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 the construction of a renormalisation group (RG)-motivated Early Dark Energy (EDE) framework as testbed, we formalise 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 rather than cosmological. We demonstrate that large language models, when deployed under explicit constraint and cross-validation protocols, can significantly accelerate the hypothesis-generation and formalisation stages of effective field theory construction. At the same time, we identify essential limitations: absence of genuine numerical execution, lack of uniqueness proofs, and continued dependence on human-directed physical insight. It aims to consolidate the AI-assisted workflow into a reproducible methodological framework, providing transparency regarding multiple LLM/AI contribution, validation structure, and human verification and oversight being first and foremost. Such architectures may be particularly relevant for latency-constrained or physically inaccessible environments, including deep-space missions, autonomous planetary habitats, and extreme terrestrial research installations, I welcome any collaborative expansion, Scrutiny/constructive criticism Into further research.