{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3PRCCNOBID66CWXVRN2KI3GAJW","short_pith_number":"pith:3PRCCNOB","schema_version":"1.0","canonical_sha256":"dbe22135c140fde15af58b74a46cc04db0f45bf411d750cd48dccb94a9eb0450","source":{"kind":"arxiv","id":"2605.28428","version":1},"attestation_state":"computed","paper":{"title":"Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jungwook Seo, Minjeong Kim, Seungho Shin, Sungyong Baik, Younkwan Lee","submitted_at":"2026-05-27T12:58:56Z","abstract_excerpt":"Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by the cost of Non-Conformity of a query patch to align with a fixed normal manifold. For each query patc"},"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":"2605.28428","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T12:58:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"49b320c6ad819eb1187e0e107f8035b32db0ec3aee65243e8bfbef3b8718ea6c","abstract_canon_sha256":"33bc7a0d6a74d13198ce8a69cc5270e054fe4556c2822a2514c5efe9bc0914a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:05:17.899161Z","signature_b64":"EIvuytR3m1psnrbkB5wn1+IH95qoo0DgLLxb382dfEkvI4h1+X6klMsRYxxZyRsXy2fsOCWEylNcQ4gRPbGaAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbe22135c140fde15af58b74a46cc04db0f45bf411d750cd48dccb94a9eb0450","last_reissued_at":"2026-05-28T01:05:17.898700Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:05:17.898700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jungwook Seo, Minjeong Kim, Seungho Shin, Sungyong Baik, Younkwan Lee","submitted_at":"2026-05-27T12:58:56Z","abstract_excerpt":"Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by the cost of Non-Conformity of a query patch to align with a fixed normal manifold. For each query patc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28428","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/2605.28428/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":"2605.28428","created_at":"2026-05-28T01:05:17.898771+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28428v1","created_at":"2026-05-28T01:05:17.898771+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28428","created_at":"2026-05-28T01:05:17.898771+00:00"},{"alias_kind":"pith_short_12","alias_value":"3PRCCNOBID66","created_at":"2026-05-28T01:05:17.898771+00:00"},{"alias_kind":"pith_short_16","alias_value":"3PRCCNOBID66CWXV","created_at":"2026-05-28T01:05:17.898771+00:00"},{"alias_kind":"pith_short_8","alias_value":"3PRCCNOB","created_at":"2026-05-28T01:05:17.898771+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/3PRCCNOBID66CWXVRN2KI3GAJW","json":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW.json","graph_json":"https://pith.science/api/pith-number/3PRCCNOBID66CWXVRN2KI3GAJW/graph.json","events_json":"https://pith.science/api/pith-number/3PRCCNOBID66CWXVRN2KI3GAJW/events.json","paper":"https://pith.science/paper/3PRCCNOB"},"agent_actions":{"view_html":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW","download_json":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW.json","view_paper":"https://pith.science/paper/3PRCCNOB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28428&json=true","fetch_graph":"https://pith.science/api/pith-number/3PRCCNOBID66CWXVRN2KI3GAJW/graph.json","fetch_events":"https://pith.science/api/pith-number/3PRCCNOBID66CWXVRN2KI3GAJW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW/action/storage_attestation","attest_author":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW/action/author_attestation","sign_citation":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW/action/citation_signature","submit_replication":"https://pith.science/pith/3PRCCNOBID66CWXVRN2KI3GAJW/action/replication_record"}},"created_at":"2026-05-28T01:05:17.898771+00:00","updated_at":"2026-05-28T01:05:17.898771+00:00"}