BioMed Advisor is built on a multi-model framework and is AI model-agnostic. It incorporating specialized, domain-specific LMMs.

Key Features:

• Advanced Function-Calling – Seamlessly integrates multiple AI models and external data sources.
• Chain-of-Thought (CoT) Reasoning – Enhances structured step-by-step logic processing.
• Tree-of-Thought (ToT) Reasoning – Allows multi-path exploration and refined decision-making.
• Integration of CoT into ToT – Enables more nuanced problem-solving by integrating structured and exploratory reasoning.

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At the heart of BioMed Advisor is a powerful reasoning orchestration engine. BmA doesn’t just run models—it directs them. It understands the user’s intent, biomedical context, and the strengths of connected AI models to produce structured, validated results.

Through contextual awareness, LLM-aware routing, multi-step reasoning (e.g., Tree-of-Thought and semantic verification), and iterative synthesis, BmA delivers scientifically grounded responses—not just generative output.

This orchestration ensures that every answer is optimized for accuracy, relevance, and research applicability.

Function Calling in BioMed Advisor

BioMed Advisor leverages advanced Function-Calling to dynamically interact with AI models and third-party data sources. This enables seamless integration of research workflows, from literature reviews to complex data analysis.

📌 For example, a researcher submitting a query about biomarkers for a specific disease would trigger BioMed Advisor to automatically query multiple AI models, extract relevant findings, and refine results using Tree-of-Thought (ToT) and Chain-of-Thought (CoT) reasoning.