arXiv AI
GlossoGen: Emergent Language in Complex Multi-Agent LLM Interactions
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arXiv Machine Learning
Aug 27
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.
arXiv Computation and Language
Aug 24
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...
arXiv Computation and Language
Oct 7
Evolving language compositionality in a frequency-structured meaning space
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...
arXiv AI
Sep 30
Semantic Projection for Continual Self-Evolution of Language Agents
arXiv:2609.36626v1 Announce Type: new Abstract: Language-model agents increasingly rely on persistent natural-language skills to adapt beyond their frozen model parameters. When a shared skill is rep...
OpenAI Blog
Mar 15, 2017
Emergence of grounded compositional language in multi-agent populations
arXiv AI
Sep 16
Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling
The paper introduces the Self‑Emergence Agent Architecture (SEAA), a framework that combines a Hidden Markov Model for behavioral inertia, a reflexive metacognition loop that updates the HMM, and a social environment where agents compare behaviors. This closed loop enables agents to develop distinct, stable personalities and social structures without external prompts. Experiments with both a language‑model‑free prototype and hosted LLMs demonstrate spontaneous symmetry breaking and the emergence of consensus hubs and outliers.