Optimisation Under Constraints V: Machine Intelligence in Theoretical Cosmology: Multi-Agent Large Language Model Collaborative Structured Reasoning

This work presents a documented methodological case study of multi-agent large language model (LLM) collaboration applied to theoretical cosmology. Using a renormalisation group (RG)-motivated Early Dark Energy (EDE) framework as a testbed, we formalize and critically examine an AI-assisted workflow incorporating GPT-series models (OpenAI), Gemini (Google), and Kimi K2.5 (Moonshot AI) in distinct functional roles: symbolic derivation, adversarial probing, and logical validation. The central contribution is methodological, not cosmological, by demonstrating that LLMs, when deployed under explicit constraints and multi-model cross-validation, can accelerate hypothesis generation, stress-test reasoning, and minimize hallucinations, while maintaining human oversight as first and last authority. The proposed multi-LLM agent matrix emerged from empirical observations during RG-EDE workflow development. Cross-model disagreements revealed hidden assumptions, reducing uncorrected reasoning errors and highlighting the value of redundancy principles familiar from safety-critical engineering and ensemble learning systems. Such structured, rigorously verified AI architectures could be applied beyond cosmology, including high-stakes computational environments, autonomous planetary research, or latency-constrained scientific simulations. They enhance reliability, reduce model bias, and provide a transparent framework for AI-assisted scientific reasoning.本研究针对理论宇宙学领域，呈现了一项具备完整文档记录的多智能体大语言模型（Large Language Model, LLM）协作方法学案例研究。本研究以基于重整化群（Renormalization Group, RG）的早期暗能量（Early Dark Energy, EDE）框架作为测试平台，规范化构建并开展了一套AI辅助工作流的批判性评估，该工作流整合了OpenAI的GPT系列模型、谷歌的Gemini以及月之暗面Moonshot AI的Kimi K2.5，并为三者赋予差异化的功能角色：符号推导、对抗性探查与逻辑验证。 本研究的核心贡献在于方法学层面而非宇宙学领域：研究表明，大语言模型在明确约束条件与多模型交叉验证的部署框架下，能够加速假说生成、对推理过程进行压力测试并降低幻觉现象的发生率，同时始终将人类监督作为首要与最终的权威依据。 所提出的多LLM智能体矩阵源自RG-EDE工作流开发过程中的实证观测结果。模型间的分歧揭示了隐含的预设假设，从而减少了未被纠正的推理错误，并凸显了安全关键工程与集成学习系统中所通用的冗余原则的价值。 这类经过结构化设计与严格验证的AI架构可应用于宇宙学领域之外的场景，包括高风险计算环境、自主行星研究，或受延迟约束的科学模拟任务。此类架构能够提升系统可靠性、降低模型偏差，并为AI辅助的科学推理提供透明化的实施框架。