Two-Faced Social Agents: Context Collapse in Role-Conditioned Large Language Models

• URL: http://arxiv.org/abs/2511.15573v1
• Date: Wed, 19 Nov 2025 16:04:49 GMT
• Title: Two-Faced Social Agents: Context Collapse in Role-Conditioned Large Language Models
• Authors: Vikram K Suresh,
• Abstract summary: GPT-5 exhibited complete mathematics contextual collapse and adopted a singular identity towards optimal responses.<n> Claude Sonnet 4.5 retained limited but measurable role-specific variation on the SAT items.<n>All models exhibited distinct role-conditioned affective preference, indicating that socio-affective variation can reemerge when cognitive constraints are relaxed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we evaluate the persona fidelity of frontier LLMs, GPT-5, Claude Sonnet 4.5 and Gemini 2.5 Flash when assigned distinct socioeconomic personas performing scholastic assessment test (SAT) mathematics items and affective preference tasks. Across 15 distinct role conditions and three testing scenarios, GPT-5 exhibited complete contextual collapse and adopted a singular identity towards optimal responses (PERMANOVA p=1.000, R^2=0.0004), while Gemini 2.5 Flash showed partial collapse (p=0.120, R^2=0.0020). Claude Sonnet 4.5 retained limited but measurable role-specific variation on the SAT items (PERMANOVA p<0.001, R^2=0.0043), though with inverted SES-performance relationships where low-SES personas outperformed high-SES personas (eta^2 = 0.15-0.19 in extended replication). However, all models exhibited distinct role-conditioned affective preference (average d = 0.52-0.58 vs near zero separation for math), indicating that socio-affective variation can reemerge when cognitive constraints are relaxed. These findings suggest that distributional fidelity failure originates in task-dependent contextual collapse: optimization-driven identity convergence under cognitive load combined with impaired role-contextual understanding. Realistic social simulations may require embedding contextual priors in the model's post-training alignment and not just distributional calibration to replicate human-like responses. Beyond simulation validity, these results have implications for survey data integrity, as LLMs can express plausible demographic variation on preference items while failing to maintain authentic reasoning constraints.

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