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Life sciences · Preprint
arXiv · August 19, 2026
Early or partial results. Treat as a signal, not a conclusion.
GEAR is a novel two-stage distillation framework for compressing tabular foundation models into lightweight predictors. The authors report performance gains on benchmark datasets and substantial reductions in inference latency and memory, but the work is unpublished, lacks external validation, and is not clinically grounded.
Preprint. Tabular datasets from TALENT and TabArena benchmark suites; no human subjects.. Intervention: GEAR two-stage distillation framework with synthetic covariate expansion and real-label re-anchoring.. Compared with: Supervised MLPs, real-data-only distillation, CatBoost, LightGBM, XGBoost..
Two-stage MLPs outperform supervised MLPs by 1.81–2.00 AUC points on binary tasks and 1.19–1.35 points on multiclass tasks Additional gains over real-data-only distillation of 1.76–2.19 AUC points (binary) and 2.09–2.40 points (multiclass) GEAR reduces median inference time by 57–2866 times and peak prediction memory by 1.9–3.3 times
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This is an unrefereed preprint presenting a novel machine-learning method with experimental validation on benchmark datasets, but lacks peer review, clinical validation, or independent replication.
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Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering large-scale deployment. We propose GEAR (\emph{Generative Expansion and Real Anchoring}), a modular two-stage framework that distills TFMs into lightweight MLP or tree-based predictors that can be deployed on commodity CPUs. Stage 1 uses synthetic covariates solely as teacher-query locations and trains the student on soft TFM targets, expanding coverage beyond observed rows. Stage 2 re-anchors the student to the target distribution using real labels and out-of-fold teacher predictions, whitch avoids self-labeling leakage. We further derive a risk certificate characterizing the trade-off between generated-query volume and generator fidelity. Experiments on TALENT and TabArena demonstrate the broad applicability of GEAR. Two-stage MLPs outperform supervised MLPs by 1.81--2.00 AUC points on binary tasks and 1.19--1.35 points on multiclass tasks, with additional gains over real-data-only distillation of 1.76--2.19 and 2.09--2.40 points, respectively. On binary tasks, the gains also transfer to LightGBM and XGBoost, and all three student families outperform CatBoost, the strongest non-TFM baseline, in mean AUC. Ablations show gains beyond longer training or alternative warm starts, greater stability from staged than mixed optimization, and generator-dependent diminishing returns as query volume increases. Finally, GEAR reduces median inference time by 57--2866 times and peak prediction memory by 1.9--3.3 times, while retaining higher AUC than matched supervised baselines.
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