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On the role of connectivity in Linear Logic proofs
Raffaele Di Donna, Lorenzo Tortora de Falco
https://arxiv.org/abs/2506.21678 https://

On the role of connectivity in Linear Logic proofs
We investigate a property that extends the Danos-Regnier correctness criterion for linear logic proof-structures. The property applies to the correctness graphs of a proof-structure: it states that any such graph is acyclic and that the number of its connected components is exactly one more than the number of nodes bottom or weakening. This is known to be necessary but not sufficient in multiplicative exponential linear logic (MELL) to recover a sequent calculus proof from a proof-structure. We…

Contact loci of semihomogeneous singularities
Eduardo de Lorenzo Poza, Jiahui Huang
https://arxiv.org/abs/2506.18805 https://arxiv.or…

Contact loci of semihomogeneous singularities
Two of the main open problems in the theory of contact loci are the arc-Floer conjecture, which states that the compactly supported cohomology of the restricted $m$-contact locus and the fixed-point Floer cohomology of the $m$-th iterate of the Milnor monodromy are isomorphic up to a shift; and the embedded Nash problem, which asks for a description of the irreducible components of the unrestricted $m$-contact locus in terms of an embedded resolution of singularities. In this paper we study the…

2D transverse laser cooling of a hexapole focused beam of cold BaF molecules
Joost W. F. van Hofslot, Izabella E. Thompson, Anno Touwen, Nithesh Balasubramanian, Roman Bause, Hendrick L. Bethlem, Anastasia Borschevsky, Ties H. Fikkers, Steven Hoekstra, Steven A. Jones, Jelmer E. J. Levenga, Maarten C. Mooij, Heleen Mulder, Bastiaan A. Nijman, Efion H. Prinsen, Bart J. Schellenberg, Lucas van Sloten, Rob G. E. Timmermans, Wim Ubachs, Jordy de Vries, Lorenz Willmann

Membership Inference Attacks on Sequence Models
Lorenzo Rossi, Michael Aerni, Jie Zhang, Florian Tram\`er
https://arxiv.org/abs/2506.05126 https://

Membership Inference Attacks on Sequence Models
Sequence models, such as Large Language Models (LLMs) and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, existing tools are insufficient to audit the resulting risks. We hypothesize that those tools' shortcomings are due to mismatched assumptions. Thus, we argue that effectively measuring privacy leakage in sequence models requires leveraging the correlations inherent in sequential g…