Portrait de Diego Calanzone

Diego Calanzone

diego.calanzone@mila.quebec
Doctorat - UdeM
Superviseur⋅e principal⋅e
Pierre-Luc Bacon
Sujets de recherche
Apprentissage par renforcement
Biologie computationnelle
Site web
Google Scholar
GitHub

Publications

Generative Meta-Models are Adversarial Classifiers
Diego Calanzone
Pietro Greiner
Pierre-Luc Bacon
We study adversarial prompt detection in LLMs through the geometry of intermediate activations. Our hypothesis is that token-level adversari… (voir plus)al attacks induce hidden states that lie outside the activation manifold associated with typical prompts. To test this, we use Generative Latent Priors (GLP), diffusion-based meta-models trained on LLM residual stream activations, as proxies for this manifold (Luo et al., 2026). We evaluate several training-free anomaly scores derived from GLP, namely Guard-GLP, including reconstruction error, diffusion time estimation, and a density-based estimate using Hutchinson trace estimation. Empirically, GLP reconstruction error separates benign prompts from adversarial prompts and provides a competitive classifier without supervised fine-tuning. We also study GLP as a regularizer for activation steering, showing that denoising edited activations can reduce attack success while limiting over-refusal and nonsensical generations. Overall, our results suggest that generative priors over LLM activations provide a useful interface for both adversarial prompt detection and safer activation-level interventions.
2026-08-04
AIW @ Conference on Language Modeling (publié)
openreview.net
openreview.net
Discovery of Sustainable Refrigerants through Physics-Informed RL Fine-Tuning of Sequence Models
Adrien Goldszal
Diego Calanzone
Vincent Taboga
Pierre-Luc Bacon
Most refrigerants currently used in air-conditioning systems, such as hydrofluorocarbons, are potent greenhouse gases and are being phased d… (voir plus)own. Large-scale molecular screening has been applied to the search for alternatives, but in practice only about 300 refrigerants are known, and only a few additional candidates have been suggested without experimental validation. This scarcity of reliable data limits the effectiveness of purely data-driven methods. We present Refgen, a generative pipeline that integrates machine learning with physics-grounded inductive biases. Alongside fine-tuning for valid molecular generation, Refgen incorporates predictive models for critical properties, equations of state, thermochemical polynomials, and full vapor compression cycle simulations. These models enable reinforcement learning fine-tuning under thermodynamic constraints, enforcing consistency and guiding discovery toward molecules that balance efficiency, safety, and environmental impact. By embedding physics into the learning process, Refgen leverages scarce data effectively and enables de novo refrigerant discovery beyond the known set of compounds.
2026-03-14
AI4X @ Neural Information Processing Systems (présentation orale)
doi.org
openreview.net
Mol-MoE: Training Preference-Guided Routers for Molecule Generation
Diego Calanzone
Pierluca D'Oro
Pierre-Luc Bacon
Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on… (voir plus) single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability.
2025-03-04
ICLR.cc/2025/Workshop/GEM (publié)
doi.org
openreview.net