CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models
CogArena is a procedurally generated 13-paradigm benchmark for evaluating cognitive ability structure in LLMs. Testing on 55 open-weight models shows all paradigm correlations are positive, a common factor explains about half the variance, but within-grouping advantages are small and fail to meet validation criteria. The study concludes that current evidence does not support stable five-dimensional cognitive profiles.
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[Submitted on 27 Jul 2026]
Title:CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models
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Abstract:LLM cognitive scores are increasingly summarized as per-ability profiles whose dimensions should converge across tasks, respond selectively to matched interventions, and generalize beyond the models used to define them. We introduce CogArena, a procedurally generated 13-paradigm benchmark built around a multimethod framework for determining when cognitive-task scores warrant dimensional labels across five theory-motivated groupings. Across 55 open-weight models, nearly all paradigm correlations are positive and a common axis explains about half the variance. The within-grouping advantage is small, scoring-sensitive, and uncertain across model families. In a separately frozen, fully crossed study across 12 models from six families, targeted scaffolds show a small matched-grouping advantage, but no scaffold-specific contrast survives multiplicity correction and selectivity does not improve held-out-family prediction. The frozen confirmation criterion fails. A post-hoc alternate-wording replication produces a smaller positive estimate and again fails. Together, these results support a boundary conclusion. Theory-aligned prompting produces a small in-battery diagonal tendency, but the present evidence does not establish stable five-dimensional profiles. CogArena provides a workflow joining behavioral signatures, covariance, matched interventions, and out-of-family prediction before cognitive labels are attached to model scores.
Comments: 21 pages, 8 figures. Code and the procedurally generated battery: this https URL
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.24999 [cs.CL]
(or arXiv:2607.24999v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.24999
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dengzhe Hou [view email] [v1] Mon, 27 Jul 2026 18:56:13 UTC (957 KB)
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