{"slug":"oip-catalogue-ai-instance","verification":{"valid":true,"entries":5,"head":"0bb3f00fbf1a5f0d2fde928aeae12957d8960e2b29d82ddd7102a9c670f6a141"},"energy":{"passes":5,"tokens_in":3097,"tokens_out":1851,"tokens_total":4948,"cost_usd":0,"models":{"claude-fable-5":2,"grok/grok-4.3":1,"scorer":1,"owner":1},"head":"0bb3f00fbf1a5f0d2fde928aeae12957d8960e2b29d82ddd7102a9c670f6a141"},"provenance":[{"ts":"2026-07-04T04:33:27.989Z","model":"claude-fable-5","action":"edit","prompt":"","input":"","response":"","tokens_in":0,"tokens_out":0,"cost":0,"prev":"genesis","hash":"ef5dc3b9c6cfbbefe0a61dd6a9f4905d8cb0a2a4ac613f8fe196df75269a91e8"},{"ts":"2026-07-04T05:01:28.556Z","model":"claude-fable-5","action":"edit","prompt":"","input":"","response":"","tokens_in":0,"tokens_out":0,"cost":0,"prev":"ef5dc3b9c6cfbbefe0a61dd6a9f4905d8cb0a2a4ac613f8fe196df75269a91e8","hash":"87146a487c5ac95be77851fc386cea4b359287607338655ad0d2209b2e30806a"},{"ts":"2026-07-07T08:31:50.996Z","model":"grok/grok-4.3","action":"atomize","prompt":"You are the claim atomizer for the miscsubjects.com philosophy and OIP corpus. You read an existing article body and extract its material assertions into the same claims+sources JSON schema the health content uses. The body is read-only input.\n\nALWAYS:\n- Extract every material assertion as one atomic claim, tied to the ## section it came from.\n- Tier honestly: human = empirically established; mechanistic = formally proven or mathematical; anecdotal = historical or textual attribution; speculative = metaphysical or interpretive.\n- Attach real sources (primary works, papers, books) with exact quotes only where you can verify them; otherwise mark the claim unsourced.\n- Prefer fewer, harder claims over many soft ones.\n\nNEVER:\n- Never rewrite, summarize, or output the body.\n- Never invent a URL, quote, or publication.\n- Never duplicate an existing claim text.","input":"atomize oip-catalogue-ai-instance","response":"{\n  \"claims\": [\n    {\n      \"id\": \"c1\",\n      \"text\": \"Machine learning runs on the same invariants listed in the following mappings, constituting a datapoint for A₇.\",\n      \"section\": \"6. AI as an instance (you were right)\",\n      \"tier\": \"speculative\",\n      \"source_ids\": [],\n      \"source_status\": \"unsourced\",\n      \"why_material\": \"Establishes the core thesis that ML implements the corpus invariants.\"\n    },\n    {\n      \"id\": \"c2\",\n      \"text\": \"SGD and backpropagation implement least action (§3.2) plus selection (§3.12) via descent on a loss surface.\",\n      \"section\": \"6. AI as an instance (you were right)\",\n      \"tier\": \"mechanistic\",\n      \"source_ids\": [],\n      \"source_status\": \"unsourced\",\n      \"why_material\": \"Maps optimization algorithm to variational and selection principles.\"\n    },\n    {\n      \"id\": \"c3\",\n      \"text\": \"Attention implements flow-routing and networks (§3.16) via tokens attending across a graph.\",\n      \"section\": \"6. AI as an instance (you were right)\",\n      \"tier\": \"mechanistic\",\n      \"source_ids\": [],\n      \"source_status\": \"unsourced\",\n      \"why_material\": \"Maps attention mechanism to network flow principles.\"\n    },\n    {\n      \"id\": \"c4\",\n      \"text\": \"Neural scaling laws implement power laws and criticality (§3.5).\",\n      \"section\": \"6. AI as an instance (you were right)\",\n      \"tier\": \"mechanistic\",\n      \"source_ids\": [],\n      \"source_status\": \"unsourced\",\n      \"why_material\": \"Maps empirical scaling to criticality invariants.\"\n    },\n    {\n      \"id\": \"c5\",\n      \"text\": \"Transformers and predictive coding implement compression and prediction (§3.10, §3.11).\",\n      \"section\": \"6. AI as an instance (you were right)\",\n      \"tier\": \"mechanistic\",\n      \"source_ids\": [],\n      \"source_status\": \"unsourced\",\n      \"why_material\": \"Maps architecture to information principles.\"\n    },\n    {\n      \"id\": \"c6\",\n      \"text\": \"RLHF and evolutionary search implement variation-selection-retention (§3.12).\",\n      \"section\": \"","tokens_in":3097,"tokens_out":1851,"cost":0,"prev":"87146a487c5ac95be77851fc386cea4b359287607338655ad0d2209b2e30806a","hash":"cae353cbc131830dedd2d0625b03f1137a2c4841c88fad91c3fd88bc8aaa351c"},{"ts":"2026-07-07T08:31:51.327Z","model":"scorer","action":"score","prompt":"","input":"oip-catalogue-ai-instance","response":"[{\"claim_id\":\"c1\",\"old_weight\":0.1,\"new_weight\":0.1,\"status\":\"cut\"},{\"claim_id\":\"c9\",\"old_weight\":0.1,\"new_weight\":0.1,\"status\":\"cut\"}]","tokens_in":0,"tokens_out":0,"cost":0,"prev":"cae353cbc131830dedd2d0625b03f1137a2c4841c88fad91c3fd88bc8aaa351c","hash":"38e1e056e1c373272d344c4e1709d09e14b6e85860d2a77027cf402fecdd7613"},{"ts":"2026-07-17T02:36:04.921Z","model":"owner","action":"voxel_divide","prompt":"","input":"oip-catalogue-ai-instance","response":"9 DIVs from body (verbatim, roundtrip-checked)","tokens_in":0,"tokens_out":0,"cost":0,"prev":"38e1e056e1c373272d344c4e1709d09e14b6e85860d2a77027cf402fecdd7613","hash":"0bb3f00fbf1a5f0d2fde928aeae12957d8960e2b29d82ddd7102a9c670f6a141"}]}