Benjie Wang

A Roadmap Towards Practical Applications of Neurosymbolic Learning and Reasoning

Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints

Meta-Ctrl is proposed, a constrained-decoding framework that guarantees the encoded constraints while preserving the base LM's plan quality, and is demonstrated on a real tabletop robot, where every generated plan satisfies its preconditions and goals by construction.

Interpretable Causal Discovery via Causal-Effect Constraints

This work considers the task of conditional causal discovery as a Bayesian inference problem, in which the posterior is targeted over causal graphs and parameters conditional on an event such as a causal-effect constraint, and adapts rare-event estimation techniques to perform inference the joint graph-parameter space.

The Pitfalls of Maximum Likelihood Training of TPMs for Controllable Language Generation

This work finds that likelihood-trained TPMs can result in failed generations due to overly large corrections to the LM’s logits, and trains TPMs with LM-aligned objectives that better align with the LM token-probability space.