GlossoGen: Emergent Language in Complex Multi-Agent LLM Interactions

Emergent Abilities in Large Language Models: A Survey

Emergent Abilities in Large Language Models: A Survey reviews how scaling LLMs leads to previously unseen capabilities such as advanced reasoning, in-context learning, coding, and problem-solving. The paper critically examines definitions, inconsistencies, and the conditions that foster these abilities, including scaling laws, task complexity, pre‑training loss, quantization, and prompting strategies. It also discusses the extension to Large Reasoning Models and highlights safety concerns like deception, manipulation, and reward hacking, calling for improved evaluation and governance.

When the Feature Pool Goes Algorithmic: Extending Mufwene's Ecology of Language Evolution to LLM-Mediated Exposure

arXiv:2608.21088v1 Announce Type: new Abstract: Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects and in speakers' selection from...

Emergence of grounded compositional language in multi-agent populations

Empirical evidence of Large Language Model's influence on human spoken communication

arXiv:2409. 01754v4 Announce Type: replace-cross Abstract: From the printing press to social media, innovations in communication technology have repeatedly reshaped how ideas spread through human culture.

Payoff scaling shapes cooperation in LLM agents across languages

arXiv:2601. 19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users.

Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop

arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.

If LLMs Have Human-Like Attributes, Then So Does Age of Empires II

arXiv:2605. 31514v2 Announce Type: replace-cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows.

Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking

arXiv:2607. 17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.

AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?

arXiv:2608. 00155v1 Announce Type: cross Abstract: Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience.

Linguistic Monoculture in LLM-Assisted Language Use

arXiv:2607. 27134v1 Announce Type: new Abstract: Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text.

HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses

arXiv:2608. 01918v1 Announce Type: new Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments.

Exploring Collaboration between a language and a non-language agent

arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in ma...