A Multi-Model Collaborative AI Framework for Cross-Disciplinary Natural Science Research: The CAI Model Approach

Keywords: Cocktail AI Integration (CAI), Multi-Model Collaboration, Dual-Brain Architecture, Cross-Disciplinary Scientific Research, Hypothesis Generation and Fusion, Knowledge Integration, Autonomous Scientific Discovery

TL;DR: CAI is an AI-first, multi-model framework that autonomously generates, evaluates, and fuses scientific hypotheses across disciplines through a structured dual-brain architecture.

Abstract: Cross-disciplinary research demands the integration of diverse knowledge domains, where single-model AI systems often struggle to balance creativity and rigor. This paper introduces the Cocktail AI Integration (CAI) Model, a structured 9+1 dual-brain architecture built on GPT-5 via MYGPT, combining human-curated innovation logic with automated reasoning. The system orchestrates nine specialized models (M01–M09) for divergent exploration, with a fusion module (M10) for arbitration and synthesis. Experiments in workflow reconstruction, knowledge flow modeling, and seismic risk forecasting demonstrate measurable performance gains over LLM baselines (e.g., GPT-5, Gemini, Claude), including 15–25% increases in novelty, 12–18% feasibility gains, and 30% fewer contradictions. Real-world validation across seven external submissions further supports alignment between AI reviewer judgments and expert outcomes. All prompts and test traces are detailed in the appendices to ensure transparency and reproducibility. CAI offers a practical framework for AI-augmented science, simulating structured hypothesis generation, peer-like critique, and synthesis in complex interdisciplinary tasks.

Submission Number: 62