Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers

Mon, 24 Nov 2025 00:00:00 +0000

Poster presentation of our ECML-PKDD 2025 paper at the RTG Symposium, held at the legendary Schloss Dagstuhl — a week-long gathering of PhD researchers from Max Planck Institute and Saarland University, with guest talks by Mor Geva Pipek and Luca Bortolussi.

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers

Thu, 10 Jul 2025 00:00:00 +0000

This work introduces a Transformer-based decoder that inverts embeddings of Signal Temporal Logic (STL) formulae. By constructing a small STL vocabulary, the model can generate valid formulae quickly, generalize across semantic structures, and simplify formulas while preserving their meaning. Our methodology is evaluated across varying formula complexity and applied to requirement mining tasks, performing optimization directly in the semantic space.

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Temporal Logic | Sara Candussio

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers