Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
A teacher-student framework that distills symbolic STL kernels into Transformer encoders via geometric alignment, bridging formal logic and neural representations.
Mar 5, 2026
Distilling Formal Logic into Neural Spaces
A teacher-student framework that distills symbolic STL robustness kernels into a Transformer encoder via geometric kernel alignment.
Mar 5, 2026
Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Poster presentation at the RTG Symposium hosted at Dagstuhl, bringing together PhD researchers from Max Planck Institute and Saarland University.
Nov 24, 2025
STLDec: Decoding Temporal Logic Embeddings via Transformers
A Transformer-based decoder that inverts semantic embeddings of Signal Temporal Logic formulae, enabling interpretable requirement mining.
Jul 10, 2025
Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
We present a Transformer-based approach to decode STL embeddings, enabling interpretable and semantically accurate formula generation, with applications to requirement mining.
Jul 10, 2025