Transformers | Sara Candussio

Distilling Formal Logic into Neural Spaces

Thu, 05 Mar 2026 00:00:00 +0000

Continuous neural representations of STL specifications via kernel distillation. Accepted at NeSy 2026.

Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic

Thu, 05 Mar 2026 00:00:00 +0000

Accepted at NeSy 2026 as an oral presentation 🎉

The Idea

Typical knowledge distillation compresses a big neural model into a smaller one. We do something different: our “expert” isn’t a neural network at all — it’s a mathematical kernel built on top of formal logic.

This kernel is provably correct: it captures the true meaning of logical formulas with mathematical guarantees. The problem? It’s expensive to compute and doesn’t scale.

So instead of distilling a big neural model into a smaller neural model, we distill the geometric structure (i.e. relative positions) of a symbolic kernel into a Transformer encoder. We’re not compressing parameters — we’re transferring mathematical meaning into neural space.

Using a teacher-student setup with a kernel-weighted geometric alignment objective, we train the encoder to mirror the semantic distances defined by the symbolic kernel. Errors are penalized proportionally to their semantic discrepancy — not just their magnitude.

Why It Matters

The result is a model that:

runs in a single forward pass
produces semantically faithful embeddings of logical formulas
can reconstruct the original formula from its embedding
all without sacrificing the logical guarantees of the original kernel

This opens up scalable, trustworthy neuro-symbolic reasoning for domains where correctness and efficiency aren’t optional:

🚗 Autonomous driving & robotics (real-time monitoring of safety specs)
🏥 Healthcare (checking patient signals against clinical guidelines)
⚙️ Cyber-physical systems (fast verification & control synthesis)

And beyond STL, this approach generalizes to any domain with a meaningful but expensive similarity function — think genomic sequences, molecular graphs, or structured data where semantics matter more than surface form.

Co-authored with Gabriele Sarti, Gaia Saveri, and Luca Bortolussi.

If you want to explore how to transfer a slow, expensive similarity function into Transformers — let’s connect!

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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If you find overlap with your work or interests, I would be glad to connect and explore possible collaborations.

STLDec: Decoding Temporal Logic Embeddings via Transformers

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

Transformer-based decoder for inverting semantic embeddings of Signal Temporal Logic (STL) formulae. Published at ECML-PKDD 2025.