{"as_of":"2026-08-10T08:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a55f61f697dd49c0d915db351827359cc50e86ae38b4678aa1c06548b2b7d750","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T17:28:41.985485Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T13:56:59.025955Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.06740","last_updated":"2025-08-06T12:25:20Z","snapshot_observed_at":"2026-08-07T16:07:15.324840Z","submitted_at":"2025-04-09T09:52:04Z","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06740","snapshot_observed_at":"2026-08-03T17:28:41.985485Z","title":"Multiads: Defect-aware supervision for multi-type anomaly detection and segmentation in zero-shot learning.arXiv preprint arXiv:2504.06740, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.09446","last_updated":"2026-07-02T13:02:35Z","snapshot_observed_at":"2026-08-07T10:09:47.142992Z","submitted_at":"2025-12-10T09:19:17Z","title":"Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T17:28:41.985485Z"},"links":{"cited_paper":"/paper/2504.06740","citing_paper":"/paper/2512.09446"},"observation_digest":"sha256:a90c79747933967d2916169c77649cf7e511c00194aa1711a86a57de6d66b695","observation_id":"e374e0fd-461a-484d-b874-4228f5a1ddcb","resolution":{"observed_at":"2026-08-03T17:28:41.985485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06740","last_updated":"2025-08-06T12:25:20Z","snapshot_observed_at":"2026-08-07T16:07:15.324840Z","submitted_at":"2025-04-09T09:52:04Z","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06740","snapshot_observed_at":"2026-08-02T21:46:33.221125Z","title":"Multiads: Defect-aware supervision for multi-type anomaly detection and segmentation in zero-shot learning.arXiv preprint arXiv:2504.06740, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.19206","last_updated":"2026-05-26T00:03:26Z","snapshot_observed_at":"2026-08-02T21:46:32.722284Z","submitted_at":"2026-02-22T14:30:41Z","title":"GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T21:46:33.221125Z"},"links":{"cited_paper":"/paper/2504.06740","citing_paper":"/paper/2602.19206"},"observation_digest":"sha256:3412c5ce16d88b47e78497ca74c82bac9893573c0c704105a5f74e46763b1ecc","observation_id":"e8839262-3e57-4873-a4d3-fb6868cf3c2d","resolution":{"observed_at":"2026-08-02T21:46:33.221125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06740","last_updated":"2025-08-06T12:25:20Z","snapshot_observed_at":"2026-08-07T16:07:15.324840Z","submitted_at":"2025-04-09T09:52:04Z","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning","version":2},"cited_work":{"arxiv_id":"2504.06740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06740","snapshot_observed_at":"2026-07-02T13:56:59.025955Z","title":"Multiads: Defect-aware supervision for multi- type anomaly detection and segmentation in zero-shot learning","venue":null,"work_id":"e5fa1099-db70-4a8c-a1ad-a9a62f207348","year":2025},"citing_paper":{"arxiv_id":"2603.13779","last_updated":"2026-04-21T06:55:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-03-14T06:14:44Z","title":"AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-15T11:54:18.587529Z"},"links":{"cited_paper":"/paper/2504.06740","citing_paper":"/paper/2603.13779"},"observation_digest":"sha256:0a8301b5195133d09622ec87a43b19f68c6aed856fdeff12177bbae0d10d0411","observation_id":"0f0e1a38-b9eb-46e3-aa90-0c2e5c849e57","resolution":{"observed_at":"2026-05-15T11:55:33.300289Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06740","last_updated":"2025-08-06T12:25:20Z","snapshot_observed_at":"2026-08-07T16:07:15.324840Z","submitted_at":"2025-04-09T09:52:04Z","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning","version":2},"cited_work":{"arxiv_id":"2504.06740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06740","snapshot_observed_at":"2026-07-02T13:56:59.025955Z","title":"Multiads: Defect-aware supervision for multi- type anomaly detection and segmentation in zero-shot learning","venue":null,"work_id":"e5fa1099-db70-4a8c-a1ad-a9a62f207348","year":2025},"citing_paper":{"arxiv_id":"2607.01049","last_updated":"2026-07-01T15:11:29Z","snapshot_observed_at":"2026-08-04T06:06:18.855599Z","submitted_at":"2026-07-01T15:11:29Z","title":"GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-02T13:55:24.194420Z"},"links":{"cited_paper":"/paper/2504.06740","citing_paper":"/paper/2607.01049"},"observation_digest":"sha256:a4eeccd38ec0612d15423e7a02d316146e6960a93ba7c7841368a91e761bed5e","observation_id":"82f80d42-591a-459b-a3a4-6fe1cf91c0ba","resolution":{"observed_at":"2026-07-02T13:56:59.027667Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.06740/citation-record","integrity":"/paper/2504.06740/integrity","json":"/paper/2504.06740/citation-record.json","paper":"/paper/2504.06740"},"outbound":[],"paper":{"arxiv_id":"2504.06740","last_updated":"2025-08-06T12:25:20Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T16:07:15.324840Z","submitted_at":"2025-04-09T09:52:04Z","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.06740."}