Thomas Sommariva — accepted AI-conference papers

2026

Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature

Angelo Porrello, Pietro Buzzega, Felix Dangel, Thomas Sommariva, Riccardo Salami, Lorenzo Bonicelli +1

ICLR 2026poster

Task Arithmetic yields a modular, scalable way to adapt foundation models. Combining multiple task vectors, however, can lead to cross-task interference, causing representation drift and degraded performance. Representation drift regularization provides a natural remedy to disentangle task vectors;…

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2026

Distilling Linearized Behavior into Non-linear Fine-Tuning for Effective Task Arithmetic

Thomas Sommariva, Francesca Morandi, Simone Calderara, Angelo Porrello

ICML 2026poster

Task vector composition has emerged as a promising paradigm for editing pre-trained models, enabling model merging via addition and task removal via subtraction. Fine-tuning in the tangent space of a pre-trained model (*linearized fine-tuning*) has proven particularly effective in this setting, as i…

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