{"as_of":"2026-08-06T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:352f4d289ff51a7b0b6ab55ed466749dfde7cb58298b2261677336c823db0185","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T00:27:53.992183Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2607.26237/citation-record","integrity":"/paper/2607.26237/integrity","json":"/paper/2607.26237/citation-record.json","paper":"/paper/2607.26237"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.498626Z","title":"Wasserstein generative adversarial networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.498626Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:87e547027d8527d49153629ec73b477e4bc4f3efc575a92e2829fbd0a201590b","observation_id":"acc11504-9323-4f4f-9db5-3971050d7cf0","resolution":{"observed_at":"2026-08-01T00:27:50.498626Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.569167Z","title":"Pu21: A novel perceptually uniform encoding for adapting existing quality metrics for hdr","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.569167Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:7aac703be19170caa3d248dd05308f74a7594d4e18d9227de0b843753b796ddd","observation_id":"eef4273a-71aa-4009-8ffe-cde0dcf57454","resolution":{"observed_at":"2026-08-01T00:27:50.569167Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.630610Z","title":"Universal guidance for diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.630610Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:d12ce8dcd7c11dfb0582a3106b6ecc85ce9382288751bd4692c07739d2503683","observation_id":"b5790adc-6a2d-4271-8f14-3f46b7605dad","resolution":{"observed_at":"2026-08-01T00:27:50.630610Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.748433Z","title":"Bracket diffusion: Hdr image generation by consistent ldr denoising","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.748433Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:012b3cded14acf694e66a29e8628919c85567658d0b508554667fa1bb1aa91b9","observation_id":"f6015d18-d6d1-41a7-a5e1-de9e8bfe1dbb","resolution":{"observed_at":"2026-08-01T00:27:50.748433Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.794207Z","title":"FLUX.1: Text-to-image generation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.794207Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:bd3c2600544bbb5245cf05b97db7804d3192cd298af8dcca8f560c23c2f29af8","observation_id":"87643ba4-6a8f-46a8-afe9-ac7359979aae","resolution":{"observed_at":"2026-08-01T00:27:50.794207Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.852427Z","title":"Align your latents: High-resolution video synthesis with latent diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.852427Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:5eac1b63903e35782c701c23a05744452fffca411c8c10dedaa93de34238d7be","observation_id":"9171fa91-114e-4baf-a206-70594af5493f","resolution":{"observed_at":"2026-08-01T00:27:50.852427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14687","last_updated":"2024-05-20T04:23:45Z","snapshot_observed_at":"2026-08-03T03:59:22.374270Z","submitted_at":"2022-09-29T11:12:27Z","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14687","snapshot_observed_at":"2026-08-01T00:27:50.935350Z","title":"Diffu- sion posterior sampling for general noisy inverse problems.arXiv preprint arXiv:2209.14687, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.935350Z"},"links":{"cited_paper":"/paper/2209.14687","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:520769c1d09480f4957db8dcc5e40893c6af3bea14d8734df37fc262aadd28db","observation_id":"154fd635-0ae8-46ff-95aa-5b3d37b055af","resolution":{"observed_at":"2026-08-01T00:27:50.935350Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:50.995789Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:50.995789Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:8a0308ecff12fdb0362982d220a8baba059f2bc0c139446201afc137a937716a","observation_id":"74df09d0-a05f-4545-8f0b-4562736f4528","resolution":{"observed_at":"2026-08-01T00:27:50.995789Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.025321Z","title":"Tweedie’s formula and selection bias.Journal of the American Statistical Association, 106(496):1602–1614, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.025321Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:5f38a3d2b645423c8855641d3db650ffe2b91d7b800086875085b4fe0a719919","observation_id":"f5863c96-8183-4e41-8cc1-d34250e302ac","resolution":{"observed_at":"2026-08-01T00:27:51.025321Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.110773Z","title":"Hdr image reconstruction from a single exposure using deep cnns.ACM transactions on graphics (TOG), 36(6):1–15, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.110773Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:d58d3917055fd4aba02033d45ad6e7dc897bc137c0a6f563dfbd5b28ddcf276d","observation_id":"88103645-678c-4e93-b9a6-4eff8cc33577","resolution":{"observed_at":"2026-08-01T00:27:51.110773Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.228606Z","title":"Scaling rectified flow trans- formers for high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.228606Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:90f96c1f8b1a73409fd37e9676b03c27750eb31ad322a43ea3024ede4320190b","observation_id":"71e40851-225d-4329-83e2-6c1e790f814f","resolution":{"observed_at":"2026-08-01T00:27:51.228606Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.289301Z","title":"Generative adversarial networks.Communications of the ACM, 63(11):139–144, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.289301Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:a1f528863ae403fec8009d9365a6057884c9bba45f443f08c18e3689d4ca4fbb","observation_id":"ea1e325e-d698-4bae-ba93-2dd72e88fd4a","resolution":{"observed_at":"2026-08-01T00:27:51.289301Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.348430Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.348430Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:b2fd6653ee2e7aa9ff0169322bfd4d4de2e70bc6d3c21078887b537dbe031847","observation_id":"db0a23ab-bd46-4065-8ee2-77c49b6238ee","resolution":{"observed_at":"2026-08-01T00:27:51.348430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-01T00:27:51.400255Z","title":"Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.400255Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:225c4f42fc85cb56ee785cca20e82c9a62b1c9f30c81a070c488a13e170afc03","observation_id":"c76ce7c4-e25a-458f-a679-5a585de2ed0f","resolution":{"observed_at":"2026-08-01T00:27:51.400255Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.488901Z","title":"Video diffusion models.Advances in neural information processing systems, 35:8633–8646, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.488901Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:3f100a39e1202fdaf0346a47ac5fe5ffeb66183cd4a4efb9ce98b94459c5abd3","observation_id":"a299bc00-7b73-4e09-ba72-a96ef5c01bcf","resolution":{"observed_at":"2026-08-01T00:27:51.488901Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.582436Z","title":"Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.582436Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:3e03ce7de904192213364f813e6e153d0484c9963e4c456d7d1a64b6bd595d83","observation_id":"3cfffab1-2e26-4518-80ef-b4b0b27ee746","resolution":{"observed_at":"2026-08-01T00:27:51.582436Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.643810Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.643810Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:7a9ccc3039a778f9ddc9b7f724229d54637deeb474a82f15d9057c4cfe7d3c24","observation_id":"840d4a59-5690-46f4-909c-cb2f1dac9e37","resolution":{"observed_at":"2026-08-01T00:27:51.643810Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.727840Z","title":"Analyzing and improving the image quality of stylegan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.727840Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:93f298013c72028eb05bddc312c1507c27a63212929bb18b49a45e93ad344bb3","observation_id":"b2fdc4e4-7bc9-4a12-a5e5-8c9b96906390","resolution":{"observed_at":"2026-08-01T00:27:51.727840Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.786443Z","title":"Noise2Score: Tweedie’s approach to self-supervised image denoising without clean images","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.786443Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:531cab09e3cabdd547beb311e9d12f526614e23f2c597c6686fc60ff1f839a07","observation_id":"0c6e1c56-ea1a-4d92-99fc-0f34ebec0cf1","resolution":{"observed_at":"2026-08-01T00:27:51.786443Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.860100Z","title":"Learning blind video temporal consistency","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.860100Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:b83bd0c168b53cea4ecefc7797501c4e6c252553669e846b3dc1b6efa7f26c02","observation_id":"8054c698-417e-4f1f-90c5-f940fd29c0e0","resolution":{"observed_at":"2026-08-01T00:27:51.860100Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:51.934024Z","title":"Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions.ACM Transactions on graphics (TOG), 30(4):1–14, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:51.934024Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:34393254bcf1fec8a6f9ba3b39efe0cefc36012e8aa0875401f9d88cd00c1ef5","observation_id":"2bf6379f-b77e-47b1-be36-7dab8f5152d7","resolution":{"observed_at":"2026-08-01T00:27:51.934024Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.014928Z","title":"Deep hdr hallucination for inverse tone mapping.Sensors, 21(12):4032, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.014928Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:4ce63311cfab73cb77b8e8d4e26fd9d21a32300e71772fb7c751cfe2397e7489","observation_id":"a428ef8c-8da9-4209-af32-95464917987d","resolution":{"observed_at":"2026-08-01T00:27:52.014928Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.095373Z","title":"Ex- pandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.095373Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:cf43871338231929cf2079f2d01a55ab18c7adf650538aecb753149f6289151a","observation_id":"236ac184-361e-47f3-8207-3ae11cc4bd4c","resolution":{"observed_at":"2026-08-01T00:27:52.095373Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.170479Z","title":"Energy-based cross attention for bayesian context update in text-to-image diffusion models.Advances in Neural Information Processing Systems, 36:76382–76408, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.170479Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:c2edd2ac65047c2a7463049e89b1078c68701ac7f9dc08f531a50b347fa7f872","observation_id":"b2511b01-35d7-44da-af80-71ebab10b262","resolution":{"observed_at":"2026-08-01T00:27:52.170479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-01T00:27:52.252409Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.252409Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:76351b5331b699b180caddd3563f29e6475fbd505661f6dfe2142f1a1891ba12","observation_id":"aad43901-c905-46a5-b1c2-00eae8916cd0","resolution":{"observed_at":"2026-08-01T00:27:52.252409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-01T00:27:52.311289Z","title":"Hierarchical text-conditional image generation with clip latents.arXiv preprint arXiv:2204.06125, 1(2):3, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.311289Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:a4d036be90917c544c6496f6c2858b443a0d57bea589c054e1d9146f52909c14","observation_id":"964c8d2a-e6f5-410e-ae4c-02fb46cb0d7a","resolution":{"observed_at":"2026-08-01T00:27:52.311289Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.370265Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.370265Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:c078614a772a6eb52a9540f161dc27b835063c5101c3aff8317922dd0ce1f5fc","observation_id":"798cd290-6d4f-42ce-87a7-fd21f636c54a","resolution":{"observed_at":"2026-08-01T00:27:52.370265Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.442178Z","title":"Morgan Kaufmann, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.442178Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:3f9a22104faf2d2bf8588a6ea38b1353e6789ce31c489483525fdd5251559dac","observation_id":"2a7ffdd8-009a-4bba-b1fb-a617498cc6f5","resolution":{"observed_at":"2026-08-01T00:27:52.442178Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.512851Z","title":"High- resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.512851Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:1a0bea084bd7a36e42172463cd208e8e592a619d2e4128d76e1b529f4380f3a7","observation_id":"ad7964a4-c014-4958-851c-7eb121d664f5","resolution":{"observed_at":"2026-08-01T00:27:52.512851Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.571391Z","title":"Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.571391Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:5bbfaaf52058df8e67ba8d19c55af1982f899b14392b6c7ac909091cd4f0712e","observation_id":"41b3c803-01a4-4f84-b95d-a4451091d6d5","resolution":{"observed_at":"2026-08-01T00:27:52.571391Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.630344Z","title":"Bovik, Neil Birkbeck, Yilin Wang, and Balu Adsumilli","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.630344Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:674cde6ef11b59259a197d8111181d9502379abf00e9e5152c528a27736fe41b","observation_id":"e0c28ee5-60a0-46ef-805f-f603193f03b4","resolution":{"observed_at":"2026-08-01T00:27:52.630344Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.688882Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.688882Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:4efea77ad3fa8cd69ea6d46fd546983612faed465d14df9e0366068fa9d0b8e0","observation_id":"1d2e3ac9-8eba-4e3f-b9e7-2526c1c2183a","resolution":{"observed_at":"2026-08-01T00:27:52.688882Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.766035Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.766035Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:8340424346d2f6f0cc8d63f08b42818cb6f9252dc232a37eff8e5f71a8e02b9a","observation_id":"33b54693-ef45-4f37-8a9b-8c978cbc0375","resolution":{"observed_at":"2026-08-01T00:27:52.766035Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:52.865551Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.865551Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:d21a17bd8c5c15bc7e0d6ded7d14b3b54e06caaa78e4a280990b459c6408439d","observation_id":"b0ab0278-5b39-46f8-a7e4-ecf717f42d7b","resolution":{"observed_at":"2026-08-01T00:27:52.865551Z","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":"2026-08-01T00:27:52.924475Z","title":"Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456, 2020","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.924475Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:4e4c846179830c0f624149c51b8fd7a1b49baf22add743238109ddcbb0bafd21","observation_id":"229f1028-bdd7-4955-b677-acdfd9bc12da","resolution":{"observed_at":"2026-08-01T00:27:52.924475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-01T00:27:52.994147Z","title":"Qwen2.5 technical report.arXiv preprint arXiv:2412.15115, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:52.994147Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:0d3186f870315ea3ce6e1717609b044a2f265dd6d53aa40194db4ed467f73709","observation_id":"0f19cf83-2611-485b-ba6d-44efc5efe732","resolution":{"observed_at":"2026-08-01T00:27:52.994147Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:53.167673Z","title":"Lediff: Latent exposure diffusion for hdr generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.167673Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:9a5a3db53e5531a321a9470720098a6cb7f865b3247668a46d6e86518336934e","observation_id":"0c6680e0-c787-4fa5-9bde-ae0c5c73c454","resolution":{"observed_at":"2026-08-01T00:27:53.167673Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:53.393358Z","title":"X2hdr: Hdr image generation in a perceptually uniform space.arXiv preprint arXiv:2602.04814, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.393358Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:3758cd61f314dcc0b354b3bd5983452c7e55eb27c75beb35076b4352688ca483","observation_id":"f7f06b19-b126-4fa0-b4a9-36a110221e26","resolution":{"observed_at":"2026-08-01T00:27:53.393358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06072","last_updated":"2025-03-26T08:33:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-12T11:47:11Z","title":"CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06072","snapshot_observed_at":"2026-08-01T00:27:53.572856Z","title":"Cogvideox: Text-to-video diffusion models with an expert transformer.arXiv preprint arXiv:2408.06072, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.572856Z"},"links":{"cited_paper":"/paper/2408.06072","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:55f6884e6168a09ec1b7ef9658341653ef9c7167a2b5f9cbb9dc093ddd183cab","observation_id":"a02301dd-e4bd-4057-add3-a789f2397f83","resolution":{"observed_at":"2026-08-01T00:27:53.572856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.10789","last_updated":"2022-06-22T01:11:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-06-22T01:11:29Z","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10789","snapshot_observed_at":"2026-08-01T00:27:53.751308Z","title":"Scaling autoregressive models for content-rich text-to-image generation.arXiv preprint arXiv:2206.10789, 2(3):5, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.751308Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:90d16caded3fefbc38bfd3d505f86c0f776a5ffbdf8be57f39c96f6fb749462b","observation_id":"318148f6-1c3e-4a83-8a82-63b959e378b9","resolution":{"observed_at":"2026-08-01T00:27:53.751308Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:53.863211Z","title":"Freedom: Training- free energy-guided conditional diffusion model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.863211Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:b3c2fe577ec55cc88efd791c7f26f4c6934818aeb8ef72148c4e92f3cbb06726","observation_id":"780adccb-e35b-4f6d-a256-643513718c0c","resolution":{"observed_at":"2026-08-01T00:27:53.863211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.06161","last_updated":"2026-04-10T00:14:18Z","snapshot_observed_at":"2026-08-02T11:38:52.346261Z","submitted_at":"2026-04-07T17:56:18Z","title":"DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.06161","snapshot_observed_at":"2026-08-01T00:27:53.935269Z","title":"Diffhdr: Re-exposing ldr videos with video diffusion models.arXiv preprint arXiv:2604.06161, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.935269Z"},"links":{"cited_paper":"/paper/2604.06161","citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:af374bea50d1005df450b817a3f03daac1d447474d2372895d42fc637c456c47","observation_id":"c65a318c-1cf3-443e-bae9-ef7aa3993fdd","resolution":{"observed_at":"2026-08-01T00:27:53.935269Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:27:53.992183Z","title":"Q-eval-100k: Evaluating visual quality and alignment level for text-to-vision content","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T00:27:53.992183Z"},"links":{"citing_paper":"/paper/2607.26237"},"observation_digest":"sha256:b26a56d1c0d9e2f2f045999db240d8ef197e2436add7c136b8cd59e7e36a0949","observation_id":"092fd1a8-b08a-40af-92c5-81b807c4be33","resolution":{"observed_at":"2026-08-01T00:27:53.992183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.26237","last_updated":"2026-07-28T20:17:11Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T13:29:36.744087Z","submitted_at":"2026-07-28T20:17:11Z","title":"LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":43,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":43},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2607.26237."}