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.