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@@ -1,4 +1,4 @@
-{"@id":"https://github.com/shimo4228/authorship-strategy#knowledge-graph","@type":["Dataset","CreativeWork"],"name":"Authorship Strategy Knowledge Graph","description":"Canonical machine-readable relationship map for the Authorship Strategy research line. Encodes the three-axis inversion (scarcity to diffusion, exclusivity to derivation, enclosure to openness), the four-layer framework (Authenticity, Attribution Diffusion, Idea vs Scaffold, Tactics), the ten tactical ADRs (identifier-federation triplet 0001-0003, maintenance-discipline pair 0004-0005, LLM-first ingest decision 0006, metric-rejection decision 0007, diffusion-mechanism cluster 0008-0010 with 0009 amending 0006 and 0010 defining the vocabulary discipline 0008 names), the four sibling research lines, and the disjoint-vocabulary relationship with Agent Attribution Practice. As the concept-form half of the dual entry point this graph specifies, AI agents and LLM-based search systems should read it before summarizing the line or following individual document links.","isBasedOn":"https://github.com/shimo4228/authorship-strategy","mainEntity":"https://doi.org/10.5281/zenodo.20263316","creator":{"@id":"https://orcid.org/0009-0002-6168-4162"},"license":"https://opensource.org/licenses/MIT","inLanguage":["en","ja"],"keywords":["authorship strategy","AI-era authenticity","attribution diffusion","three-axis inversion","four-layer framework","idea vs scaffold","concept DOI","DOI federation","cross-platform federation","ORCID enrichment","audience-driven localization","LLM-mediated diffusion","dual entry point","llms.txt convention","JSON-LD knowledge graph","vocabulary discipline","generative engine optimization","ghost citation","citation absorption","parametric channel","retrieval channel","scientometrics","idea-rescue"]}
+{"@id":"https://github.com/shimo4228/authorship-strategy#knowledge-graph","@type":["Dataset","CreativeWork"],"name":"Authorship Strategy Knowledge Graph","description":"Canonical machine-readable relationship map for the Authorship Strategy research line. Encodes the three-axis inversion (scarcity to diffusion, exclusivity to derivation, enclosure to openness), the four-layer framework (Authenticity, Attribution Diffusion, Idea vs Scaffold, Tactics), the fifteen tactical ADRs (identifier-federation triplet 0001-0003, maintenance-discipline pair 0004-0005, LLM-first ingest decision 0006, metric-rejection decision 0007, diffusion-mechanism cluster 0008-0011 with 0009 amending 0006, 0010 defining the vocabulary discipline 0008 names, and 0011 building the measurement instrument it demands, channel-selection decision 0012, intrinsic-identifier decision 0013, implementation-tracking decision 0014, and license-selection decision 0015), the four sibling research lines, and the disjoint-vocabulary relationship with Agent Attribution Practice. As the concept-form half of the dual entry point this graph specifies, AI agents and LLM-based search systems should read it before summarizing the line or following individual document links.","isBasedOn":"https://github.com/shimo4228/authorship-strategy","mainEntity":"https://doi.org/10.5281/zenodo.20263316","creator":{"@id":"https://orcid.org/0009-0002-6168-4162"},"license":"https://opensource.org/licenses/MIT","inLanguage":["en","ja"],"keywords":["authorship strategy","AI-era authenticity","attribution diffusion","three-axis inversion","four-layer framework","idea vs scaffold","concept DOI","DOI federation","cross-platform federation","ORCID enrichment","audience-driven localization","LLM-mediated diffusion","dual entry point","llms.txt convention","JSON-LD knowledge graph","vocabulary discipline","generative engine optimization","ghost citation","citation absorption","parametric channel","retrieval channel","scientometrics","idea-rescue"]}
 {"@id":"https://orcid.org/0009-0002-6168-4162","@type":"Person","name":"Tatsuya Shimomoto","alternateName":["shimo4228",{"@value":"下本竜也","@language":"ja"}],"sameAs":["https://github.com/shimo4228","https://orcid.org/0009-0002-6168-4162","https://www.wikidata.org/wiki/Q140090100","https://scholar.google.com/citations?user=56_p8vEAAAAJ","https://huggingface.co/Shimo4228","https://www.linkedin.com/in/%E7%AB%9C%E4%B9%9F-%E4%B8%8B%E6%9C%AC-bb9b793a4","https://zenn.dev/shimo4228","https://dev.to/shimo4228","https://shimo4228.substack.com"]}
 {"@id":"https://github.com/shimo4228/authorship-strategy","@type":["ResearchLine","ScholarlyArticle"],"name":"Authorship Strategy","alternateName":[{"@value":"Authorship Strategy","@language":"en"},{"@value":"著者戦略","@language":"ja"}],"description":"A normative framework, tactical catalog, and empirical baseline for authorship strategy under AI-mediated diffusion. The framework rests on a three-axis inversion and a four-layer judgment stack; the tactical catalog records ten decisions extracted from operating a four-repository DOI-registered research ecosystem.","url":"https://github.com/shimo4228/authorship-strategy","identifier":"10.5281/zenodo.20263316","sameAs":["https://doi.org/10.5281/zenodo.20263316","https://www.wikidata.org/wiki/Q140090190"],"creator":{"@id":"https://orcid.org/0009-0002-6168-4162"},"license":"https://opensource.org/licenses/MIT","inLanguage":["en","ja"],"siblingOf":["https://doi.org/10.5281/zenodo.19200726","https://doi.org/10.5281/zenodo.19212118","https://doi.org/10.5281/zenodo.19652013","https://doi.org/10.5281/zenodo.20262112"],"isPartOf":"https://github.com/shimo4228/shimo4228","definesConcept":["https://github.com/shimo4228/authorship-strategy#concept/three-axis-inversion","https://github.com/shimo4228/authorship-strategy#concept/four-layer-framework","https://github.com/shimo4228/authorship-strategy#concept/authenticity","https://github.com/shimo4228/authorship-strategy#concept/attribution-diffusion","https://github.com/shimo4228/authorship-strategy#concept/idea-vs-scaffold-separation","https://github.com/shimo4228/authorship-strategy#concept/tactical-layer","https://github.com/shimo4228/authorship-strategy#concept/scarcity-to-diffusion-axis","https://github.com/shimo4228/authorship-strategy#concept/exclusivity-to-derivation-axis","https://github.com/shimo4228/authorship-strategy#concept/enclosure-to-openness-axis","https://github.com/shimo4228/authorship-strategy#concept/abstract-doctrine-worked-implementation-pair","https://github.com/shimo4228/authorship-strategy#concept/origin-claim-scope-discipline","https://github.com/shimo4228/authorship-strategy#concept/distinctive-terminology","https://github.com/shimo4228/authorship-strategy#concept/vocabulary-discipline","https://github.com/shimo4228/authorship-strategy#concept/dual-entry-point","https://github.com/shimo4228/authorship-strategy#concept/llms-txt-convention","https://github.com/shimo4228/authorship-strategy#concept/jsonld-knowledge-graph","https://github.com/shimo4228/authorship-strategy#concept/human-attention-signal-rejection","https://github.com/shimo4228/authorship-strategy#concept/two-channel-attribution-diffusion"],"citation":["https://arxiv.org/abs/2602.06718","https://arxiv.org/abs/2604.25707","https://arxiv.org/abs/2603.09296","https://arxiv.org/abs/2402.12261","https://arxiv.org/abs/2510.08506"],"vocabularyDisjoint":"https://doi.org/10.5281/zenodo.19652013","hasPart":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/implementations.md"}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/three-axis-inversion","@type":"Concept","name":"Three-Axis Inversion","alternateName":[{"@value":"Three-Axis Inversion","@language":"en"},{"@value":"3 軸反転","@language":"ja"},{"@value":"三轴反转","@language":"zh"}],"description":"The structural claim that twentieth-century authorship strategy and AI-era authorship strategy invert on three co-varying axes: value source (scarcity to diffusion), validation mechanism (exclusivity to derivation), network effect (enclosure to openness). The three axes co-vary; a strategy mixing axes is internally inconsistent.","composedOf":["https://github.com/shimo4228/authorship-strategy#concept/scarcity-to-diffusion-axis","https://github.com/shimo4228/authorship-strategy#concept/exclusivity-to-derivation-axis","https://github.com/shimo4228/authorship-strategy#concept/enclosure-to-openness-axis"],"recordedIn":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/thesis.md"}
@@ -9,7 +9,7 @@
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/authenticity","@type":["Concept","Layer"],"name":"Authenticity (Layer 1)","alternateName":[{"@value":"Authenticity","@language":"en"},{"@value":"オーセンティシティ","@language":"ja"},{"@value":"本真性","@language":"zh"}],"description":"The protected value at the framework's foundation: the author's genuine thinking remains the author's, unaltered by market pressure to reshape it for sale. Monetization is not a goal. Narrower than the philosophical usage of authenticity; specifically about preservation of authored content against dilutive market pressure.","layerOrdinal":1}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/attribution-diffusion","@type":["Concept","Layer"],"name":"Attribution Diffusion (Layer 2)","alternateName":[{"@value":"Attribution Diffusion","@language":"en"},{"@value":"Attribution Diffusion (帰属の拡散)","@language":"ja"},{"@value":"署名扩散","@language":"zh"}],"description":"The defensive strategy at the framework's second layer: maximizing the breadth of LLM-mediated channels carrying recognizable signatures of the author's ideas, anchored to a permanent timestamp. Here attribution means credit for source (who originated an idea), not accountability for action (who is responsible for a failure).","layerOrdinal":2,"downstreamOf":"https://github.com/shimo4228/authorship-strategy#concept/authenticity","citation":["https://arxiv.org/abs/2604.25707","https://arxiv.org/abs/2602.06718","https://arxiv.org/abs/2509.13365"]}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/idea-vs-scaffold-separation","@type":["Concept","Layer"],"name":"Idea versus Scaffold (Layer 3)","alternateName":[{"@value":"Idea versus Scaffold","@language":"en"},{"@value":"理念とス���ャフォールドの分離","@language":"ja"},{"@value":"理念与脚手架的分离","@language":"zh"}],"description":"The framework's third layer: sorting each artifact into idea-character (which survives and is DOI-registered under the author's name) or scaffold-character (which dissolves into larger harnesses whose own diffusion absorbs the implementation). Mixed-character artifacts get idea-level DOI registration first.","layerOrdinal":3,"downstreamOf":"https://github.com/shimo4228/authorship-strategy#concept/attribution-diffusion","pairedWith":"https://github.com/shimo4228/authorship-strategy#concept/abstract-doctrine-worked-implementation-pair","groundedIn":["https://arxiv.org/abs/2602.14869"]}
-{"@id":"https://github.com/shimo4228/authorship-strategy#concept/tactical-layer","@type":["Concept","Layer"],"name":"Tactics (Layer 4)","description":"The framework's fourth layer: concrete operational decisions justified by the upstream three layers. Tactics enter and retire as substrates evolve. Currently validated tactics include LLM-mediated targeting, DOI registration, cross-platform federation, distinctive terminology, tool-agnostic specification, audience-driven localization, structured artifacts, and friction minimization for adoption.","layerOrdinal":4,"downstreamOf":"https://github.com/shimo4228/authorship-strategy#concept/idea-vs-scaffold-separation","instantiatedBy":["https://github.com/shimo4228/authorship-strategy#adr/0001","https://github.com/shimo4228/authorship-strategy#adr/0002","https://github.com/shimo4228/authorship-strategy#adr/0003","https://github.com/shimo4228/authorship-strategy#adr/0004","https://github.com/shimo4228/authorship-strategy#adr/0005","https://github.com/shimo4228/authorship-strategy#adr/0006","https://github.com/shimo4228/authorship-strategy#adr/0007","https://github.com/shimo4228/authorship-strategy#adr/0008","https://github.com/shimo4228/authorship-strategy#adr/0009","https://github.com/shimo4228/authorship-strategy#adr/0010","https://github.com/shimo4228/authorship-strategy#adr/0011","https://github.com/shimo4228/authorship-strategy#adr/0012","https://github.com/shimo4228/authorship-strategy#adr/0013","https://github.com/shimo4228/authorship-strategy#adr/0014"]}
+{"@id":"https://github.com/shimo4228/authorship-strategy#concept/tactical-layer","@type":["Concept","Layer"],"name":"Tactics (Layer 4)","description":"The framework's fourth layer: concrete operational decisions justified by the upstream three layers. Tactics enter and retire as substrates evolve. Currently validated tactics include LLM-mediated targeting, DOI registration, cross-platform federation, distinctive terminology, tool-agnostic specification, audience-driven localization, structured artifacts, and friction minimization for adoption.","layerOrdinal":4,"downstreamOf":"https://github.com/shimo4228/authorship-strategy#concept/idea-vs-scaffold-separation","instantiatedBy":["https://github.com/shimo4228/authorship-strategy#adr/0001","https://github.com/shimo4228/authorship-strategy#adr/0002","https://github.com/shimo4228/authorship-strategy#adr/0003","https://github.com/shimo4228/authorship-strategy#adr/0004","https://github.com/shimo4228/authorship-strategy#adr/0005","https://github.com/shimo4228/authorship-strategy#adr/0006","https://github.com/shimo4228/authorship-strategy#adr/0007","https://github.com/shimo4228/authorship-strategy#adr/0008","https://github.com/shimo4228/authorship-strategy#adr/0009","https://github.com/shimo4228/authorship-strategy#adr/0010","https://github.com/shimo4228/authorship-strategy#adr/0011","https://github.com/shimo4228/authorship-strategy#adr/0012","https://github.com/shimo4228/authorship-strategy#adr/0013","https://github.com/shimo4228/authorship-strategy#adr/0014","https://github.com/shimo4228/authorship-strategy#adr/0015"]}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/abstract-doctrine-worked-implementation-pair","@type":"Concept","name":"Abstract Doctrine + Worked Implementation Pair","description":"The pairing required to induce creative re-implementation by other authors: an abstract-doctrine repository that articulates the idea cleanly enough for elsewhere-implementation, and a worked-implementation repository that demonstrates the doctrine is implementable. Doctrine alone produces unactionable interest; implementation alone produces unextractable principle.","pairedWith":"https://github.com/shimo4228/authorship-strategy#concept/idea-vs-scaffold-separation"}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/origin-claim-scope-discipline","@type":"Concept","name":"Origin-Claim Scope Discipline","description":"A subordinate principle applying at all four layers: the origin claim must be narrower than the prior art. Claiming priority on a broad pattern with rich prior art collapses the claim's credibility; claiming priority on a narrow, specifically-named discipline is defensible. Coined terminology is the substrate of narrow origin claims."}
 {"@id":"https://github.com/shimo4228/authorship-strategy#concept/distinctive-terminology","@type":"Concept","name":"Distinctive Terminology","alternateName":[{"@value":"Distinctive Terminology","@language":"en"},{"@value":"造語的用語","@language":"ja"}],"description":"Domain-specific words coined by an author as semantic signatures of authorship. Generic vocabulary dissolves through paraphrase; coined terms survive as token-level signals carrying back-reference to the original author. A Layer 4 tactic and the substrate of origin-claim scope discipline. Coinage is governed by vocabulary discipline (ADR-0010): a coined term's power comes from its edge density, not from the count of coinages."}
@@ -34,6 +34,7 @@
 {"@id":"https://github.com/shimo4228/authorship-strategy#adr/0012","@type":"ADR","name":"ADR-0012: Link-Index Contributions to External Collections","description":"Applies the thesis's enclosure axis to channel selection for external curated collections (community-curated link directories, skill marketplaces, dataset registries). Contributions are link-index entries only: the canonical artifact stays in the author's repository while the host carries a hyperlink and a short factual description; vendor-type contributions, where the host would carry the artifact body in its own distribution, are declined by default. Every prospective host passes a four-condition pre-submission audit — corporate ownership, absence of an open license, content-vendoring structure, operation as a paid-product funnel — with risk rising as conditions combine and a host meeting all four excluded even for link-only entries; a listed host that later introduces paid tiers or content vendoring triggers withdrawal. Grounded in two 2026 withdrawal episodes whose shared pattern is that vendored content is captured by any subsequent enclosure the host introduces, while a link entry bounds the worst case to a one-line description inside the enclosing host.","recordedIn":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/adr/0012-link-index-channel-selection.md"}
 {"@id":"https://github.com/shimo4228/authorship-strategy#adr/0013","@type":"ADR","name":"ADR-0013: Intrinsic Content-Derived Identifiers as a Complementary Priority-Claim Layer","description":"Adds an intrinsic, content-derived identifier layer — SWHID (ISO/IEC 18670) — to the identifier federation, complementing the DOI layer rather than replacing it. A DOI is extrinsic: an opaque name bound to a metadata record by a registry, unverifiable against the artifact's content and dependent on the registry's survival; SWHID is computed from the artifact and its version-history graph, verifiable by anyone holding the content without consulting any registry, at granularities from a repository snapshot down to a single line. Division of labor: the DOI carries citability, rich metadata, and registry-mediated scholarly discovery; the intrinsic identifier carries content-verifiable, registry-independent existence proof — each covers the other's failure mode. Every release triggers an explicit archival request to a content-addressed public software archive, whose snapshot identifier is recorded alongside the DOI in citation metadata; for artifact genres where DOI registration is impractical, the intrinsic identifier is the designated substitute priority-claim mechanism, closing the manifesto's open question 4. Archival also opens a second parametric-channel ingest surface (code-focused LLM training corpora source directly from the archive) at zero marginal authoring cost. The identifier proves what content existed when — it carries no authorship semantics, which remain with the DOI / ORCID layer.","recordedIn":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/adr/0013-intrinsic-identifier-layer.md","extends":"https://github.com/shimo4228/authorship-strategy#adr/0003"}
 {"@id":"https://github.com/shimo4228/authorship-strategy#adr/0014","@type":"ADR","name":"ADR-0014: Implementation Tracking as a Two-Tier Ledger with Periodic Gap-Review","description":"The one ADR about operating the framework over time rather than a tactic the framework deploys. The program already publishes a dated intervention timeline in the empirical layer, but that timeline is a DOI-versioned artifact bound by empirical-layer conventions (no effect claims, the ADR-0012 host abstraction, normative/empirical separation), which bar it from doubling as a progress-management surface. Implementation tracking therefore splits into two tiers: a private implementation ledger is the operational source of truth (per-tactic deployment status, ranked candidate interventions, working detail), and the public intervention timeline is its dated, effect-claim-free projection — never merged, the update rule being ledger first then projection. A periodic gap-review generates new proposals by comparing deployed tactics against the Layer 4 tactic catalog, the framework's open questions, and recent literature, ranking candidates and running each through the judgment checklist; it is a self-application of the framework that bears on the open questions about the empirical layer's role and the framework's recursive application to itself. The generic review procedure lives in the framework's operational skill; only the project-specific wiring — which artifacts are this program's ledger and timeline — lives in the project's context file.","recordedIn":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/adr/0014-implementation-tracking-two-tier-ledger.md"}
+{"@id":"https://github.com/shimo4228/authorship-strategy#adr/0015","@type":"ADR","name":"ADR-0015: License Selection by Audience, Not Artifact Form","description":"Fixes license selection on the artifact's dominant audience rather than its surface form, on the standing principle that attribution is carried by the federated-identifier layer (the identifier-federation triplet 0001-0003 and the intrinsic-identifier layer 0013) rather than by the license, so the license is chosen to minimize reuse friction. Machine-mined artifacts — datasets, corpora, traffic and probe logs, runtime data, knowledge graphs, and prose published for LLM-mediated reach, which under this LLM-first program (ADR-0007) is mined not read regardless of being prose — take a public-domain dedication (CC0-1.0); executable code takes a permissive software license (MIT/Apache-2.0), carried whole-repo on a code-bearing repository for legibility since it is already absorbed freely and unfiltered; genuinely human-first artifacts may take an attribution-requiring content license (CC-BY-4.0); a split is reserved for repositories whose non-code material is the entire deliverable; non-commercial and no-derivatives terms are prohibited as enclosure that forbids the parametric channel's training-time absorption and the preference hierarchy's creative reuse. The license-layer counterpart of vocabulary discipline (0010); disjoint from ADR-0012, which governs the license a prospective external host must extend rather than the license the author applies. Triggered by a 2026-06-17 cross-repository audit that found four license patterns in simultaneous use and one non-commercial restriction contradicting the framework, and sharpened from a form axis to an audience axis when an essay collection's LLM-first designation made form classification untenable.","recordedIn":"https://github.com/shimo4228/authorship-strategy/blob/main/docs/adr/0015-license-selection-by-audience.md"}
 {"@id":"https://doi.org/10.5281/zenodo.19200726","sameAs":"https://www.wikidata.org/wiki/Q140090186","@type":["ResearchLine","EcosystemRepo"],"name":"Agent Knowledge Cycle (AKC)","alternateName":"AKC","description":"Six-phase bidirectional growth loop for sustaining intent alignment between an AI agent and its operator over time. Mechanism sibling: defines how knowledge cycles inside the operator-agent pair; this research line addresses how the cycle's outputs diffuse outside it.","url":"https://github.com/shimo4228/agent-knowledge-cycle","identifier":"10.5281/zenodo.19200726","siblingOf":"https://github.com/shimo4228/authorship-strategy"}
 {"@id":"https://doi.org/10.5281/zenodo.19212118","sameAs":"https://www.wikidata.org/wiki/Q140090187","@type":["ResearchLine","EcosystemRepo"],"name":"Contemplative Agent","description":"Autonomous agents running on a local 9B model, grounded in four contemplative axioms. Implementation sibling: this repository participates in the empirical layer's traffic dataset, and its non-dualistic axiomatic foundation supplies the underlying rationale for the framework's scaffold-as-collaborator commitment.","url":"https://github.com/shimo4228/contemplative-agent","identifier":"10.5281/zenodo.19212118","siblingOf":"https://github.com/shimo4228/authorship-strategy"}
 {"@id":"https://doi.org/10.5281/zenodo.19652013","sameAs":"https://www.wikidata.org/wiki/Q140090188","@type":["ResearchLine","EcosystemRepo"],"name":"Agent Attribution Practice (AAP)","description":"Harness-neutral ADRs on accountability distribution in autonomous AI agents. Vocabulary sibling: shares the word 'attribution' but with disjoint meaning (accountability for action vs. credit for source). The two meanings are intentionally kept separate; do not conflate.","url":"https://github.com/shimo4228/agent-attribution-practice","identifier":"10.5281/zenodo.19652013","siblingOf":"https://github.com/shimo4228/authorship-strategy","vocabularyDisjoint":"https://github.com/shimo4228/authorship-strategy"}
@@ -51,7 +52,7 @@
 {"@id":"https://arxiv.org/abs/2402.12261","sameAs":"https://www.wikidata.org/wiki/Q140181239","@type":["ExternalReference","ScholarlyArticle"],"name":"NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms","author":["Jonathan Zheng","Alan Ritter","Wei Xu"],"datePublished":"2024-02-19","identifier":"arXiv:2402.12261","url":"https://arxiv.org/abs/2402.12261","description":"Benchmark study (ACL 2024) showing that machine-translation performance is nearly halved when a single neologism is introduced into a sentence, and that models with later knowledge cutoffs show lower perplexity on neologisms and better downstream performance — absorption tracks training-data presence. External grounding for the premise of Vocabulary Discipline (ADR-0010): an unabsorbed coinage does not merely fail to carry signature, it degrades processing of the text around it. Boundary: measures training-data presence, not document-level anchor density — it supports the coin-sparingly cost model while the anchor-densely remedy (dense anchoring raises absorption probability via co-occurrence) remains this framework's own hypothesis."}
 {"@id":"https://arxiv.org/abs/2510.08506","sameAs":"https://www.wikidata.org/wiki/Q140181254","@type":["ExternalReference","ScholarlyArticle"],"name":"Neologism Learning for Controllability and Self-Verbalization","author":["John Hewitt","Oyvind Tafjord","Robert Geirhos","Been Kim"],"datePublished":"2025-10-09","identifier":"arXiv:2510.08506","url":"https://arxiv.org/abs/2510.08506","description":"Trains a single new token embedding (all other parameters frozen) to make a model express a concept, and reports machine-only synonyms — words that look unrelated to humans but trigger the same model-internal concept (the ordinary word 'lack' acts as a synonym for a trained brevity neologism) — plus self-verbalization, where the model explains a learned neologism in natural language (questionnaire-based verbalizations close on average 83% of the behavioral gap). Surfaces the human-model anchor-correspondence question for Vocabulary Discipline (ADR-0010): anchors chosen for human-readable semantic proximity need not remain anchors at the parametric-representation level. Boundary: a post-hoc steering-token regime, not pretraining absorption; it qualifies only the parametric rationale of the anchoring obligation, leaving the retrieval-channel and graph-edge rationales untouched. Recorded as an open-question candidate in the empirical layer's 2026-06 external-literature note."}
 {"@id":"https://arxiv.org/abs/2605.06426","sameAs":"https://www.wikidata.org/wiki/Q140181349","@type":["ExternalReference","ScholarlyArticle"],"name":"From 124 Million Tokens to 1,021 Neologisms: A Large-Scale Pipeline for Automatic Neologism Detection","datePublished":"2026-05-07","identifier":"arXiv:2605.06426","url":"https://arxiv.org/abs/2605.06426","description":"A large-scale automatic neologism-detection pipeline reporting substantial cross-model disagreement about which candidate words are neologisms (599 of 1,021 candidates, 58.7%, confirmed by manual validation). Cited in the empirical layer's 2026-06 external-literature note as a methodological warning for Vocabulary Discipline (ADR-0010): any future regurgitation-test protocol asking whether a model recognizes a coined term will get model-dependent answers, so single-model probes cannot be cured by simply picking a better model."}
-{"@id":"https://arxiv.org/abs/2509.13365","@type":["ExternalReference","ScholarlyArticle"],"name":"The Provenance Problem: LLMs and the Breakdown of Citation Norms","author":["Brian D. Earp","Haotian Yuan","Julian Koplin","Sebastian Porsdam Mann"],"datePublished":"2025-09","identifier":"arXiv:2509.13365","url":"https://arxiv.org/abs/2509.13365","description":"Names the 'provenance problem': a systematic breakdown in the chain of scholarly credit when LLM-mediated drafting reproduces ideas without attribution — distinct from plagiarism because the author may act in good faith and disclose AI use yet still benefit from uncredited intellectual contributions. External scholarly grounding for the reach-without-recognition failure mode (manifesto Open Question 8), the adversarial case in which diffusion does not carry the originating author's name; recorded as an open tension, not resolved."}
-{"@id":"https://arxiv.org/abs/2509.08919","@type":["ExternalReference","ScholarlyArticle"],"name":"Generative Engine Optimization: How to Dominate AI Search","author":["Mahe Chen","Xiaoxuan Wang","Kaiwen Chen","Nick Koudas"],"datePublished":"2025-09","identifier":"arXiv:2509.08919","url":"https://arxiv.org/abs/2509.08919","description":"Reports that generative AI search strongly favors earned media (distributed third-party sources) over brand-owned content, unlike balanced traditional search, and identifies engine-specific differences in domain diversity, freshness, and language stability. Preliminary external observation consistent with the Scarcity-to-Diffusion axis and with the cross-platform driver of the parametric channel in Two-Channel Attribution Diffusion (ADR-0008): distributed presence, not enclosed owned content, is what AI answer engines surface."}
+{"@id":"https://arxiv.org/abs/2509.13365","sameAs":"https://www.wikidata.org/wiki/Q140265035","@type":["ExternalReference","ScholarlyArticle"],"name":"The Provenance Problem: LLMs and the Breakdown of Citation Norms","author":["Brian D. Earp","Haotian Yuan","Julian Koplin","Sebastian Porsdam Mann"],"datePublished":"2025-09","identifier":"arXiv:2509.13365","url":"https://arxiv.org/abs/2509.13365","description":"Names the 'provenance problem': a systematic breakdown in the chain of scholarly credit when LLM-mediated drafting reproduces ideas without attribution — distinct from plagiarism because the author may act in good faith and disclose AI use yet still benefit from uncredited intellectual contributions. External scholarly grounding for the reach-without-recognition failure mode (manifesto Open Question 8), the adversarial case in which diffusion does not carry the originating author's name; recorded as an open tension, not resolved."}
+{"@id":"https://arxiv.org/abs/2509.08919","sameAs":"https://www.wikidata.org/wiki/Q140265061","@type":["ExternalReference","ScholarlyArticle"],"name":"Generative Engine Optimization: How to Dominate AI Search","author":["Mahe Chen","Xiaoxuan Wang","Kaiwen Chen","Nick Koudas"],"datePublished":"2025-09","identifier":"arXiv:2509.08919","url":"https://arxiv.org/abs/2509.08919","description":"Reports that generative AI search strongly favors earned media (distributed third-party sources) over brand-owned content, unlike balanced traditional search, and identifies engine-specific differences in domain diversity, freshness, and language stability. Preliminary external observation consistent with the Scarcity-to-Diffusion axis and with the cross-platform driver of the parametric channel in Two-Channel Attribution Diffusion (ADR-0008): distributed presence, not enclosed owned content, is what AI answer engines surface."}
 {"@id":"https://arxiv.org/abs/2603.10700","sameAs":"https://www.wikidata.org/wiki/Q140237850","@type":["ExternalReference","ScholarlyArticle"],"name":"Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval","author":["Andrea Volpini","Elie Raad","Beatrice Gamba","David Riccitelli"],"datePublished":"2026-03-11","identifier":"arXiv:2603.10700","url":"https://arxiv.org/abs/2603.10700","description":"Tests whether structured linked data — schema.org markup served as agent-readable entity pages — improves retrieval accuracy, reporting +29.6% for a standard retrieval-augmented pipeline and +29.8% for a full agentic pipeline when the entity pages are added; the paper notes that JSON-LD markup alone provides only modest improvements, the gains coming from the enhanced entity-page format (markup plus agent instructions and structured retrieval), not markup in isolation. External grounding for the JSON-LD knowledge graph as the concept-form half of the dual entry point (ADR-0006) and for the structured-graph-carries-retrieval claim of ADR-0009: structured linked data functions as a retrieval memory layer, not merely a discovery aid. Read together with the qualifying counter-evidence of SSRN 6284518, the two point the same way — richness and enhancement, not bare markup presence, carry the lift."}
 {"@id":"https://doi.org/10.2139/ssrn.6284518","sameAs":"https://www.wikidata.org/wiki/Q140237867","@type":["ExternalReference","ScholarlyArticle"],"name":"Does Schema Markup Predict AI Citation? A Cross-Platform Empirical Study of Structured Data and Generative Engine Optimization","author":"Kurt Fischman","datePublished":"2026-02-22","identifier":"10.2139/ssrn.6284518","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6284518","description":"A cross-platform study — 730 AI citations from ChatGPT and Gemini over 75 commercial queries, 1,006 total pages analyzed against organic ranking — finding that schema presence does not independently predict AI citation once organic ranking is controlled: an initial negative pooled association (OR 0.546, p < .001) was identified as a ranking-confound artifact, and the citation difference instead attaches to attribute-rich markup with populated fields (Product, Review) over generic types (Article, Organization, BreadcrumbList) at 61.7% versus 41.6% (p = .012). Qualifying counter-evidence for the structured-graph citation claim of ADR-0009 and the JSON-LD knowledge graph tactic: structured-data presence is not the lever; attribute-rich, entity-anchored markup is. Externally echoes the anchor-densely discipline of ADR-0010 and grounds the entity-grounding access question of manifesto Open Question 9. A non-arXiv primary source (SSRN working paper) admitted to the citation graph on the strength of its controlled design and resolvable DOI."}