AI 中文总结
针对未知环境中智能体需开发共享词汇的问题，提出神经符号词汇发现框架，让基于大语言模型的智能体群体进行指称博弈自组织词汇表，通过语义锚定扩展人类词汇，模拟达成共识并表征收敛动态，为自主探索预部署规划迈出第一步。
AI 中文摘要
部署在未知环境（如行星或深海探索）中的自主智能体群体必须开发共享词汇来指代任何人类语言中都没有名称的实体。我们提出了神经符号词汇发现（NSLD）框架，其中基于大语言模型的智能体群体针对分布外视觉指称物进行指称博弈，自主自组织共享的外星词汇表。每个智能体将冻结的CLIP视觉编码器与私有FAISS向量索引和纯文本大语言模型相结合。关键的是，发现的外星词汇通过嵌入空间中的语义接近度与自然语言相锚定，用新的基于感知的词汇扩展人类词汇表。在多达二十个智能体和十个视觉指称物的群体模拟中达成了共识。通过三个分析模型对收敛动态进行了表征，R^2>0.95，这代表了自主探索任务中预部署规划的第一步。
英文摘要
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.
Comments32 pages, 7 figures
