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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 latter is more consequential, as no observational data can validate the claimed formula. Measuring this failure requires queries that are pr...
SaCRL is introduced, a framework that jointly identifies the causal structure and learns the corresponding invariant representation without prior structural knowledge, and provides theoretical guarantees for structure identification, including under random-feature approximation, invariance satisfaction, and out-of-dist...
A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person's record, and such records are private, so benchmarks generate the person and the questio...
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