{"as_of":"2026-08-24T01:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3b32fe64e3821a8f3b9cb4bf7ecfdcd04b9968946982754d2ddc926410cc06dc","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:14:24.638092Z","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":"2026-05-10T23:57:09.140459Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":"2211.15462","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":"8aa956a6-6785-477e-ab57-625c6aa81068","year":2022},"citing_paper":{"arxiv_id":"2308.06721","last_updated":"2023-08-13T08:34:51Z","snapshot_observed_at":"2026-07-06T16:05:39.158819Z","submitted_at":"2023-08-13T08:34:51Z","title":"IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T23:57:08.818348Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2308.06721"},"observation_digest":"sha256:ddf16ab142a017602cf85521f0f886b5bef06af927573471479c7dd72c548f83","observation_id":"c5ff5d13-ecb0-4c17-8208-5ea4ab36e628","resolution":{"observed_at":"2026-05-10T23:57:09.143297Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-12T17:14:24.638092Z","title":"arXiv preprint arXiv:2211.15462 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.12811","last_updated":"2024-11-19T19:04:31Z","snapshot_observed_at":"2026-08-17T17:12:23.480023Z","submitted_at":"2024-11-19T19:04:31Z","title":"Stylecodes: Encoding Stylistic Information For Image Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T17:14:24.638092Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2411.12811"},"observation_digest":"sha256:cca14993ffa7194a47ffed34b0cd3d2b6b4e632db8bd724da6d4d1e750c13596","observation_id":"134a9c24-c024-40f4-8b7b-bf26fec94ed2","resolution":{"observed_at":"2026-08-12T17:14:24.638092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-12T13:32:27.724590Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16783","last_updated":"2024-11-25T08:20:14Z","snapshot_observed_at":"2026-08-18T09:58:58.760733Z","submitted_at":"2024-11-25T08:20:14Z","title":"CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:32:27.724590Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2411.16783"},"observation_digest":"sha256:b9af4ca278e2437d4ec276fd4e2e454dd8f0e88000c192a80168d6219802f4b7","observation_id":"706d41fe-8f25-4a42-9736-5b0e03171bec","resolution":{"observed_at":"2026-08-12T13:32:27.724590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-12T12:22:17.933808Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17310","last_updated":"2024-11-26T10:54:33Z","snapshot_observed_at":"2026-08-18T22:51:15.485546Z","submitted_at":"2024-11-26T10:54:33Z","title":"Reward Incremental Learning in Text-to-Image Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:17.933808Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2411.17310"},"observation_digest":"sha256:de5cb9d079db995d01bf3798919306a6c55f6048a6726403f0a973d46af82709","observation_id":"e24bd065-4a3a-40b3-a0b7-f3fb9599204a","resolution":{"observed_at":"2026-08-12T12:22:17.933808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-12T11:23:38.935021Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.18301","last_updated":"2024-11-27T12:47:06Z","snapshot_observed_at":"2026-08-21T06:33:28.379475Z","submitted_at":"2024-11-27T12:47:06Z","title":"Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T11:23:38.935021Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2411.18301"},"observation_digest":"sha256:8beef753af6e426d293207b1aec333fb1aa285912916d27f281460b480f7271f","observation_id":"386e4eb0-ce3a-4e38-9ec9-68c1cb288040","resolution":{"observed_at":"2026-08-12T11:23:38.935021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-11T00:13:43.647701Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19637","last_updated":"2025-06-21T13:41:27Z","snapshot_observed_at":"2026-08-19T01:32:48.784739Z","submitted_at":"2024-12-27T13:31:55Z","title":"ReNeg: Learning Negative Embedding with Reward Guidance","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T00:13:43.647701Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2412.19637"},"observation_digest":"sha256:47ebcea0aba59ac111da5f711a4ebdf6d413f184c8085542b6c79183f41d108a","observation_id":"d957a185-b34e-4d60-a271-a07aa32d33c2","resolution":{"observed_at":"2026-08-11T00:13:43.647701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-07T11:26:29.533343Z","title":"Investigating prompt engineering in diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02528","last_updated":"2025-06-03T07:06:35Z","snapshot_observed_at":"2026-08-18T06:47:17.866917Z","submitted_at":"2025-06-03T07:06:35Z","title":"RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:26:29.533343Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2506.02528"},"observation_digest":"sha256:19acc73791627302cd22d88342e3bb999dec1a4de1f28733600a43f32ec6d764","observation_id":"f9b2ed42-302f-42aa-8570-66ae28118dd6","resolution":{"observed_at":"2026-08-07T11:26:29.533343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.15462","snapshot_observed_at":"2026-08-06T16:31:06.705475Z","title":"Investigating Prompt Engineering in Diffusion Models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13221","last_updated":"2025-07-17T15:35:27Z","snapshot_observed_at":"2026-08-21T01:56:29.361284Z","submitted_at":"2025-07-17T15:35:27Z","title":"Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:31:06.705475Z"},"links":{"cited_paper":"/paper/2211.15462","citing_paper":"/paper/2507.13221"},"observation_digest":"sha256:20d803b6d1918ec90369dcaf66588d9772c1776ae7be3e423a7544b438987bdd","observation_id":"f25f0715-04c9-402d-a76c-64563532367d","resolution":{"observed_at":"2026-08-06T16:31:06.705475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2211.15462/citation-record","integrity":"/paper/2211.15462/integrity","json":"/paper/2211.15462/citation-record.json","paper":"/paper/2211.15462"},"outbound":[],"paper":{"arxiv_id":"2211.15462","last_updated":"2022-11-21T07:07:19Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T16:14:39.476525Z","submitted_at":"2022-11-21T07:07:19Z","title":"Investigating Prompt Engineering in Diffusion Models"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2211.15462."}