Arnaud Gotlieb

Precondition Constraint Acquisition Through Explanatory Queries

Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach

A neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge is proposed.

Probabilistic Model Checking of Autoregressive Neural Sequence Models

The pipeline extracts a discrete-time Markov chain from the SUT's token-by-token generation, verifies formal PCTL specifications with the PRISM model checker, and aggregates the per-input verdicts into a coverage curve over the input space, identifying the gap between structural completeness and chemical validity.

Semi-supervised CAPP Transformer Learning via Pseudo-labeling

Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, demonstrating the method's effectiveness in data-scarce manufacturing environments.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies

Gimitest is an open-source tool that supports various gym frameworks and allows for modifications of their integrated components and showcases its effectiveness in testing multiple RL policies in environments such as the official Farama Gymnasium and PettingZoo.