12 citations · 16 across the 15 of their papers we have counts for
7 papers · 1 filter
Localized Anomaly Detection via Differentiable D-vine Copulas
Nicholas Andrea Pearson, Francesca Zanello, Davide Russo +2
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine require…
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
Sara Candussio, Daniel Scalena, Luca Bortolussi +3
Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and hi…
Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
Daniel Scalena, Sara Candussio, Luca Bortolussi +3
Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individual steps on the final answer remains po…
RogueAI: A Reverse Turing Test for Detecting Licensed AI Deception in Dialogue
Sara Candussio, Emanuele Ballarin, Lorenzo Bonin +2
The original Turing Test asks a human judge to distinguish a machine from a person through dialogue. Three quarters of a century later, conversational systems pass this test in cas…
Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
Lorenzo Bonin, Francesco Giacomarra, Luca Bortolussi +2
The rapid advancement of autonomous driving (AD) technologies has outpaced the development of robust safety evaluation methods. Conventional testing relies on exposing AD systems t…
Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
Sara Candussio, Gabriele Sarti, Gaia Saveri +1
We introduce a framework for learning continuous neural representations of formal specifications by distilling the geometry of their semantics into a latent space. Existing approac…
DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling
Francesca Randone, Romina Doz, Mirco Tribastone +1
We present DeGAS, a differentiable Gaussian approximate semantics for loopless probabilistic programs that enables sample-free, gradient-based optimization in models with both cont…