{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RXPVHQF347KKON5Q33ALKZSJFV","short_pith_number":"pith:RXPVHQF3","schema_version":"1.0","canonical_sha256":"8ddf53c0bbe7d4a737b0dec0b566492d61f4608cde92dda8df616308282a0bbf","source":{"kind":"arxiv","id":"2607.00013","version":1},"attestation_state":"computed","paper":{"title":"GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Aman Kumar, Asit Desai, Prashant Devadiga","submitted_at":"2026-05-08T12:53:53Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) systems are widely used in institutional question answering settings where responses must be grounded in authoritative documentation (Gao et al., 2023). In entity-dense domains where relevant information is distributed across heterogeneous documents, vector-only retrieval often produces fragmented evidence and increases dependence on inference-time reasoning (Zhao et al., 2024). This paper introduces GRACE-RAG, a retrieval-governed, graph-augmented RAG architecture that externalizes structural reasoning from the generative stage to a structured retrieval la"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.00013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-08T12:53:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"53181c273aa785161aa43f1a99b374bc233ad64bb6f592b7c13ed075abe0a3a6","abstract_canon_sha256":"3243320f9423444903ff8a136e4230aee1d8dfd6cdd40e9355d97c9178e9c3e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T00:18:04.648903Z","signature_b64":"m2Uie2TAqWfSW5yuET3sjI3/+df4BpPfclSbsRH2BavbnhiNruXzXPqsqtG+LAhr93h/8FD324N+R+d8e7kABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ddf53c0bbe7d4a737b0dec0b566492d61f4608cde92dda8df616308282a0bbf","last_reissued_at":"2026-07-02T00:18:04.648354Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T00:18:04.648354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Aman Kumar, Asit Desai, Prashant Devadiga","submitted_at":"2026-05-08T12:53:53Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) systems are widely used in institutional question answering settings where responses must be grounded in authoritative documentation (Gao et al., 2023). In entity-dense domains where relevant information is distributed across heterogeneous documents, vector-only retrieval often produces fragmented evidence and increases dependence on inference-time reasoning (Zhao et al., 2024). This paper introduces GRACE-RAG, a retrieval-governed, graph-augmented RAG architecture that externalizes structural reasoning from the generative stage to a structured retrieval la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00013","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.00013/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.00013","created_at":"2026-07-02T00:18:04.648433+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00013v1","created_at":"2026-07-02T00:18:04.648433+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00013","created_at":"2026-07-02T00:18:04.648433+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXPVHQF347KK","created_at":"2026-07-02T00:18:04.648433+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXPVHQF347KKON5Q","created_at":"2026-07-02T00:18:04.648433+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXPVHQF3","created_at":"2026-07-02T00:18:04.648433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV","json":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV.json","graph_json":"https://pith.science/api/pith-number/RXPVHQF347KKON5Q33ALKZSJFV/graph.json","events_json":"https://pith.science/api/pith-number/RXPVHQF347KKON5Q33ALKZSJFV/events.json","paper":"https://pith.science/paper/RXPVHQF3"},"agent_actions":{"view_html":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV","download_json":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV.json","view_paper":"https://pith.science/paper/RXPVHQF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00013&json=true","fetch_graph":"https://pith.science/api/pith-number/RXPVHQF347KKON5Q33ALKZSJFV/graph.json","fetch_events":"https://pith.science/api/pith-number/RXPVHQF347KKON5Q33ALKZSJFV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV/action/storage_attestation","attest_author":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV/action/author_attestation","sign_citation":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV/action/citation_signature","submit_replication":"https://pith.science/pith/RXPVHQF347KKON5Q33ALKZSJFV/action/replication_record"}},"created_at":"2026-07-02T00:18:04.648433+00:00","updated_at":"2026-07-02T00:18:04.648433+00:00"}