{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XSAMKVGLWYGWSEI7SGWDRE6D7C","short_pith_number":"pith:XSAMKVGL","schema_version":"1.0","canonical_sha256":"bc80c554cbb60d69111f91ac3893c3f8972d7f808817c51531f759bb62f14007","source":{"kind":"arxiv","id":"2601.22107","version":2},"attestation_state":"computed","paper":{"title":"Prior-Informed Flow Matching for Graph Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Harvey Chen, Nicolas Zilberstein, Santiago Segarra","submitted_at":"2026-01-29T18:38:02Z","abstract_excerpt":"We introduce \\textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction. Reconstructing graphs from partial observations remains a key challenge; classical embedding methods often lack global consistency, while modern generative models struggle to incorporate structural priors. PIFM bridges this gap by integrating embedding-based priors with continuous-time flow matching. Grounded in a permutation equivariant version of the distortion-perception theory, our method first uses a prior, such as GraphSAGE or node2vec, to form an informed initial estimate of the"},"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":"2601.22107","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-29T18:38:02Z","cross_cats_sorted":[],"title_canon_sha256":"48c9adab37cac1d379e61504c24d39b9f702e9a6cffc855e16c8bac019cb7042","abstract_canon_sha256":"8e373bda0722dbbb205fa5f39a89d319be5501bb6db016d0a38d25acfff998da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:18.148159Z","signature_b64":"RqIo5U02lJiYGIYgvGeuathFI06jGOrT95EFPfW52MzuX1rbZX5UrOH1ukEGuWjd+as86WIcYICytPKVSE1CDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc80c554cbb60d69111f91ac3893c3f8972d7f808817c51531f759bb62f14007","last_reissued_at":"2026-06-19T16:12:18.147771Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:18.147771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prior-Informed Flow Matching for Graph Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Harvey Chen, Nicolas Zilberstein, Santiago Segarra","submitted_at":"2026-01-29T18:38:02Z","abstract_excerpt":"We introduce \\textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction. Reconstructing graphs from partial observations remains a key challenge; classical embedding methods often lack global consistency, while modern generative models struggle to incorporate structural priors. PIFM bridges this gap by integrating embedding-based priors with continuous-time flow matching. Grounded in a permutation equivariant version of the distortion-perception theory, our method first uses a prior, such as GraphSAGE or node2vec, to form an informed initial estimate of the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.22107","kind":"arxiv","version":2},"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/2601.22107/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":"2601.22107","created_at":"2026-06-19T16:12:18.147827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.22107v2","created_at":"2026-06-19T16:12:18.147827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.22107","created_at":"2026-06-19T16:12:18.147827+00:00"},{"alias_kind":"pith_short_12","alias_value":"XSAMKVGLWYGW","created_at":"2026-06-19T16:12:18.147827+00:00"},{"alias_kind":"pith_short_16","alias_value":"XSAMKVGLWYGWSEI7","created_at":"2026-06-19T16:12:18.147827+00:00"},{"alias_kind":"pith_short_8","alias_value":"XSAMKVGL","created_at":"2026-06-19T16:12:18.147827+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/XSAMKVGLWYGWSEI7SGWDRE6D7C","json":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C.json","graph_json":"https://pith.science/api/pith-number/XSAMKVGLWYGWSEI7SGWDRE6D7C/graph.json","events_json":"https://pith.science/api/pith-number/XSAMKVGLWYGWSEI7SGWDRE6D7C/events.json","paper":"https://pith.science/paper/XSAMKVGL"},"agent_actions":{"view_html":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C","download_json":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C.json","view_paper":"https://pith.science/paper/XSAMKVGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.22107&json=true","fetch_graph":"https://pith.science/api/pith-number/XSAMKVGLWYGWSEI7SGWDRE6D7C/graph.json","fetch_events":"https://pith.science/api/pith-number/XSAMKVGLWYGWSEI7SGWDRE6D7C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C/action/storage_attestation","attest_author":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C/action/author_attestation","sign_citation":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C/action/citation_signature","submit_replication":"https://pith.science/pith/XSAMKVGLWYGWSEI7SGWDRE6D7C/action/replication_record"}},"created_at":"2026-06-19T16:12:18.147827+00:00","updated_at":"2026-06-19T16:12:18.147827+00:00"}