Improving large language model applications in biomedicine with retrieval-augmented generation
Independent research examining retrieval-augmented generation across biomedical LLM applications.
Original source →Research
This research explores engineered approaches to reasoning, evidence selection, traceability, and evidence-grounded medical intelligence. Public descriptions are intentionally limited to protect ongoing research and patent-sensitive work.
This paper presents Victor Evidence: a deterministic evidence-selection framework that separates candidate retrieval from evidence admissibility before answer generation. The public paper describes Meaning-Utility Estimation, Diversity-Utility Estimation, and an explicit evidence gate for producing compact and inspectable evidence sets.
These buttons point to independent scholarly repositories/indexes where the same research work is available or indexed.
Current Research
VictorMind is a developing mathematical and engineered intelligence framework for structured understanding and reasoning. Its reasoning decisions are governed by defined theories, relationships, inputs, and engineered processes rather than delegated to a language model.
A dedicated VictorMind paper is in preparation. No publication link is provided yet because the work is continuing.
Public materials intentionally do not disclose proprietary theories, equations, mathematical relationships, thresholds, internal decision rules, algorithms, or implementation details. Patent applications covering aspects of VictorMind and Victor Evidence are pending.
Research Resources
The prototype may use publicly available or research-access resources and services where their applicable terms permit academic and research use. Third-party publications, APIs, databases, repositories, and evidence sources remain the property and responsibility of their respective authors and providers. Their inclusion or use does not imply endorsement, partnership, sponsorship, or validation of VictorMind or Victor Evidence.
Independent research examining retrieval-augmented generation across biomedical LLM applications.
Original source →Independent research evaluating limitations of LLMs in realistic clinical decision-making scenarios.
Original source →Independent review of healthcare RAG methods, evaluation, transparency, and research gaps.
Original source →Independent research illustrating the importance of high-precision retrieval in evidence-centered clinical workflows.
Original source →Research Collaboration
The current objective is to continue the research and extend the work. Researchers, clinicians, academics, institutions, and others with a serious research interest may contact the Primary Researcher directly. This is not presented as an investment or commercial solicitation.