This work proposes that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices, and highlights how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.
Abstract
Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.
Rapid advances in large language models (LLMs) have empowered autonomous agents to generate social networks, communicate, and form shared and diverging opinions on political issues. However, our understanding of their collective behaviours and underlying mechanisms remains incomplete. In this paper, we simulate netwo...
Large language models (LLMs) are increasingly deployed to simulate human behavior, acting as computational replicas of human subjects. Yet the lived psychological experience of humans is difficult to benchmark, particularly as it unfolds over time. We introduce a psychometric benchmark for computational replicas: perso...
This study proposes a transparent rule-based framework for estimating persistence, delegation-related lexical patterns, and refinement-related lexical markers without inferring psychological dependence in conversational generative models.
Aracely Mera-Navarrete, Solange Revelo, Jefferson Beltrán-Morales et al.· Frontiers in Artificial Inte...· 0 citations
Large Language Model (LLM) agents are increasingly deployed as populations of interacting entities, in which consensus --agreement on a shared answer-- emerges as a collective, unengineered behaviour. Prior work on LLM consensus shows that agents can cross-verify their answers and converge towards more factual response...
Emanuele Ricco, Elia Onofri, Vincenzo Sammartino et al.· 0 citations
It is argued that measuring signatures of this kind offers a lightweight, agent-agnostic diagnostic layer for detecting coordinated collective behaviour in deployed multi-agent systems without relying on natural language or model versioning.
It is shown that resonance propagates to related beliefs, suggesting interconnected belief structures within AI agents, and indicates that AI agents are susceptible to radicalization, particularly when messages align with their existing beliefs.
Ozgur Can Seckin, Shalmoli Ghosh, A. Flammini et al.· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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