Results
Results So Far
Early outputs from the project: open-source software and tool extensions. Each result also appears on the page of the theme it belongs to. This page grows as the project progresses.
Software
property-driven-ml
A general open-source framework for property-driven machine learning: logical constraints over hyper-rectangle input regions are compiled into training loss terms through differentiable logics (Gödel, Łukasiewicz, Reichenbach, Yager, DL2, and Signal Temporal Logic), combined with PGD and Auto-PGD adversarial training. Gusts Grinbergs is a contributor to the framework, which is described in A General Framework for Property-Driven Machine Learning (Flinkow et al., 2025).
github.com/property-driven-ml/property-driven-ml · Themes: Rules & Training, Differentiable Logics
NIDS-Verify
NIDS-Verify combines adversarial training with formal verification for network intrusion detection: attack patterns are formally specified in the Vehicle language and enforced during training through projected gradient descent over hyperrectangle-bounded input regions. Extending the companion code of Flood et al., 2024, Gusts Grinbergs re-engineered the dataset and verification pipeline — rebuilding feature extraction and preprocessing for CIC-IDS-2017, adding tooling for large-scale packet captures, and correcting known timestamp inconsistencies in the dataset — and modernised the training stack through a migration to PyTorch Lightning and integration with the property-driven-ml framework.
github.com/ggustavs/NIDS-Verify · Themes: Datasets, Rules & Training
Signal Temporal Logic for Vehicle
An extension of the Vehicle specification language with Signal Temporal Logic operators (globally, finally, until) and closed-loop controller specifications, implemented as a built-in differentiable logic on top of STLCG++. Joint work by Alessandro Bruni, Matthew L. Daggitt, Gusts Gustavs Grīnbergs, and Ekaterina Komendantskaya; a paper on this work is to appear at LOPSTR+PPDP 2026.