{"as_of":"2026-08-16T14:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2653f525b8b108b0ff7bef28241e8042c8128cfa634ff92383fdd49fb62ca4a0","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:42:20.609636Z","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":"2026-07-02T02:16:26.851560Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07397","snapshot_observed_at":"2026-08-06T23:20:57.324745Z","title":"Improv- ing zero-shot generalization for clip with synthesized prompts,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18504","last_updated":"2025-06-30T05:24:22Z","snapshot_observed_at":"2026-08-16T09:44:26.567520Z","submitted_at":"2025-06-23T10:56:37Z","title":"Generalizing vision-language models to novel domains: A comprehensive survey","version":2},"reference_index":200,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:57.324745Z"},"links":{"cited_paper":"/paper/2307.07397","citing_paper":"/paper/2506.18504"},"observation_digest":"sha256:bf056a42caff6f3b827c7a1c92b217fb6f7c665937be3d556d1c60139ba0c7b4","observation_id":"df2c3df4-e049-467b-8200-c7949357cb7e","resolution":{"observed_at":"2026-08-06T23:20:57.324745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07397","snapshot_observed_at":"2026-08-15T17:42:20.609636Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.08644","last_updated":"2025-08-12T05:16:00Z","snapshot_observed_at":"2026-08-15T17:31:43.996364Z","submitted_at":"2025-08-12T05:16:00Z","title":"AME: Aligned Manifold Entropy for Robust Vision-Language Distillation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:20.609636Z"},"links":{"cited_paper":"/paper/2307.07397","citing_paper":"/paper/2508.08644"},"observation_digest":"sha256:66cf97c334dae3abef8e063e77c6b0ce91501007a1430ec6c170c490d04dfb2b","observation_id":"780bd6d6-273e-40f1-973f-c71f8c620dff","resolution":{"observed_at":"2026-08-15T17:42:20.609636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts","version":1},"cited_work":{"arxiv_id":"2307.07397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.07397","snapshot_observed_at":"2026-07-02T02:16:26.851560Z","title":"arXiv preprint arXiv:2307.07397 (2023)","venue":null,"work_id":"5e0a7784-d987-448a-a09e-617409b7a9f6","year":2023},"citing_paper":{"arxiv_id":"2603.25383","last_updated":"2026-04-22T09:05:30Z","snapshot_observed_at":"2026-08-14T01:05:01.648606Z","submitted_at":"2026-03-26T12:34:18Z","title":"CLIP-RD: Relative Distillation for Efficient CLIP Knowledge Distillation","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-15T00:20:36.975106Z"},"links":{"cited_paper":"/paper/2307.07397","citing_paper":"/paper/2603.25383"},"observation_digest":"sha256:7552232f9ddced0a0c1c746ae37395548fb38f4ca9bfc5898bc9e07e1d3bc625","observation_id":"586014c2-5229-40c5-b204-2009c91114b8","resolution":{"observed_at":"2026-05-15T00:23:22.869936Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts","version":1},"cited_work":{"arxiv_id":"2307.07397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.07397","snapshot_observed_at":"2026-07-02T02:16:26.851560Z","title":"arXiv preprint arXiv:2307.07397 (2023)","venue":null,"work_id":"5e0a7784-d987-448a-a09e-617409b7a9f6","year":2023},"citing_paper":{"arxiv_id":"2605.25922","last_updated":"2026-05-25T15:00:07Z","snapshot_observed_at":"2026-08-02T01:29:33.033813Z","submitted_at":"2026-05-25T15:00:07Z","title":"Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T22:40:26.803098Z"},"links":{"cited_paper":"/paper/2307.07397","citing_paper":"/paper/2605.25922"},"observation_digest":"sha256:0c8a4926429616a6780aa213b5e7150f1cf4cc675b80e87f32a5b513c9342b2c","observation_id":"3f6ce232-ec4e-4605-942a-f8c38968347d","resolution":{"observed_at":"2026-06-29T22:44:01.421468Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts","version":1},"cited_work":{"arxiv_id":"2307.07397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.07397","snapshot_observed_at":"2026-07-02T02:16:26.851560Z","title":"arXiv preprint arXiv:2307.07397 (2023)","venue":null,"work_id":"5e0a7784-d987-448a-a09e-617409b7a9f6","year":2023},"citing_paper":{"arxiv_id":"2606.03730","last_updated":"2026-08-11T09:26:07Z","snapshot_observed_at":"2026-08-15T00:55:31.878235Z","submitted_at":"2026-06-02T14:49:04Z","title":"Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-28T11:04:30.654255Z"},"links":{"cited_paper":"/paper/2307.07397","citing_paper":"/paper/2606.03730"},"observation_digest":"sha256:44201b59cc83fcf6c6119c3c55cb878218e5328f1c9b4c4a533be0653e3efee7","observation_id":"a4cbee48-621b-40a3-aee9-2b09d9c6051e","resolution":{"observed_at":"2026-07-02T02:16:26.853066Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2307.07397/citation-record","integrity":"/paper/2307.07397/integrity","json":"/paper/2307.07397/citation-record.json","paper":"/paper/2307.07397"},"outbound":[],"paper":{"arxiv_id":"2307.07397","last_updated":"2023-07-14T15:15:45Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T14:14:54.287135Z","submitted_at":"2023-07-14T15:15:45Z","title":"Improving Zero-Shot Generalization for CLIP with Synthesized Prompts"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.07397."}