OpenAI Blog
Emergence of grounded compositional language in multi-agent populations
Read the original on OpenAI Blog →The Flow has not summarised this story yet — read it at OpenAI Blog.
Related stories
arXiv AI
Sep 2
GlossoGen: Emergent Language in Complex Multi-Agent LLM Interactions
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...
arXiv Computation and Language
Sep 3
AI agents reshape consensus formation in human groups
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.
arXiv Machine Learning
Sep 17
Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric
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 AI
Jun 10
Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching
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 AI
Jun 9
A Survey on Large Language Model-Based Game Agents
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
arXiv Computation and Language
Aug 27
SwarmWorld: Stigmergic technological evolution in societies of language-model agents
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.