Neural networks power critical applications, from autonomous vehicles to medical diagnostics, but their opacity pose significant safety and trust challenges. Formal logical frameworks offer a rigorous path to understand, validate, and trust these models. This workshop aims to bridge the gap between logic and neural network analysis.
We invite contributions that demonstrate how logical frameworks can address core challenges in neural‑network analysis. Topics of interest include the expressivity of logical formalisms for neural networks, the formal verification of safety‑critical properties, the development of interpretation mechanisms grounded in logic, and the assessment of the computational complexity of model behavior.
Submissions can be in three categories:
Submissions should follow the CEURART format. The templare is available here: http://ceur-ws.org/Vol-XXX/CEURART.zip. The proceedings shall be submitted to CEUR-WS.org for online publication. Authors of regular and short papers may opt out of having their work included in the proceedings. Abstracts of already published papers will not be included in the proceedings. We plan to evaluate the possibility of a journal special issue, depending on the outcome of the workshop.
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