{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VLEBBCFBEA27P5T2QZE2FIJUFT","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":"17af7db634772ac108a9171ea1ce112126cd138bf63833ccd45e08dd1384e429","cross_cats_sorted":["cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-12-03T23:28:30Z","title_canon_sha256":"2eef789983ae09fec792776a72d479c8fc0f0e567bcea1fc70664e807d09d13d"},"schema_version":"1.0","source":{"id":"2512.04329","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.04329","created_at":"2026-05-20T00:02:59Z"},{"alias_kind":"arxiv_version","alias_value":"2512.04329v2","created_at":"2026-05-20T00:02:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.04329","created_at":"2026-05-20T00:02:59Z"},{"alias_kind":"pith_short_12","alias_value":"VLEBBCFBEA27","created_at":"2026-05-20T00:02:59Z"},{"alias_kind":"pith_short_16","alias_value":"VLEBBCFBEA27P5T2","created_at":"2026-05-20T00:02:59Z"},{"alias_kind":"pith_short_8","alias_value":"VLEBBCFB","created_at":"2026-05-20T00:02:59Z"}],"graph_snapshots":[{"event_id":"sha256:a4a9025531d82b56506c9a6a4d6d86a2e280f73e62e3a023c73bc7d26c847e6c","target":"graph","created_at":"2026-05-20T00:02:59Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2512.04329/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reusing existing neural-network components is central to research efficiency, yet discovering, extracting, and validating such modules across thousands of open-source repositories remains difficult. We introduce NN-RAG, a retrieval-augmented generation system that converts large, heterogeneous PyTorch codebases into a searchable and executable library of validated neural modules. Unlike conventional code search or clone-detection tools, NN-RAG performs scope-aware dependency resolution, import-preserving reconstruction, and validator-gated promotion -- ensuring that every retrieved block is sc","authors_text":"Dmitry Ignatov, Radu Timofte, Waleed Khalid","cross_cats":["cs.SE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-12-03T23:28:30Z","title":"A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.04329","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:25106aa5d94b811c6793f1d6e2b15f3039d9de9b94aa84359887c79a96cffb66","target":"record","created_at":"2026-05-20T00:02:59Z","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":"17af7db634772ac108a9171ea1ce112126cd138bf63833ccd45e08dd1384e429","cross_cats_sorted":["cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-12-03T23:28:30Z","title_canon_sha256":"2eef789983ae09fec792776a72d479c8fc0f0e567bcea1fc70664e807d09d13d"},"schema_version":"1.0","source":{"id":"2512.04329","kind":"arxiv","version":2}},"canonical_sha256":"aac81088a12035f7f67a8649a2a1342ceb742c5795d9b17dc6718592f55bd40c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aac81088a12035f7f67a8649a2a1342ceb742c5795d9b17dc6718592f55bd40c","first_computed_at":"2026-05-20T00:02:59.717983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:59.717983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6xYZIoS3jfyYC5wFgb7kvjXNikeNFBOSJTH5X+czSS+Q1Lfubx0QiQ7qocsG8DU5+lnX0XEVv5KpeduM+GwXDg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:59.720517Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.04329","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25106aa5d94b811c6793f1d6e2b15f3039d9de9b94aa84359887c79a96cffb66","sha256:a4a9025531d82b56506c9a6a4d6d86a2e280f73e62e3a023c73bc7d26c847e6c"],"state_sha256":"9267107f0279c1d26a4200bea5dcf9be3d08c932dd1c676725b8532dc2c1f886"}