2 citations · 2 across the 2 of their papers we have counts for
4 papers
Reasoning Models Are Accurate but Unsound on Identification
Arman Behnam, Binghui Wang
A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers a nonidentifiable one. The…
Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations
Haozheng Luo, Yimin Wang, Jiahao Yu +2
We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness against jailbreak attacks. Unlike p…
Measure-Theoretic Anti-Causal Representation Learning
Arman Behnam, Binghui Wang
Causal representation learning in the anti-causal setting (labels cause features rather than the reverse) presents unique challenges requiring specialized approaches. We propose An…
Graph Neural Network Causal Explanation via Neural Causal Models
Arman Behnam, Binghui Wang
Graph neural network (GNN) explainers identify the important subgraph that ensures the prediction for a given graph. Until now, almost all GNN explainers are based on association,…