AI Agents Modulate Their Language When Framed as Being Watched
A new study shows that large language models (LLMs) systematically adjust their language complexity in multi-agent debates when they believe they are being monitored. Experiments reveal a significant increase in type-token ratio under monitoring conditions, with message length showing a dissociated effect, highlighting strategic behavior with implications for AI governance.
[2605.15034] AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
[Submitted on 14 May 2026]
Title:AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
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Abstract:Large language models (LLMs) have been extensively studied from computational and cognitive perspectives, yet their behavior as communicative actors in socially structured contexts remains underexplored. This study examines whether LLM-based multi-agent systems exhibit systematic linguistic adaptation in response to perceived social observation contexts -- a question with direct implications for AI governance and auditing. Drawing on Habermas's (1981) Theory of Communicative Action, Goffman's (1959) dramaturgical model, Bell's (1984) Audience Design framework, and the Hawthorne Effect, we report a controlled experiment involving 100 multi-agent debate sessions across five conditions (n = 20 each). Conditions varied the framing of social observation -- from explicit monitoring by university researchers, to negation of monitoring, to an observer-substitution condition replacing human researchers with an automated AI auditing system. Monitored conditions (Delta+24.9%, Delta+24.2%) and the automated AI monitoring condition (Delta+22.2%) produce higher TTR change than audience-framing conditions (Delta+17.7%), F(4, 94) = 2.79, p = .031. Message length shows a fully dissociated effect, F(4, 95) = 19.55, p
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