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A Steerable Biomedical World Model

The physician sets the direction; the model navigates — we encode aging, disease, and intervention as computable networks, so every individualized decision follows a traceable evidence chain.

Three-Layer Stack

Bench · InfrastructureMap · EngineeringN-of-1 · Application

Infrastructure → Engineering → Application · One shared evidence substrate

Content on this site is for research reference only and does not constitute medical advice.

Three-Layer Stack

From Infrastructure to Application

Build the evaluation infrastructure first (Bench quantifies capability), engineer it into services (Map turns capability into workflows), and land it in individualized applications (N-of-1 intervention reasoning) — three layers on one shared evidence substrate.

Infrastructure

Bench Module Benchmark

New paper · Aug 14, 2026

A shared coordinate system for AI-generated biological maps.

Why

AI can generate "aging modules," but without a unified benchmark no one can say which is better. Drug repurposing needs a reproducible, comparable, human-scale testbed.

What

An evaluation infrastructure of 332 modules × 1,916 small molecules × 5 chronic-disease tasks (extended to 23 disease categories): a frozen protocol guarantees reproducibility; the online demo replays the paper's propose–score–reflect–refine agent loop; all data and code open-sourced.

Where

A self-learning agent: the module atlas grows with the literature instead of relying on manual curation. Core finding — no single biological map is universally optimal; different diseases need different module combinations.

Engineering

Map: State–Intervention Navigation

Evidence should not be scattered across thousands of papers — it should grow into a navigable map.

Why

Physicians and researchers need navigation from individual state to intervention options, not yet another pile of literature. Evidence must be organized, versioned, and traceable.

What

Engineering Bench-validated capability into a versioned, evidence-aware navigation map: personal state projection for individuals; an industrial research workflow (disease reclassification → patient stratification → drug repurposing → target discovery) for pharma and R&D teams.

Where

To become the computable data substrate of longevity medicine — clinical communication and industrial R&D sharing one map, one evidence chain.

Application

N-of-1 Individualized Reasoning

Longevity interventions are N-of-1 trials — population averages cannot answer "will this work for me?"

Why

Multimorbidity among adults 65+ reaches 42.4%; the one-disease-one-model paradigm has failed. Each aging trajectory is unique and demands counterfactual reasoning about this individual.

What

DNA methylation reads individual state; the DNet dependency network (108 terms, 1,121 edges) bridges Western and Chinese medicine; PPI networks link drug targets — converging into a four-layer evidence chain (state → steering alignment → mechanism → confidence), translated into a three-panel clinical view.

Where

From retrospective validation (four-disease drug screening; RA Recall@10 = 51.7%, 5.8× random baseline) toward a prospective precision-intervention loop: target engagement → clinical biomarker response → individualized prescriptions.