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This paper develops post-selection fit assessment for partially exploratory factor analysis (PEFA) under variational Bayesian variable selection, proposing absolute and relative fit diagnostics and a scale-free gain rule for factor-number selection, validated by simulations and an empirical example.
This paper investigates whether frontier LLMs exhibit individuated metacognition—the ability to assess their own item-level capabilities beyond shared signals. Through factor analysis and pairwise calibration across 20 models and six benchmarks, the authors find no evidence of such metacognition; confidence differences reduce to a single shared difficulty factor, suggesting models rely on a common difficulty signal rather than model-specific self-knowledge.