Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports

Abstract:Language-model self-reports are evidence about behavior in a prompt environment, not by themselves evidence of a self-model. We investigate counterfactual reports about affect-like states under activation interventions and ask whether the report remains bound to the named intervention when the demonstration environment changes. Across three open instruction models, wrong-source demonstrations move reports toward the source answer family, while explicit mechanism binding reduces this pull. Self-report benchmarks should include environment-shift invariance tests under fixed intervention before treating accuracy as evidence for an autonomous report mechanism.

Computation and Language (cs.CL)

arXiv:2609.32449 [cs.CL]

(or arXiv:2609.32449v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.32449 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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