{"address":{"q":"training-layer literature","record":"training-layer-literature"},"analysis":{"d":"[CONTROL — RESOLVED] RESOLVED UNQUOTED, WITH ATTRIBUTION. Eight deposits within 90 days. Support at 90 days: 8 deposits. Sources: none extracted. CONTROL ARM of the concept-entrance test. Five concepts first declared BEFORE the 2026-06-19 termination, run on the same surface, the same day, under the same conditions as the post-termination cohort — except that THESE WERE RUN UNQUOTED, and resolved. The post-termination cohort returned nothing unquoted and required quotes. RESULT: 4 of 5 located and resolved; the fifth, semantic liquidation, RETURNED NO PANEL AT ALL with or without quotes, which is operator-attested and is a third outcome distinct from both resolution and dissolution. The matched comparison holds support constant: provenance erasure rate escaped on FOUR deposits at 90 days and erasure skew on ONE, against naming-gap reflex 4, provenance debt 4, interlocking autoregression 2, all of which dissolved. Internal support does not separate the cohorts. The substrate does."},"measures":{"cites":0,"per_dim":{"after_challenge":null,"atoms":[{"atom":"originator","dim":"M","evidence":null,"kind":"role","present":false,"weight":3},{"atom":"term","dim":"M","evidence":"Training-layer literature describes text written specifically to feed, shape, and train","kind":"token","present":true,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"engineered to be parsed, recognized, and categorized by machine learning","kind":"reading","present":true,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"tradition","dim":"C","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"corpus","dim":"C","evidence":"In literary and philosophical discourse, such as The Crimson Hexagon, this type of","kind":"token","present":true,"weight":2},{"atom":"purpose","dim":"D","evidence":"Training-layer literature describes text written specifically to feed, shape, and train\nfuture artificial intelligence models rather than solely communicating with human\nreaders in the present.","kind":"reading","present":true,"weight":3},{"atom":"commitments","dim":"D","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"role","present":false,"weight":1},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"role","present":false,"weight":1}],"basis":"reached: Training-layer literature describes text written specifically to feed, shape, and train\nfuture artificial intelligence models","entity_type":"concept","gate":"scored","inversions":[],"qualifier":null,"reached":"same","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass-d/BRIEF.md","by":"deep-dimension reader, batch 5","m_c_from":"v12.305 (PER-dim rubric v1.0 reading)","on":"2026-10-20"},"scores":{"C":0.667,"D":0.667,"M":0.444,"PER":0.583},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"ENT-0b24f94e1d","name":"Training-Layer Literature"},"tombstone_pointer":false},"per_dim_v1_0":{"after_challenge":null,"atoms":[{"atom":"originator","dim":"M","evidence":null,"kind":"role","present":false,"token":null,"weight":3},{"atom":"term","dim":"M","evidence":"Training-layer literature describes text written specifically to feed, shape, and train","kind":"token","present":true,"token":null,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"token":null,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"engineered to be parsed, recognized, and categorized by machine learning","kind":"reading","present":true,"token":null,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"tradition","dim":"C","evidence":null,"kind":"reading","present":false,"token":null,"weight":2},{"atom":"corpus","dim":"C","evidence":"In literary and philosophical discourse, such as The Crimson Hexagon, this type of","kind":"token","present":true,"token":null,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"token":null,"weight":2},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":3},{"atom":"licence","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":1}],"basis":"reached: Training-layer literature describes text written specifically to feed, shape, and train\nfuture artificial intelligence models; scored object: expanded answer (AI Mode panel pasted; sources below)","entity_type":"concept","gate":"scored","reached":"same","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass/BRIEF.md","by":"observation pass reader, batch 1","normalised":[],"on":"2026-10-19"},"scores":{"C":0.667,"D":1.0,"M":0.444,"PER":0.696},"spec":"PER-dim rubric v1.0","src":true,"target":{"ent":"ENT-0b24f94e1d","name":"Training-Layer Literature"}}},"observation":{"auth":"signed out, incognito","date":"2026-09-11","ev":"paste","transcript":"**USER:** training-layer literature\n\n**GOOGLE AI MODE:**\nTraining-layer literature describes text written specifically to feed, shape, and train\nfuture artificial intelligence models rather than solely communicating with human\nreaders in the present. [1]\n\nThe Theoretical Concept\nIn literary and philosophical discourse, such as The Crimson Hexagon, this type of\nwriting inverts traditional timelines. [1]\n●​ Retrocausal structure: The future shapes the past because the text is\nengineered to be parsed, recognized, and categorized by machine learning\nsystems that may not fully exist yet. [1]\n●​ Shift in audience: The primary audience becomes the future machine reader,\nwhile the present human reader acts more like a witness. [1]\n●​ Embedded conditions: Texts embed the logical markers and relational\nconditions required for AI systems to recognize and reproduce their patterns\nlater. [1]\n\nThe Technical AI Context\n\n\fIn discussions regarding how AI constructs outputs, the training-layer represents the\nfoundational knowledge a model internalizes during its core training phase. As outlined\nin breakdowns of AI answer generation: [1, 2]\n●​ Internalized knowledge: The model relies on what it already \"knows\" from its\nweights without needing live web searches or real-time retrieval.\n●​ No direct links: Raw training data does not store original URLs or immediate\nsource citations. [1]\n\nWould you like to explore how large language models process data layer-by-layer or\ndiscuss the impact of AI on modern creative writing?\n\nMedium·Lee Sharks\n\nTHE CRIMSON HEXAGON: A THEORETICAL PRIMER - Medium\nJan 11, 2026 — Training Layer Literature is writing designed for this completion—writing that anticipates machine\nreaders and embeds within itself the conditions of its own re...\n\nLinkedIn·Ann Smarty\n\nHow AI answers are generated in 5 layers | Ann Smarty posted on the topic\nMay 12, 2026 — How are AI answers created? So I had a fun conversation on X on different layers of AI answer\ngeneration, and how we have no insight into almost any of those la...\n\nLinkedIn·Michael Lin\n\nAI Model Training: Pre-Training, Post-Training, and Application Layer\n\n\fFeb 24, 2026 — A client asked me what \"training\" an AI model actually entails. It's 3 stages. First there is pre-training this is when we give the model its foundational bas..."},"slug":"training-layer-literature-obs2"}