A two-agent swarm architecture uses a gatekeeper agent to filter cases and a diagnostic agent to adjudicate final diagnoses, coordinating via a defined sequence outlined in the paper.
Their Mai DXO ensemble achieved 80% diagnostic accuracy at ~$2.5K test cost, versus $8K for a single O3 model, and 50% accuracy with only question-based diagnosis.
They overlaid standardized US medical test pricing plus a $300 consult fee per patient query to jointly evaluate diagnostic accuracy and incurred test costs.
A gatekeeper agent synthesizes and returns real or fabricated test results to prevent reward hacking when an LLM swarm infers lack of data as negative feedback.
They designed a swarm of O3-based LLM personas (challenger, checklist, hypothesis generator, test-ordering, stewardship) orchestrated via a chain-of-debate mechanism to iteratively converge on a diagnosis.
Researchers built SDbench, a 304-case sequential diagnostic benchmark from New England Journal of Medicine case proceedings to evaluate iterative AI diagnostic agents.
Use publicly available medical case datasets—such as those from the New England Journal of Medicine or Hugging Face benchmarks—and evaluate AI agent performance against clinician diagnoses.
Adopt a modular multi-agent pipeline where each agent specializes in steps like data extraction, reasoning, and diagnosis, as demonstrated by Microsoft’s applied medical AI paper to outperform both frontier models and doctors.
Gemma 3N uses a nested architecture with 2 billion active parameters out of 4 billion total to drastically cut computational requirements while retaining performance.
Use Cloudflare’s AI House of Mirrors pattern to detect AI crawlers and serve self-referencing garbage content that wastes their tokens and keeps them occupied.
Adopt a microtransaction-based API gating pattern to monetize real-time LLM crawler requests and control content ingestion using an infrastructure layer like Cloudflare’s.
Even with advanced LLMs, deeply understanding your dataset's nuances remains essential to structure prompts effectively and extract high-quality outputs.
Combining a unique, high-quality evaluation dataset with iterative prompt engineering can yield competitive AI product performance without building or fine-tuning custom models.
