{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KX22QOPDYIJANPOUB4MCBFSQNH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5a3ef9266d69cd2cef9046f9e9820edb0e3578e4f61a6d2c56fbc8e01a1b5c8c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-01T13:31:12Z","title_canon_sha256":"9cce5771b904db0a97992173e7393cfc956537ed62f258162045cbb829a4f483"},"schema_version":"1.0","source":{"id":"2604.25928","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.25928","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"arxiv_version","alias_value":"2604.25928v2","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.25928","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_12","alias_value":"KX22QOPDYIJA","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_16","alias_value":"KX22QOPDYIJANPOU","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_8","alias_value":"KX22QOPD","created_at":"2026-06-03T01:05:14Z"}],"graph_snapshots":[{"event_id":"sha256:5e67a65fff8297a40baaffe88c92f3312afe7f94fa7dc889a6d478aeb5411c3e","target":"graph","created_at":"2026-06-03T01:05:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experiments on two representative models, Qwen3-8B and Llama3.1-8B, show that CogRAG+ consistently outperforms general-purpose models and standard RAG methods on the Registered Dietitian qualification exam. In single-question mode, it raises overall accuracy to 85.8% for Qwen3-8B and 60.3% for Llama3.1-8B, with clear gains over vanilla baselines."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that a judge-driven dual-path retrieval strategy can reliably identify and supply missing foundational knowledge without domain-specific tuning or additional training data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"CogRAG+ raises LLM accuracy on a dietitian exam to 85.8% by using dual-path retrieval and structured reasoning templates."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"CogRAG+ separates retrieval from reasoning in LLMs using dual paths and structured templates to fix knowledge gaps on professional exams."}],"snapshot_sha256":"31c80d4001398e2cebb6dd379eeb4b14d9922f9e5d72a8c5e52c55572ae9d3e6"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"5ae7427c5dec90fa7a133fe61c0eb10fc6d6deb82aacadf975738200f7c5b8b6"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.25928/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) frameworks typically process all queries through a one-size-fits-all pipeline, ignoring the heterogeneous cognitive demands of different tasks. This cognitive-blind approach causes two failure modes: cascading errors when low-level factual gaps trigger hallucinated reasoning, and reasoning-answer inconsistency in higher-order analytical tasks. We introduce CogRAG, a training-free, domain-agnostic framework that tackles these heterogeneous cognitive demands via stratified retrieval and reasoning. Inspired by Bloom's Taxonomy, CogRAG uses the predicted cognit","authors_text":"Kui Su, Xudong Wang, Zhaoyan Ming, Zilong Wang","cross_cats":[],"headline":"CogRAG+ separates retrieval from reasoning in LLMs using dual paths and structured templates to fix knowledge gaps on professional exams.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-01T13:31:12Z","title":"CogRAG: Tackling Heterogeneous Cognitive Demands in RAG via Stratified Retrieval and Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.25928","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-13T22:25:57.600245Z","id":"cda35c8d-83bd-4227-b831-0118372c87fa","model_set":{"reader":"grok-4.3"},"one_line_summary":"CogRAG+ raises LLM accuracy on a dietitian exam to 85.8% by using dual-path retrieval and structured reasoning templates.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"CogRAG+ separates retrieval from reasoning in LLMs using dual paths and structured templates to fix knowledge gaps on professional exams.","strongest_claim":"Experiments on two representative models, Qwen3-8B and Llama3.1-8B, show that CogRAG+ consistently outperforms general-purpose models and standard RAG methods on the Registered Dietitian qualification exam. In single-question mode, it raises overall accuracy to 85.8% for Qwen3-8B and 60.3% for Llama3.1-8B, with clear gains over vanilla baselines.","weakest_assumption":"The assumption that a judge-driven dual-path retrieval strategy can reliably identify and supply missing foundational knowledge without domain-specific tuning or additional training data."}},"verdict_id":"cda35c8d-83bd-4227-b831-0118372c87fa"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0de7d6357ea4474d5074080f4019a1630788b3e876adca3842cc67795b9d5d78","target":"record","created_at":"2026-06-03T01:05:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5a3ef9266d69cd2cef9046f9e9820edb0e3578e4f61a6d2c56fbc8e01a1b5c8c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-01T13:31:12Z","title_canon_sha256":"9cce5771b904db0a97992173e7393cfc956537ed62f258162045cbb829a4f483"},"schema_version":"1.0","source":{"id":"2604.25928","kind":"arxiv","version":2}},"canonical_sha256":"55f5a839e3c21206bdd40f1820965069ec405284b4cc23cb912c5d44b46d3af3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55f5a839e3c21206bdd40f1820965069ec405284b4cc23cb912c5d44b46d3af3","first_computed_at":"2026-06-03T01:05:14.150446Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:14.150446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"by8dfuzV4hJ/uJTBYzDTLhey7Vnz+9AvFCx6oasPuQbNrPtBtP4atYfPS09Oc9jXdifR938P70cjR8RQhh30DA==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:14.150929Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.25928","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0de7d6357ea4474d5074080f4019a1630788b3e876adca3842cc67795b9d5d78","sha256:5e67a65fff8297a40baaffe88c92f3312afe7f94fa7dc889a6d478aeb5411c3e"],"state_sha256":"69d1113a214b60a6c73d988c5e00b8e52a19161fe76ab86455582056b03afe61"}