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arXiv Machine Learning By Zhen Zhang, Ahmad Hafez, Amr Alanwar

Cross-View Correspondence Is a Measurement Intervention: Two-Sided Validation for Agent Evaluation and Credit Assignment

Read the original on arXiv Machine Learning →

The paper argues that cross‑view correspondence, commonly used in agent evaluation and trace‑based learning, functions as a measurement intervention. Removing or altering this correspondence can create artificial sensitivity or invariance, and multiple optimal correspondences can obscure mechanism labels and learning credit. The authors propose a validity theory with two‑sided validation, all‑optima identification, and uncertainty propagation, and demonstrate through experiments that unvalidated correspondences can misattribute credit and erase harmful responses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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