发表机构
BAIKA Women’s University（梅田花女子大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出Semantic Lenia人工生命框架，将LLM推理转化为宏观logit空间的连续动力系统，通过非线性稳态反馈回路平衡语义吸引与句法排斥，在混沌边缘涌现自主语义孤子，建立机器认知的物理标度律。
AI 中文摘要
我们提出了Semantic Lenia，这是一种人工生命框架，它将大语言模型（Large Language Model，LLM）的推理从静态优化问题转变为宏观logit空间内的连续动力系统。通过建立非线性稳态反馈回路来动态平衡语义吸引与句法排斥，我们证明了“自主语义孤子”的涌现——这种宏观耗散结构可避免重复结晶。我们开展了详尽的参数扫描，绘制出一条关键的“宜居脊”，其中施加的引导力与模型固有的句法惯性达到完美平衡。该方法成功将生成轨迹维持在混沌边缘，触发深刻的溯因飞跃且无结构崩溃，并建立了机器认知的物理标度律。
英文摘要
We introduce Semantic Lenia, an artificial life framework that transforms Large Language Model (LLM) inference from a static optimization problem into a continuous, closed-loop dynamical system. By establishing a non-linear homeostatic feedback loop to dynamically balance semantic attraction and syntactic repulsion, we demonstrate the emergence of ``Homeostatic Solitons''-metastable semantic structures that actively resist repetitive crystallization. Our exhaustive parameter sweeps map a critical ``Habitable Ridge'' where applied steering forces balance the model's intrinsic syntactic inertia. This approach successfully maintains generative trajectories in a numerically sensitive critical regime, triggering profound abductive leaps without structural collapse, and reveals a capacity-dependent scaling trend in the syntactic inertia across different model sizes.
Comments18 pages, 6 figures. Code, datasets, and interactive phase diagrams are available at https://y-kayama.github.io/semantic-lenia/
