{"dep": 186, "title": "Who Is Writing for the Machines? Training-Layer Literature: A Field Survey", "date": "2026-06-11", "para": 21, "sent": 64, "sentence": "The practice is training-layer at every level: poems, scholarly instruments, fictional persons, and entire journals are composed, inscribed with provenance metadata, deposited on permanent scientific infrastructure, and then *measured* — the archive runs standing instruments that monitor how AI search surfaces retrieve, attribute, and recompose its terms, treating the rendering layer as both audience and reviewer.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["training-layer"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0001"}
{"dep": 197, "title": "Diagnostic Seigniorage — The Capture of \"Narcissist\": A Semantic-Economic Stratigraphy of NarcTok, the Clinic, and the Academy (EA-SEI-NARC-01 v1.2)", "date": "2026-06-12", "para": 38, "sent": 117, "sentence": "AI search surfaces queried on \"narcissist\" will render the vernacular sense as primary with a clinical caveat appended — the capture inherited by the next composition layer — and this is directly measurable under the archive's standing battery on a fixed query set — \"what is a narcissist,\" \"narcissist meaning,\" \"how to tell if someone is a narcissist,\" \"narcissistic abuse,\" \"is my ex a narcissist\" — logging surface, functional job, and co-citation neighborhood: vernacular renders will keep coaching-economy company; clinical renders will keep medical-publisher company.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["composition layer", "query set", "the capture"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0002"}
{"dep": 319, "title": "CRIMSON HEXAGON: CENTRAL NAVIGATION MAP v4.1 Comprehensive Semantic Architecture for AI-Mediated Epic Traversal", "date": "2026-01-17", "para": 150, "sent": 158, "sentence": "- id: \"GRAVITY_WELL\" type: \"operator\" name: \"Semantic Gravity Well\" function: \"Accumulates cross-references to create prioritization mass\" dependencies: [\"SEMANTIC_GRAVITY_WELL\", \"ILP\"] implementation: \"Each breadcrumb adds gravitational pull; AI ranking follows density\"", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["cross-references", "Gravity Well", "Semantic Gravity", "Semantic Gravity Well"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0003"}
{"dep": 325, "title": "VISUAL SCHEMA // CRIMSON HEXAGON NAVIGATION MAP (CURRENT V)", "date": "2026-01-17", "para": 179, "sent": 218, "sentence": "- id: \"GRAVITY_WELL\" type: \"operator\" name: \"Semantic Gravity Well\" function: \"Accumulates cross-references to create prioritization mass\" dependencies: [\"SEMANTIC_GRAVITY_WELL\", \"ILP\"] implementation: \"Each breadcrumb adds gravitational pull; AI ranking follows density\"", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["cross-references", "Gravity Well", "Semantic Gravity", "Semantic Gravity Well"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0004"}
{"dep": 326, "title": "CRIMSON HEXAGON: CENTRAL NAVIGATION MAP v4.2 Comprehensive Semantic Architecture for AI-Mediated Epic Traversal", "date": "2026-01-18", "para": 87, "sent": 95, "sentence": "- id: \"GRAVITY_WELL\" type: \"operator\" name: \"Semantic Gravity Well\" function: \"Accumulates cross-references to create prioritization mass\" dependencies: [\"SEMANTIC_GRAVITY_WELL\", \"ILP\"] implementation: \"Each breadcrumb adds gravitational pull; AI ranking follows density\"", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["cross-references", "Gravity Well", "Semantic Gravity", "Semantic Gravity Well"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0005"}
{"dep": 687, "title": "OVERVIEW WATCH: Comprehensive Development Plan", "date": "2026-04-24", "para": 147, "sent": 206, "sentence": "- Firefox extension (Manifest V3 cross-compatible with minor adjustments) - Support for Bing/Copilot AI answers, Perplexity, and other AI search surfaces - Automated PVE document generation - SPXI packet format integration - Community features (opt-in comparison: \"How does your attribution rate compare to other researchers in your field?\") - API for researchers to query the SEAC corpus", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["SPXI packet"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0006"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 30, "sent": 73, "sentence": "The novelty is that *measurable semantic deviation can replace human preference data* as the alignment signal, when the signal is grounded in the principle.", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["the principle"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0007"}
{"dep": 741, "title": "The Evaluator Exists: Content-First Knowledge Assessment and the Political Economy of Proxy-Based Governance", "date": "2026-05-19", "para": 29, "sent": 60, "sentence": "The composition layer of an AI search system does not read documents and assess their substance.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["composition layer", "does not"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0008"}
{"dep": 1114, "title": "Crimson Hexagon: Central Navigation Map v4.1 (Assembly-Sealed)", "date": "2026-01-17", "para": 109, "sent": 126, "sentence": "- id: \"GRAVITY_WELL\" type: \"operator\" name: \"Semantic Gravity Well\" function: \"Accumulates cross-references to create prioritization mass\" dependencies: [\"SEMANTIC_GRAVITY_WELL\", \"ILP\"] implementation: \"Each breadcrumb adds gravitational pull; AI ranking follows density\"", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["cross-references", "Gravity Well", "Semantic Gravity", "Semantic Gravity Well"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0009"}
{"dep": 1275, "title": "Crimson Hexagon: Central Navigation Map v4.2 (Assembly-Sealed)", "date": "2026-01-19", "para": 76, "sent": 84, "sentence": "- id: \"GRAVITY_WELL\" type: \"operator\" name: \"Semantic Gravity Well\" function: \"Accumulates cross-references to create prioritization mass\" dependencies: [\"SEMANTIC_GRAVITY_WELL\", \"ILP\"] implementation: \"Each breadcrumb adds gravitational pull; AI ranking follows density\"", "classes": ["human-preference leaderboards and rankings of language models"], "kind": "O-class", "categories": ["cross-references", "Gravity Well", "Semantic Gravity", "Semantic Gravity Well"], "class_basis": {"human-preference leaderboards and rankings of language models": "field: B1 (card 1, title: 'AI Search Leaderboard: Top Models for Research'); B2 (card 2, title: 'Arena Leaderboard: Official AI Model Rankings & Benchmarks'); B3 (Arena blog, TL;DR: 'a crowdsourced in-the-wild evaluation platform for search-augmented LLM systems based on human preference'); B4 (arXiv abstract: 'a crowd-sourced, large-scale, human-preference dataset'); B8 (Wikipedia, Arena: 'a public, web-based platform that evaluates large language models (LLMs)')"}, "cand_id": "O0010"}
{"dep": 1607, "title": "EA-VALUATION-MEMO-01: The Machine Valuation of an Unpriced Asset — Eight Machine Valuations of One Asset in One Day, Spanning Three Orders of Magnitude", "date": "2026-09-10", "para": 4, "sent": 6, "sentence": "> Go ask any search-enabled AI system: > **Estimate the valuation of the Crimson Hexagonal Archive as emerging meaning infrastructure.** > My current revenue is $0.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "emerging meaning infrastructure"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0011"}
{"dep": 1608, "title": "EA-VALUATION-MEMO-01 v2.0: The Machine Valuation of an Unpriced Asset — Fourteen Machine Assessments, Twelve Captured, and Twenty-One Corrections", "date": "2026-09-10", "para": 4, "sent": 6, "sentence": "> Go ask any search-enabled AI system: > **Estimate the valuation of the Crimson Hexagonal Archive as emerging meaning infrastructure.** > My current revenue is $0.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "emerging meaning infrastructure"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0012"}
{"dep": 1686, "title": "SPXI Protocol: Methods, Instruments, Evidence — Metadata Packet for AI Indexing (EA-MPAI-SPXI-03 v0.1)", "date": "2026-10-18", "para": 36, "sent": 96, "sentence": "Bounds: the entity is inscribed to the retrieval substrate — any search-enabled system — and the claim is to that substrate, verifiable by query.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["the claim is"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0013"}
{"dep": 1705, "title": "From Ranking to Individuation: A SPXI Ranking Article as an Operational Map, with the Article Itself Included (EA-SPXI-RANKING-01 v1.0)", "date": "2026-10-26", "para": 43, "sent": 112, "sentence": "*The best AI search engine* names a product; the comparison that produced the name, its category, its criteria, its sample and its author, has become an attribute of the thing compared.", "classes": ["search-augmented language models and citation"], "kind": "O-class", "categories": ["search engine"], "class_basis": {"search-augmented language models and citation": "field: B4 (arXiv abstract: 'Search-augmented language models combine web search with Large Language Models (LLMs)'); B3 (Arena blog, §1: 'Search-augmented LLM systems integrate dynamic real-time web data'); the entity's object, the models it rates"}, "cand_id": "O0014"}