{"as_of":"2026-08-18T17:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:35548c9297790dcb04a731f773d6042c4c37d5ce2ac1d97be15021667d01c23c","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:18:45.955865Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.11205/citation-record","integrity":"/paper/2608.11205/integrity","json":"/paper/2608.11205/citation-record.json","paper":"/paper/2608.11205"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-16T02:30:42.660030Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-15T14:18:45.377365Z","title":"Flow matching for generative modeling.arXiv preprint arXiv:2210.02747,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.377365Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:892417338b5519b08973c14ccc7c2e2c2fdf9ddcefcc664067a26570c53cc86d","observation_id":"dd5585ea-579e-4cbf-ae1a-260e31dc80d5","resolution":{"observed_at":"2026-08-15T14:18:45.377365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-15T14:18:45.545419Z","title":"Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.545419Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:fafbb19a8c73b19e6e406d3e6ce7c89cbb6063b140e834d2695d9e26764a08bc","observation_id":"173d08d4-88f4-4b29-95a5-4e4a3a582478","resolution":{"observed_at":"2026-08-15T14:18:45.545419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:18:46.391734Z","title":"Fast high-resolution image synthesis with latent adversarial diffusion distillation","venue":null,"work_id":"0eb5c2d2-8b69-430a-aa8a-a35d54ec5266","year":2024},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.671493Z"},"links":{"citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:a4f0b048f026c8360986f56cfecfbd7ab732e3dc962a4afd6957c31b6ae79487","observation_id":"5867df1e-1ad3-4a8d-ae4a-779552d85dd5","resolution":{"observed_at":"2026-08-15T14:18:46.463790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:18:45.950813Z","title":"Improved distribution matching distillation for fast image synthesis.Ad- vances in neural information processing systems, 37:47455–47487, 2024a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.950813Z"},"links":{"citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:2cd211ef44bde044523f52927a2e5cb1a7f24179258bc57ac9e987231a116954","observation_id":"f559193b-c555-43ea-a599-759d6d80b4a7","resolution":{"observed_at":"2026-08-15T14:18:45.950813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.11690","last_updated":"2025-10-13T17:51:39Z","snapshot_observed_at":"2026-08-13T23:51:28.874833Z","submitted_at":"2025-10-13T17:51:39Z","title":"Diffusion Transformers with Representation Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.11690","snapshot_observed_at":"2026-08-15T14:18:45.955865Z","title":"Diffusion transformers with repre- sentation autoencoders.arXiv preprint arXiv:2510.11690,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.955865Z"},"links":{"cited_paper":"/paper/2510.11690","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:a9ce3bf3d45bf3acaa10d35c9afde5ebb7deb2475cb999962c767cd80b24b32b","observation_id":"b4179dc1-5a9d-496a-8875-dfe8da79c88c","resolution":{"observed_at":"2026-08-15T14:18:45.955865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-15T14:18:45.788299Z","title":"Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.788299Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:13ac977d0c81dda138b2ad11dded960a63c377d6d73afd80e8e7f813d23b716f","observation_id":"379aea10-a9e0-46cd-8486-74d7a35f1e3f","resolution":{"observed_at":"2026-08-15T14:18:45.788299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.06797","last_updated":"2026-05-07T18:00:58Z","snapshot_observed_at":"2026-08-15T02:21:45.662068Z","submitted_at":"2026-05-07T18:00:58Z","title":"MIND: Monge Inception Distance for Generative Models Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.06797","snapshot_observed_at":"2026-08-15T14:18:45.030759Z","title":"Mind: Monge inception distance for generative models evaluation.arXiv preprint arXiv:2605.06797,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.030759Z"},"links":{"cited_paper":"/paper/2605.06797","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:9fb656f3f6e0a36e130b6014744610d075f3e4be14615604b266a67d4327f5c5","observation_id":"b3af3431-01ab-4e87-aefc-ac5b146ab298","resolution":{"observed_at":"2026-08-15T14:18:45.030759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.11096","last_updated":"2019-02-25T21:32:06Z","snapshot_observed_at":"2026-07-06T07:04:57.275371Z","submitted_at":"2018-09-28T15:38:49Z","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.11096","snapshot_observed_at":"2026-08-15T14:18:45.164542Z","title":"Large scale gan training for high fidelity natural image synthesis.arXiv preprint arXiv:1809.11096,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.164542Z"},"links":{"cited_paper":"/paper/1809.11096","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:7cb38722d9414f22105edd5947b97a316c8ea1e8b7619d495cb7a21d717daa05","observation_id":"aa557ce6-b92c-4be7-bab9-da6c15bbe0d8","resolution":{"observed_at":"2026-08-15T14:18:45.164542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01863","last_updated":"2024-04-02T11:40:38Z","snapshot_observed_at":"2026-08-16T14:04:20.571690Z","submitted_at":"2024-04-02T11:40:38Z","title":"Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01863","snapshot_observed_at":"2026-08-15T14:18:45.311287Z","title":"Confidence-aware reward optimization for fine-tuning text-to-image models.arXiv preprint arXiv:2404.01863,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.311287Z"},"links":{"cited_paper":"/paper/2404.01863","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:7800b72c03e5236fe43f596157a0a66528892016f647a038aad465780b8fdc96","observation_id":"2e0fba82-322f-43f8-9b0e-bf1617f720a9","resolution":{"observed_at":"2026-08-15T14:18:45.311287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.11774","last_updated":"2020-03-30T20:35:11Z","snapshot_observed_at":"2026-08-16T12:37:15.893258Z","submitted_at":"2020-03-26T07:37:18Z","title":"Image Generation Via Minimizing Fr\\'echet Distance in Discriminator Feature Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.11774","snapshot_observed_at":"2026-08-15T14:18:45.170306Z","title":"Image generation via minimizing fr\\’echet distance in discriminator feature space","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.170306Z"},"links":{"cited_paper":"/paper/2003.11774","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:4b0cfa0b988f7ef263acfd5ecdfd6ea6bfbb74d8a5b5e1837bf96461d84676d8","observation_id":"09e72b48-cb66-4e2c-b774-e92793343701","resolution":{"observed_at":"2026-08-15T14:18:45.170306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-15T14:18:45.384346Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003, 2022a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.384346Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:cd16750dcda60849b84b06200e07fd47c648e89c3b1f4a7eea11004de873c27d","observation_id":"6bda5aed-63d4-446c-9fc4-70c8c8fdf617","resolution":{"observed_at":"2026-08-15T14:18:45.384346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.03544","last_updated":"2022-02-14T09:05:38Z","snapshot_observed_at":"2026-08-16T03:45:29.671274Z","submitted_at":"2022-01-10T18:58:52Z","title":"The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.03544","snapshot_observed_at":"2026-08-15T14:18:45.611022Z","title":"The effects of reward misspecification: Mapping and mitigating misaligned models.arXiv preprint arXiv:2201.03544,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.611022Z"},"links":{"cited_paper":"/paper/2201.03544","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:c851da9423742b8320d5d89e08a5d8507ae06398d78852daf9ff07c620c9b333","observation_id":"f8a640ee-02bd-4701-b42a-f659c474d4d0","resolution":{"observed_at":"2026-08-15T14:18:45.611022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.28190","last_updated":"2026-04-30T17:59:51Z","snapshot_observed_at":"2026-08-16T06:56:23.848229Z","submitted_at":"2026-04-30T17:59:51Z","title":"Representation Fr\\'echet Loss for Visual Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.28190","snapshot_observed_at":"2026-08-15T14:18:45.799220Z","title":"Representation fr\\’echet loss for visual generation.arXiv preprint arXiv:2604.28190,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.799220Z"},"links":{"cited_paper":"/paper/2604.28190","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:9a2782223b6743954463605363678b15535dfbce55e8779fc168b0c54f2b065e","observation_id":"10361188-a01e-4ea5-a154-6d253d746eea","resolution":{"observed_at":"2026-08-15T14:18:45.799220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22158","last_updated":"2026-05-09T16:47:56Z","snapshot_observed_at":"2026-08-18T07:47:46.863162Z","submitted_at":"2026-01-29T18:59:56Z","title":"One-step Latent-free Image Generation with Pixel Mean Flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.22158","snapshot_observed_at":"2026-08-15T14:18:45.493576Z","title":"One-step latent-free image generation with pixel mean flows","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.493576Z"},"links":{"cited_paper":"/paper/2601.22158","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:4d748112cfc9ae7a1355daeb776fae2759076b651495461ba8af06f42ccb5134","observation_id":"947af2ef-9b5d-4e76-8dae-1698323c3e38","resolution":{"observed_at":"2026-08-15T14:18:45.493576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01401","last_updated":"2021-01-14T05:36:59Z","snapshot_observed_at":"2026-08-13T06:40:48.610500Z","submitted_at":"2018-01-04T15:25:26Z","title":"Demystifying MMD GANs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01401","snapshot_observed_at":"2026-08-15T14:18:45.092619Z","title":"Demystifying mmd gans.arXiv preprint arXiv:1801.01401,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-15T14:18:45.092619Z"},"links":{"cited_paper":"/paper/1801.01401","citing_paper":"/paper/2608.11205"},"observation_digest":"sha256:4fc460a9818fe7d9d938b6a77b9f8db0db008d2de3a03dd1ef1a9c882c112a89","observation_id":"9b4602da-775a-4714-aac2-a30638f5a471","resolution":{"observed_at":"2026-08-15T14:18:45.092619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.11205","last_updated":"2026-08-11T17:59:56Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T06:44:17.953998Z","submitted_at":"2026-08-11T17:59:56Z","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":15},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.11205."}