arXiv:2609.01491v1 Announce Type: cross Abstract: The growing rate at which LLM agents interact with one another raises key questions about language evolution in multi-LLM-agent settings, with implic...
The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.
The paper investigates how a minority of biased agents in a multi‑agent system of large language models (LLMs) can amplify bias through textual interactions. Even a small percentage of persistently extreme agents causes significant opinion shifts among the non‑biased agents, with the effect occurring faster in the Llama 3.2 model than in a classical Friedkin‑Johnsen model. Semantic analysis shows that rhetorical consistency rises with biased exposure and that non‑biased agents adopt the biased vocabulary even when their numerical opinions change only modestly.
arXiv:2606. 10582v1 Announce Type: cross Abstract: Generalization in emergent communication has largely focused on novel inputs or linguistic structures, yet the capacity for agents to communicate with strangers from strictly disjoint communities remains relatively unexplored.
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
SwarmWorld demonstrates that homogeneous language‑model agents can self‑organize into evolving technological societies without assigned roles or direct communication. In a spatial environment, agents explore, process resources, construct artifacts, and write executable controllers that are later evaluated by a deterministic simulator. The resulting societies develop broader, more resilient technological portfolios than isolated search, with agents differentiating into exploration, construction, maintenance, and coordination roles as the world matures.
arXiv:2606. 12191v1 Announce Type: cross Abstract: Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities.
arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.
arXiv:2310. 06555v3 Announce Type: replace-cross Abstract: Emergent communication enables agents to develop bespoke languages that improve communication efficiency.
arXiv:2606. 27443v1 Announce Type: new Abstract: Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored.
arXiv:2606. 06380v1 Announce Type: cross Abstract: The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminative) or engineer consciousness-inspired modules directly (architectural); both leave open whether observed structures are artifacts of human language priors.
arXiv:2607.29642v2 Announce Type: replace Abstract: The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages hav...