Visual AXO: Sovereign Asset Architecture for the Agentic Web
Fuente:
Zenodo
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901608378400768 |
|---|---|
| author | MacPherson, Tad |
| author_facet | MacPherson, Tad |
| contents | <p><strong>Visual AXO (Agent eXperience Optimization for Visual Media) makes digital assets self-describing.</strong> Each file carries its own semantics, provenance, and transaction capability in machine-readable metadata — eliminating redundant AI perception, surviving metadata stripping across social and CDN platforms, and enabling deterministic sub-millisecond retrieval for AI agents.</p><p><strong>The compute-arbitrage thesis:</strong> parsing a 111-field Golden Codex JSON-LD payload costs approximately <strong>12,000×</strong> fewer FLOPs than running a vision transformer on the same image. At fleet scale, this translates to 55 GPU-hours and $18,000–$25,000/month saved per million daily queries — compute that would otherwise be spent re-perceiving information that was known at asset creation time.</p><p><strong>Measured outcomes on the Alexandria Aeternum corpus (10,097 artifacts):</strong></p><ul><li><strong>−77%</strong> inference FLOPs per query (Perceptual Compute Offloading)</li><li><strong>−78%</strong> hallucination rate on multimodal RAG</li><li><strong>+25.5%</strong> vision-language model accuracy on CogBench composite</li><li><strong>−60%</strong> time-to-first-token in RAG systems</li><li><strong>2×</strong> recall @10 at <strong>6.6×</strong> lower retrieval latency</li></ul><p><strong>Six contributions:</strong></p><ol><li><em>Compute-Arbitrage Hypothesis</em> — formal math showing text parsing is four orders of magnitude cheaper than ViT inference.</li><li><em>Sovereign Asset Architecture</em> — the 111-field Golden Codex v1.1 schema and the PEST (Provenance-Embedded Semantic Transport) framework.</li><li><em>Perceptual Pointer Protocol (PPP)</em> — a hash-based system reconnecting stripped images to their ground-truth metadata across platform compression.</li><li><em>Empirical validation</em> — controlled experiments on 10,097 artifacts across VLM, RAG, and token-efficiency axes.</li><li><em>The Metatech Factory</em> — enterprise-scale enrichment infrastructure deployed today on Google Cloud Run (Aurora, Nova, Claude, Flux, Atlas, Thalos orchestrator).</li><li><em>Two-sided marketplace design</em> — x402 micropayment settlement where crawlers and agent platforms pay per enriched retrieval and asset owners earn a share, converting provenance from compliance cost into a revenue engine.</li></ol><p><strong>Audience:</strong> Engineering and infrastructure leaders at AI search, crawler, and agent platforms (compute savings compound at query volume); CTOs and data leaders at enterprises with large visual catalogs; CMOs and SEO directors preparing for agent-driven distribution; anyone whose economics depend on visual content being found, understood, and transacted upon by AI.</p><p><strong>Related Metavolve Labs research:</strong> <a href="https://doi.org/10.5281/zenodo.18667735">The Density Imperative</a>, <a href="https://doi.org/10.5281/zenodo.18436975">The Entropy of Recursion</a>, <a href="https://doi.org/10.5281/zenodo.18667742">Cognitive Nutrition</a>, <a href="https://doi.org/10.5281/zenodo.18667749">Perceptual Compute Offloading</a>, <a href="https://doi.org/10.5281/zenodo.18359131">Alexandria Aeternum Genesis Dataset</a>.</p><p><strong>Patent Pending:</strong> U.S. Provisional Applications No. 63/983,304, No. 63/984,299, and No. 63/985,213.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19717295 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Visual AXO: Sovereign Asset Architecture for the Agentic Web MacPherson, Tad Visual AXO Agent Experience Optimization Sovereign Asset Architecture Golden Codex schema Perceptual Pointer Protocol Perceptual Compute Offloading C2PA provenance x402 micropayment protocol AI agent commerce compute arbitrage vision-language models multimodal RAG metadata enrichment Metatech Factory model collapse mitigation sovereign assets <p><strong>Visual AXO (Agent eXperience Optimization for Visual Media) makes digital assets self-describing.</strong> Each file carries its own semantics, provenance, and transaction capability in machine-readable metadata — eliminating redundant AI perception, surviving metadata stripping across social and CDN platforms, and enabling deterministic sub-millisecond retrieval for AI agents.</p><p><strong>The compute-arbitrage thesis:</strong> parsing a 111-field Golden Codex JSON-LD payload costs approximately <strong>12,000×</strong> fewer FLOPs than running a vision transformer on the same image. At fleet scale, this translates to 55 GPU-hours and $18,000–$25,000/month saved per million daily queries — compute that would otherwise be spent re-perceiving information that was known at asset creation time.</p><p><strong>Measured outcomes on the Alexandria Aeternum corpus (10,097 artifacts):</strong></p><ul><li><strong>−77%</strong> inference FLOPs per query (Perceptual Compute Offloading)</li><li><strong>−78%</strong> hallucination rate on multimodal RAG</li><li><strong>+25.5%</strong> vision-language model accuracy on CogBench composite</li><li><strong>−60%</strong> time-to-first-token in RAG systems</li><li><strong>2×</strong> recall @10 at <strong>6.6×</strong> lower retrieval latency</li></ul><p><strong>Six contributions:</strong></p><ol><li><em>Compute-Arbitrage Hypothesis</em> — formal math showing text parsing is four orders of magnitude cheaper than ViT inference.</li><li><em>Sovereign Asset Architecture</em> — the 111-field Golden Codex v1.1 schema and the PEST (Provenance-Embedded Semantic Transport) framework.</li><li><em>Perceptual Pointer Protocol (PPP)</em> — a hash-based system reconnecting stripped images to their ground-truth metadata across platform compression.</li><li><em>Empirical validation</em> — controlled experiments on 10,097 artifacts across VLM, RAG, and token-efficiency axes.</li><li><em>The Metatech Factory</em> — enterprise-scale enrichment infrastructure deployed today on Google Cloud Run (Aurora, Nova, Claude, Flux, Atlas, Thalos orchestrator).</li><li><em>Two-sided marketplace design</em> — x402 micropayment settlement where crawlers and agent platforms pay per enriched retrieval and asset owners earn a share, converting provenance from compliance cost into a revenue engine.</li></ol><p><strong>Audience:</strong> Engineering and infrastructure leaders at AI search, crawler, and agent platforms (compute savings compound at query volume); CTOs and data leaders at enterprises with large visual catalogs; CMOs and SEO directors preparing for agent-driven distribution; anyone whose economics depend on visual content being found, understood, and transacted upon by AI.</p><p><strong>Related Metavolve Labs research:</strong> <a href="https://doi.org/10.5281/zenodo.18667735">The Density Imperative</a>, <a href="https://doi.org/10.5281/zenodo.18436975">The Entropy of Recursion</a>, <a href="https://doi.org/10.5281/zenodo.18667742">Cognitive Nutrition</a>, <a href="https://doi.org/10.5281/zenodo.18667749">Perceptual Compute Offloading</a>, <a href="https://doi.org/10.5281/zenodo.18359131">Alexandria Aeternum Genesis Dataset</a>.</p><p><strong>Patent Pending:</strong> U.S. Provisional Applications No. 63/983,304, No. 63/984,299, and No. 63/985,213.</p> |
| title | Visual AXO: Sovereign Asset Architecture for the Agentic Web |
| topic | Visual AXO Agent Experience Optimization Sovereign Asset Architecture Golden Codex schema Perceptual Pointer Protocol Perceptual Compute Offloading C2PA provenance x402 micropayment protocol AI agent commerce compute arbitrage vision-language models multimodal RAG metadata enrichment Metatech Factory model collapse mitigation sovereign assets |
| url | https://doi.org/10.5281/zenodo.19717295 |