Lexical discovery in unknown environments orchestrated by Large Language Models

AI 中文解读

AI智能体在没有人类语言的情况下，自己发明新词汇来描述从未见过的事物——就像探险队给外星新物种起名字。研究人员让一群AI在陌生环境（比如深海或外星球）中玩猜谜游戏，它们要互相描述看到的奇怪物体。AI们通过一套巧妙的方法——结合视觉识别和语言模型——逐步统一了称呼，创造出一套全新的“外星词汇”。这些词汇还能对应到人类语言，好比给词典增加了新词。未来机器人探索未知区域时，不需要提前编程所有名字，它们能自己创造并交流新词汇，大大提升自主探索能力。普通人可能会见证AI像人类一样发明语言，甚至在虚拟世界中帮我们命名新发现，让机器协作更灵活，也为科学探索开辟新可能。

arXiv:2607.22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.g. planetary or deep-sea exploration) must develop shared vocabularies to refer to entities that have no name in any human language. We propose the Neuro-Symbolic Lexical Discovery (NSLD) framework, in which a population of LLM-based agents plays a referential game over out-of-distribution visual referents, autonomously self-organising a shared alien lexicon. Each agent combines a frozen CLIP vision encoder with a private FAISS vector index and a text-only LLM. Crucially, discovered alien words are anchored to natural language via semantic proximity in the embedding space, enlarging the human vocabulary with new perceptually grounded words. Consensus is reached in simulations with populations of up to twenty agents and ten visual referents. Convergence dynamics are characterised through three analytical models achieving R^2 > 0.95, representing a first step towards pre-deployment planning in autonomous exploration missions.