Architecture
Synergizing Two Core Frontier Technologies
Uniting the 'Right Brain' creativity of LLMs with the 'Left Brain' logic of Ontology. Our unique architecture anchors generative AI in verified biological truth, enabling the AI Co-Scientist to hypothesize, validate, and evolve without the risk of hallucination.
Ontology & Knowledge Graphs
Structured, precise, and grounded—enabling high-accuracy retrieval and factual control.
The Synergy
Ontology anchors LLM outputs to verified knowledge, aligning generation with truth for reliable, real-world applications.
Large Language Models (LLMs)
Creative, fluent, and generative—powerful at language, but prone to hallucination.
The AI Co-Scientist System Architecture
An autonomous multi-agent framework designed for continuous hypothesis generation and validation.
Supervisor Agent
Executes the 'Research Plan Configuration' phase. This central coordinator manages the task queue, assigns specialized agents, and dynamically allocates resources.
Generation Agent
Explores vast scientific literature via web search, synthesizes findings, and engages in simulated debates to generate initial, high-potential research hypotheses.
Reflection Agent
Acts as a critical peer reviewer, rigorously examining generated hypotheses for scientific validity, logical consistency, and novelty before they proceed.
Ranking Agent
Employs Elo-based tournament algorithms to assess and rank hypotheses against each other, prioritizing only the most promising candidates for further development.
Proximity Agent
Computes semantic proximity between hypotheses to identify clusters, preventing duplication and ensuring a diverse search space for innovation.
Evolution Agent
Refines top-ranked hypotheses by applying "out-of-the-box" thinking—combining distinct ideas and mutating parameters to improve feasibility and impact.
Meta-Review Agent
Synthesizes insights from all review cycles to optimize the entire system's performance, creating a continuous feedback loop for self-improvement.