censored survival outcomes into jackknife pseudo-observation labels for restricted mean survival time (RMST) at a fixed horizon τ, then feeds those labels to interchangeable tabular foundation-model backbones (TabPFN-v3, TabICL, TabDPT, TabH2O, MITRA) for ordinary horizon-specific regression without survival-specific fine-tuning or new output heads. Controlled simulations with known conditional RMST (24 settings, five replicates) show accurate oracle recovery and a same-backbone ablation in which pseudo-RMST targets reduce oracle RMSE and improve Uno’s C-index relative to min(Y,τ) and event-only labels. On 36 eligible static SurvSet datasets the SurvFM backbones are competitive with RMST-regression and classical survival comparators on discrimination and tau-normalized IPCW RMST error, with performance varying by horizon, endpoint and practical constraints; predicted-RMST tertiles also separate held-out Kaplan–Meier trajectories and enrich events. The authors position the contribution as a portable censoring-aware interface rather than a universally superior survival model.

Significance. If the results hold, the paper supplies a practical, theory-grounded route for applying existing tabular foundation models to right-censored biomedical data without redesigning their architectures or losses. The combination of known-truth oracle recovery, a clean same-backbone target ablation, multi-backbone public benchmarks with explicit denominators, and transparent practical profiles (column limits, runtimes, reduced-training ranks) is stronger evidence than is typical for early foundation-model survival adaptations. The work is complementary to survival-native and representation-side approaches and is likely to be useful to methodologists and applied groups that already rely on TabPFN-style tools. Scope limitations (static covariates, fixed horizons, eligible SurvSet subset, fixed hyperparameters) are stated clearly and do not erase the portability claim.

major comments (2)

Methods §4.3, Eq. (7) and the target-ablation design (Results 2.2, Fig. 2d): the central portability claim rests on the classical approximation E{Zi(τ;D)|Xi=x}=μτ(x)+op(1) under independent/non-informative censoring. The same-backbone ablation already shows clear gains over naive targets, and the authors clip labels to [0,τ], but the manuscript still lacks a finite-sample diagnostic (e.g., bias of leave-one-out RMST as a function of risk-set size or censoring rate) that would let readers judge when the approximation is adequate. A short simulation or real-data sensitivity panel quantifying this would strengthen the load-bearing premise without changing the framework.
Results 2.4–2.5 and Methods §4.11–4.12: public-data conclusions are drawn from an eligible static SurvSet subset (36 of 77 screened) plus a limited tau80 scalability sensitivity on 11 scale-excluded datasets. While denominators and non-imputation are reported carefully, the main text still risks over-generalization (“competitive \ldots across 36 eligible static SurvSet datasets”). Explicitly framing the benchmark as a static, size- and dimensionality-filtered subset in the abstract and Results opening, and stating that dynamic/longitudinal/competing-risk settings remain out of scope, would keep the claim proportionate to the evidence.

minor comments (5)

Figure 2a caption notes a zoomed error axis; the main text should also flag that full-range tails appear only in Supplementary Fig. 1a so readers do not misread dispersion.
Methods §4.5–4.6 list package versions and fixed hyperparameters; a single compact table of all backbone and comparator configurations would improve reproducibility scanning.
The distinction between oracle conditional-RMST RMSE and realized restricted-time error (Eqs. 11–12) is clear in Methods but could be restated briefly when first used in Results 2.2.
Supplementary Table 6 design-choice audits are appropriately scoped as non-main, but a one-sentence pointer in the Discussion would help readers locate them.
Minor notation consistency: both Zi(τ;D) and zi(τ) appear; standardizing on one form would reduce friction.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: oracle RMST recovery and same-backbone target ablation are independent of the fitted backbones; classical pseudo-observation theory is external.

full rationale

SurvFM-RMST's central claim is that jackknife pseudo-RMST targets form a portable interface for frozen tabular backbones. The derivation chain is: (i) define conditional RMST μ au(x) (Eq. 2); (ii) construct jackknife Zi( au;D) from KM-RMST (Eqs. 5–6); (iii) invoke classical pseudo-observation theory that E{Zi|Xi=x}=μ au(x)+op(1) under independent censoring (Eq. 7, citing Andersen et al.); (iv) regress Zi with interchangeable tabular backbones; (v) evaluate against known conditional RMST in simulation and against external SurvSet comparators. Step (iii) is an asymptotic approximation from the literature, not a self-definition. Simulation oracle RMSE uses the known data-generating μ au, independent of any fitted model. The target ablation holds the backbone fixed and varies only the label, so gains for pseudo-RMST over min(Y, au) and event-only targets are not forced by construction. Public benchmarks use external datasets and standard survival/RMST baselines. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors, and no self-citation is load-bearing for the interface claim. The only mild loop is the usual methods-paper framing that the interface works because the evaluations show it works; that does not make the results circular. Score 1 reflects that ordinary self-contained methods paper structure, not a reduction of the claim to its inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The framework rests on classical survival estimands and pseudo-observation theory plus practical modeling choices. No new physical entity is postulated. Load-bearing domain assumptions are independent/non-informative censoring and the asymptotic pseudo-observation approximation. Free choices include the restriction horizon, clipping, fixed learner configs, and eligibility filters that define the public benchmark subset.

free parameters (4)

restriction horizon τ (tau50/tau80/tau90 quantiles of training uncensored events)
Horizon is part of the prediction question and is chosen from training-fold event-time percentiles; rankings can change with τ.
pseudo-RMST and prediction clipping to [0,τ]
Finite-sample jackknife targets can leave the natural RMST range; clipping is a pragmatic harness choice, not classical theory.
fixed backbone and comparator hyperparameters (e.g., RF 200 trees, TabICL n_estimators=8, DeepSurv architecture)
Benchmark uses fixed configs rather than exhaustive per-dataset tuning, so absolute rankings depend on these choices.
SurvSet eligibility filters (static, size, dimensionality, loading/runtime constraints)
36 of 77 screened datasets enter the main public benchmark; the subset definition affects reported competitiveness.

assumptions (5)

domain assumption Independent or non-informative censoring and regularity conditions under which jackknife pseudo-observations target conditional RMST.
Invoked in Methods §4.3 and classical citations [18–20]; without it, Zi(τ;D) is not a valid regression response for μτ(x).
domain assumption E{Zi(τ;D)|Xi=x}=μτ(x)+op(1): pseudo-observations approximate conditional RMST asymptotically, not as a finite-sample identity.
Stated explicitly as Eq. 7; finite-sample noise and bias remain.
standard math Conditional RMST equals the optimal squared-error regression target for min(T,τ) under complete observation.
Eqs. 2–4 motivate RMST regression as mean restricted event time.
domain assumption Kaplan–Meier RMST functional on the training fold is an adequate plug-in for jackknife target construction.
Eqs. 5–6 define Zi from full-sample and leave-one-out KM RMST estimators.
ad hoc to paper Static baseline covariates and a single fixed horizon suffice for the intended prediction task; no longitudinal/dynamic/competing-risk structure is required.
Study design and Discussion deliberately scope the framework to static tabular RMST prediction.

invented entities (1)

SurvFM-RMST target-interface framework independent evidence
purpose: Name the shared workflow that pairs jackknife pseudo-RMST labels with interchangeable tabular foundation-model backbones.
A methodological packaging, not a new physical object; independent evidence is the simulation and SurvSet evaluations rather than an external measurement of a postulated entity.

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Cite this review

Pith. "Pith review of A censoring-aware target interface for tabular foundation models in survival prediction." pith.science (2026). https://pith.science/paper/WOQ7PM65

@misc{pith2026260709577,
  author       = {Pith},
  title        = {Pith review of: A censoring-aware target interface for tabular foundation models in survival prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WOQ7PM65}},
  note         = {Machine review of arXiv:2607.09577}
}

read the original abstract

Time-to-event prediction from tabular patient data is central to prognosis and biomedical decision support, but right-censored follow-up prevents direct use of ordinary regression labels. Tabular foundation models offer reusable prediction machinery for modest heterogeneous datasets, yet they generally assume fully observed outcomes. We introduce SurvFM-RMST, a censoring-aware target-interface framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time, enabling multiple tabular backbones to perform horizon-specific RMST regression without survival-specific fine-tuning. In controlled simulations with known conditional RMST, SurvFM-RMST recovered restricted event-free time accurately, and pseudo-RMST targets outperformed naive restricted observed-time and event-only targets. Across 36 eligible static SurvSet datasets, SurvFM backbones were competitive with established survival and RMST-regression comparators, though relative performance varied by endpoint, horizon and practical constraints. Predicted RMST further stratified held-out patients into groups with ordered observed event-free time and event enrichment. Overall, the results support pseudo-RMST target construction as a portable interface between censored survival data and tabular foundation-model prediction.

Figures

Figures reproduced from arXiv: 2607.09577 by the authors.

**Figure 1.** SurvFM-RMST framework for converting censored survival data into patientlevel restricted event-free-time predictions. (a) Right-censored tabular survival data consist of baseline features, observed follow-up time, Yi = min(Ti , Ci), and event indicator, ∆i , where event times after censoring are unknown. (b) At a restriction horizon τ , restricted mean survival time (RMST) is represented as the area under an estima… view at source ↗

**Figure 2.** Controlled simulation benchmark and pseudo-RMST target-interface validation. (a) Distribution of oracle conditional-RMST prediction error in controlled simulations at tau80 and tau90. Simulations spanned 24 settings defined by signal structure, sample size, feature count, censoring and noise regimes. Lower root mean squared error indicates better recovery of the known conditional RMST function. The displayed error … view at source ↗

**Figure 3.** Simulation-regime robustness across controlled data settings. (a) Mean tau80 oracle-RMST-error rank by signal structure across 24 controlled simulation settings, defined by four signal structures and six data-regime patterns with five replicates per setting. Points show mean ranks and horizontal bars show the interquartile range of block-level ranks. Rank 1 indicates the lowest oracle conditional-RMST RMSE. Displaye… view at source ↗

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D. R. Cox. Regression models and life-tables.Journal of the Royal Statistical Society: Series B, 34(2):187–202, 1972

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