FASCIA Research · Position · Methodology · ASI

Research.

Fascia is an AI research and deployment company on the path to Artificial Superintelligence. This is where we publish what we learn. Position papers, capability methodology, and the Fascia Framework — the case that the next leap in AI is connective, not monolithic.

Open access CC BY 4.0 support@fasciaai.com
Published · Multi-volume research series

The full catalog.

Volume I establishes the framework. Volumes II–IV operationalize it — methodology, empirical economics, formal safety guarantees. Plus the Industrial AI Report: where compositional AI is producing measurable enterprise ROI today, sector by sector.

Vol IIJun 2026

FASCIA Capability Index.

The 6-dimension methodology, calibrated. A quarterly tracker of frontier AI progress across reasoning depth, autonomy horizon, multimodal integration, long-range planning, alignment fidelity, and scale composability. Full baseline benchmarks across GPT-5, Claude 4 Opus, Gemini 3 Ultra, and Llama-4 405B.

Q2 2026 Frontier Baseline

Aggregated frontier capability sits at 53.0 / 100, up 7.4 points YoY. Composed 5-agent networks score 67.0 — decisively beating frontier monoliths on autonomy (+18), planning (+16), and composability (+30). The composition thesis is validated empirically. Inter-rater reliability: 0.81 aggregate.

• Published · Jun 2026 · Read PDF →
Vol IIIJun 2026

The Specialist Economy.

Why networks beat monoliths, empirically. An economic model of compositional AI derived from 18 months of GPT Store deployment data — 67 specialists, 14.2M user interactions, full network-effect math.

The Headline Result

Value scaling exponent β = 1.43 — meaningfully higher than App Store (1.31) or AWS Marketplace (1.28). Doubling specialists yields 2.69× user value. Capital implication: the next $100B of AI value capture flows 60/30/10 to compositional platforms, vertical specialists, and frontier monoliths.

• Published · Jun 2026 · Read PDF →
Vol IVJun 2026

Alignment Through Composition.

The formal safety case for connective AI. A 6-layer Composition Alignment Framework with measurable safety guarantees, plus 6 months of production data from Fascia's deployment surface across 4.2M interactions.

The Composition Alignment Theorem

For composed systems with type-contracted integration boundaries, system-level alignment is bounded below by specialist alignment plus boundary verification quality. The bound is computable. 2,553 alignment interventions per million interactions logged in production, 96.0% true-positive rate. Monolithic controls log zero interventions.

• Published · Jun 2026 · Read PDF →
Open access

Research, adopted.

Every paper in the Fascia Framework series publishes under Creative Commons Attribution 4.0. The framework is meant to be adopted, critiqued, improved, and operationalized by other labs.

ASI, if it is reached, will not be reached by one lab. It will be reached by a network of labs that figured out how to coordinate — the same way the systems they build will be. The connective argument applies to the research itself.

For citation correspondence, collaboration inquiries, or to contribute to the next volume: support@fasciaai.com

Research · Position · Methodology · fasciaai.com/research
How to cite: Fascia Research. (2026). The Fascia Framework: A Path to Connective Superintelligence, Volume I. Fascia — An AI Research & Deployment Company. fasciaai.com/research