2 citations · 2 across the 2 of their papers we have counts for
7 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…
Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval
Arman Behnam, Binghui Wang
Memory-augmented large language models must decide which memories to retain, and recent systems do so by estimating each memory's effect on task performance. However, these estimat…
RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations
Arman Behnam, Sunglyoung Kim, Liangwei Yang +2
An AI companion that talks with someone for months should come to understand them. It should know who they are, remember what they said, and recognize when something from the past…
Structure-agnostic Causal Representation Learning
Arman Behnam, Binghui Wang
Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods require specifying the causal struct…
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…
VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
Arman Behnam
Accurate detection and segmentation of brain tumors from magnetic resonance imaging (MRI) are essential for diagnosis, treatment planning, and clinical monitoring. While convolutio…
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,…