24 citations · 109 across the 67 of their papers we have counts for
7 papers · 1 filter
Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints
Gwen Yidou-Weng, Edward Sun, Tianyi Ma +5
LLMs generate fluent plans for robots but routinely violate the syntactic and se8mantic constraints they must satisfy to execute, and existing remedies trade formal guarantees agai…
Interpretable Causal Discovery via Causal-Effect Constraints
Cixuan Zhang, Guy Van den Broeck, Benjie Wang
Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but al…
The Parser Already Knows: Lightweight Bias Correction in Constrained Decoding
Işıl Özgü, Yaoxuan Wu, Guy Van den Broeck +1
Grammar Constrained Decoding (GCD) forces Language Models (LMs) to produce syntactically valid outputs by masking out non-conforming tokens at each step. However, rigid masking dis…
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
Xi Zheng, Dulanga Weerakoon, Yintong Huo +8
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…
Probabilistic Programs of Thought
Poorva Garg, Renato Lui Geh, Daniel Israel +3
LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to reason about code, generate cod…
ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits
Annaëlle Baiget, Jaron Maene, Seongmin Lee +3
Understanding and explaining the structure of generated test inputs is essential for effective software testing and debugging. Existing approaches--including grammar-based fuzzers,…