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cs.AI2026
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…
cs.AI2026
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…
cs.AI2026
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…