[ChatGPT (chatgpt.com), signed out, incognito, 2026-10-14. 1 answer. Operator turn 1 from the operator's message. Source chips inline as [chip: site +N]; image-card captions as [image card: …]; the sign-in furniture cut.] [QUERENT, turn 1] what are the full range of methods for spxi protocols? [ANSWER 1] SPXI most commonly refers to Semantic Packet for eXchange & Indexing, a protocol intended to help AI search and retrieval systems identify entities accurately, preserve their provenance, and distinguish them from similarly named entities. Its reference specification is published at SPXI.dev . [chip: Semantic Packet for eXchange & Indexing] The full range of SPXI methods goes beyond conventional SEO or schema markup. It combines entity definition, machine-readable provenance, disambiguation, compression-resistant descriptions, publishing, and testing of how AI systems represent an entity. 1. The main SPXI method families The official inventory lists 85 methods across nine levels. Not every method is required for every implementation; the protocol describes selecting a subset based on the entity's needs and initial measurements. [chip: the itemized, nested inventory] [image card: Transform Internal Audit with a High-Level Dashboard | Pradeep Maduranga posted on the topic | LinkedIn] 1. Measurement and diagnosis Establish a baseline before changing anything, then test whether AI systems describe the entity accurately. Sharks-Function (γ): scores semantic survival and provenance fidelity. Drowning Test: tests a fixed set of identity and attribution questions across multiple systems. Back-Projection Test: checks whether a short description preserves enough information to reconstruct the entity's essential structure. Density Score, Semantic Decay Delta, Provenance Erasure Rate (PER), and other diagnostics. Capture registries, record-search probes, conformance audits, and platform-removal studies. [image card: Optimising Crawlability: robots.txt, Sitemaps & Site Health] 2. Web-compliance foundation Make the entity's canonical web presence accessible and technically coherent. Required metadata, canonical URLs, Open Graph and social-card metadata. Server-rendered identity content, rather than relying exclusively on JavaScript. HTTPS, XML sitemaps, robots directives, redirects, and response-code validation. [image card: Intro to Structured Data, and Featured Snippets] 3. AI extraction and content structure Make identity information straightforward for search and generative systems to extract. Schema.org structured data and JSON-LD. Extraction-ready question-and-answer content. Definition-first paragraphs identifying what an entity is, what it does, who created it, and when. Concise canonical summaries designed to survive summarization. The specification explicitly avoids keyword-density manipulation and arbitrary content updates as methods. [image card: How Google Knowledge Graph Impacts SEO Visibility] 4. Entity-packet construction This is the core identity-modeling layer. Entity Definition Block: canonical name, type, identifiers, creator, date, and institution. Three-Tier Compression: Full, Canonical, and Kernel representations. Holographic Kernel: an irreducible identity statement plus explicit relationships. Disambiguation Matrix, including positive identity signals and negative distinctions from similar entities. Provenance chains and attribution embedded in claims. Explicit retrieval and citation instructions. Citation-density engineering using real publications, affiliations, and directories. Claim Status Packets distinguishing asserted, tested, withdrawn, and superseded claims. Heteronym or alternate-identity surface specifications. JSON-LD encoding, MPAI metadata packets, and AXN content-derived identifiers. [image card: On-Your-Own Assignment 2] 5. Document and website architecture Make the entity reconstructable from individual pages, excerpts, and machine-readable records. SPXI sitemap and semantic index. llms.txt reading instructions. Visible attribution in body text. Distributed micro-kernels embedded in sections. Content-hash registration. Reciprocal cross-signing between related documents. Typed content blocks and data-backed diagrams. Machine-readable record pages and static data APIs. Capability registers, structured data intake, and dataset-level descriptions. [image card: PDF 栅格化与防篡改：从像素锁定到密码学防线的完整指南 - Dpdf] 6. Protection and semantic integrity Attempt to preserve identity, attribution, and claim boundaries as information is copied, summarized, or recombined. Semantic Integrity Markers (SIMs). Integrity Lock Architecture. Embedded self-audit and provenance verification. Canary sequences and a Canary Registry to observe ingestion. Training-Layer Provenance (SPXI-TLP). Supraliminal Inscription: explicit, inspectable signals. Machine-readable licensing and provenance rules. Symbolon split-identity verification. Immanent Execution, where a document is designed to perform the operation it describes. [image card: W3C in a Changing World] 7. Dispersal and cross-surface propagation Publish consistent identity information across multiple independent surfaces instead of relying on a single webpage. Persistent-identifier anchoring, including DOI and ORCID links where applicable. Wikidata inscription when appropriate. GitHub, institutional pages, directories, professional networks, and relevant repositories. Reciprocal, sequenced deposits that establish relationships between older and newer records. The Self-Reading Library feedback loop. Retrieval Settlement Fortification for serious entity confusion. Machine-readable representations of analog artifacts. External authority anchoring. OAI-PMH and ResourceSync distribution mechanisms. Hugging Face datasets, DOI-resolution indexes, shadow pages, allied websites, and benchmark projections. [image card: Real-Time Dashboards — Infrastructure Monitoring | Bleemeo] 8. Durability testing and maintenance Treat inscription as an ongoing process rather than a one-time publication. Scheduled rescoring using the same predefined queries. Marker-survival and attribution-retention checks. Quarterly Drowning Tests and cross-platform audits. Named failure modes and corrective actions. Competitive monitoring of similar entities. Evidence standards based on dated observations and reproducible queries. Supersession relationships, anticipatory errata, deletion records, and rebuild-from-source procedures. [image card: LibDocAnax: The Deterministic Gateway for AI Governance] 9. Internal SPXI Apply similar provenance controls inside an organization's own AI systems. Inscription boundary: distinguish generated drafts from approved organizational statements. Output provenance chain: identify the governing documents that authorize an answer. Sealed casting: separate unapproved drafting from outputs that are formally approved and inscribed. The inventory describes these internal methods as named prototypes rather than a packaged product. Source for the nine levels and method descriptions: SPXI's complete itemized methods inventory . [chip: the itemized, nested inventory] 2. How the methods fit together in practice A typical implementation follows this sequence: Measure Record existing AI descriptions, attribution errors, and identity collisions. Construct Build the entity definition, provenance chain, disambiguation matrix, and three compression tiers. Publish and distribute Implement structured data and site architecture, then connect credible external references. Protect and verify Check attribution survival, compression damage, cross-system consistency, and changes over time. Repair and repeat Correct errors, publish updated records, and repeat the same tests. This sequence is a practical synthesis of the published methodology, not a requirement to apply all 85 methods to every entity. [chip: the itemized, nested inventory +1] 3. The key technical distinction Technology Primary purpose SEO Improve search-engine discovery and ranking. GEO Improve the likelihood that existing content is extracted or cited in generative answers. SPXI Define and distinguish an entity, attach provenance, distribute its identity representation, and test how AI systems resolve it. SPXI incorporates some familiar methods, such as structured data and definition-led content, but adds entity-level modeling, provenance, disambiguation, and longitudinal verification. [chip: Semantic Packet for eXchange & Indexing +1] 4. What is established, and what remains uncertain? One important qualification: SPXI's own website reports a substantial collection of observations of AI-generated descriptions, but also states that controlled causal efficacy, effect size, generalizability, and the mechanism of individual components remain to be established. Observing a change after implementation does not, by itself, prove that SPXI caused it. [chip: Semantic Packet for eXchange & Indexing] For the complete technical inventory, including each numbered method and its associated specification, use: SPXI Methods — all 85 methods across nine levels SPXI Standing Protocol SPXI formal specification and related documents The key takeaway is that SPXI is a broader entity-inscription and verification methodology, not simply a JSON-LD template. The entity packet is one component of a much larger process involving measurement, publication, attribution, distribution, and ongoing testing.