{"paper":{"title":"Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"ChatIPC extracts ordered token-transition rules from text and builds responses by similarity selection on a token graph.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Caleb Princewill Nwokocha","submitted_at":"2022-07-31T01:52:40Z","abstract_excerpt":"Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection. The system may be viewed as a rule extractor operating over a token graph rather than a conventional classifier. I formalize the knowledge base, "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"ChatIPC is a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection, operating over a token graph rather than a conventional classifier.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That similarity-guided selection on the token-transition graph, combined with definition expansion, will reliably produce coherent and useful responses (assumed without stated validation or comparison to baselines).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"ChatIPC extracts ordered token-transition rules from text via incremental learning and definition expansion, then constructs responses through similarity-guided selection on a token graph.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"ChatIPC extracts ordered token-transition rules from text and builds responses by similarity selection on a token graph.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8255a6ee4290c03d3bcde0b5ae1e7e04dfea70146bcd39dcd4624b229efe9a82"},"source":{"id":"2208.00335","kind":"arxiv","version":5},"verdict":{"id":"694b419c-de0c-43ec-9db6-efded1f77a6a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T11:17:48.771747Z","strongest_claim":"ChatIPC is a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection, operating over a token graph rather than a conventional classifier.","one_line_summary":"ChatIPC extracts ordered token-transition rules from text via incremental learning and definition expansion, then constructs responses through similarity-guided selection on a token graph.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That similarity-guided selection on the token-transition graph, combined with definition expansion, will reliably produce coherent and useful responses (assumed without stated validation or comparison to baselines).","pith_extraction_headline":"ChatIPC extracts ordered token-transition rules from text and builds responses by similarity selection on a token graph."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.00335/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}