Author

Raphaël Bonnet-Guerrini

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Preprint Aug 2026

Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

The first application of sparse-autoencoder-based mechanistic interpretability to particle physics suggests that mechanistic interpretability can reveal learned latent physics encoded within a model's internal representation and help design downstream tasks that exploit it.

Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, I. Timiryasov et al. · 0 citations

Jul 2026

Multiclass Classification without Labels via Posterior Simplex Geometry

Classification without Labels (CWoLa) shows that, in the binary case, a classifier trained to distinguish two impure mixtures with different class proportions can recover an optimal class discriminator without knowing the mixture proportions, and proposes prior-free procedures that train a standard classifier to distin...

Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Troels C. Petersen et al. · arXiv.org · 1 citation

Review Jul 2026

Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification

The results show that injection-driven, weakly supervised training can enable scalable and consistent Real-Bogus classification without human-labeled training data while providing calibrated uncertainties, and achieves competitive calibration relative to more expensive ensemble baselines.

Raphaël Bonnet-Guerrini, B. S'anchez, D. Fouchez et al. · 1 citation