13 papers · 1 filter
CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability
Pratinav Seth, Hem Gosalia, Aditya Kasliwal +1
Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such an…
Distilling Tabular Foundation Models for Structured Health Data
Aditya Tanna, Nassim Bouarour, Mohamed Bouadi +2
Tabular foundation models (TFMs) achieve strong performance on health datasets, but their inference cost and infrastructure requirements limit practical use. We study whether their…
Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
Aditya Tanna, Yash Desai, Pratinav Seth +3
Tabular foundation models (TFMs) now match or beat tuned gradient-boosted trees on a growing fraction of tabular tasks, but no single TFM wins on every dataset. Ensembling is the g…
Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees
Aditya Tanna, Nassim Bouarour, Mohamed Bouadi +2
A fraud scorer needs to answer in under 2 ms. The best tabular foundation models (TFMs) take 151-1,275 ms on GPU. We close this gap by distilling the TFM offline into an XGBoost or…
Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
Aditya Tanna, Mitul Solanki, Mohamed Bouadi +3
Credit default prediction is a tabular learning problem with severe class imbalance, heterogeneous features, and tight latency budgets. Tabular Foundation Models (TFMs) approach th…
Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands
Pratinav Seth, Vinay Kumar Sankarapu
This position paper argues that behavioural assurance, even when carefully designed, is being asked to carry safety claims it cannot verify. AI governance frameworks enacted betwee…