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papersTODAY 04:00 UTC

Paper Proposes Using Model Internals to Predict Behavior on Unseen Data

A new arXiv paper reframes interpretability research around predicting how a model will respond to previously unseen inputs, rather than only to targeted mechanistic interventions. The authors use a model's internal representations to forecast its out-of-distribution behavior. The work appears in two arXiv listings, cs.AI and cs.LG, as a replacement submission.

interpretability/research/arxiv/evaluation