{"as_of":"2026-08-11T19:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:09778b9e9667c8cb4f72d6ae242ad5566f24eff867d1a04fe254613335e9fdce","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T06:49:04.349040Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2607.11136/citation-record","integrity":"/paper/2607.11136/integrity","json":"/paper/2607.11136/citation-record.json","paper":"/paper/2607.11136"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.15381","last_updated":"2025-05-09T22:31:01Z","snapshot_observed_at":"2026-08-11T10:39:14.780828Z","submitted_at":"2024-11-22T23:46:13Z","title":"DiffServe: Efficiently Serving Text-to-Image Diffusion Models with Query-Aware Model Scaling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15381","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Sitara- man, and Hui Guan","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2411.15381","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:3b5c6f6f725d69359579e9ae4021abd9109da9242f23314866321ff32519de75","observation_id":"9f45b247-6672-4d76-81e7-15703380acc9","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:a32b39b2f0ed5b714659043cc5576e968f025ac15518c954064c85a7799aea7f","observation_id":"17d6a524-d7b9-4c3a-918a-01b8b1595472","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09800","last_updated":"2023-01-18T17:31:52Z","snapshot_observed_at":"2026-08-06T07:25:23.651829Z","submitted_at":"2022-11-17T18:58:43Z","title":"InstructPix2Pix: Learning to Follow Image Editing Instructions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09800","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2211.09800","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:6ace3ad7bcf5abdffc6d83ce857c492217a8b98bd2b7398f376c92f0867f91dc","observation_id":"d7f77fce-cb2f-4147-8067-3f8ee17353b0","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00426","last_updated":"2023-12-29T16:42:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-30T16:18:00Z","title":"PixArt-$\\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00426","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2310.00426","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:27af98a3639df918cc4c21a372aa2caac64a1ff2cf3e66e3f43a9a994b415796","observation_id":"2fc297ce-ec00-45c9-a0cc-29b8d492dac8","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11529","last_updated":"2023-06-21T17:02:45Z","snapshot_observed_at":"2026-08-11T12:42:23.180603Z","submitted_at":"2023-01-27T04:22:27Z","title":"PLay: Parametrically Conditioned Layout Generation using Latent Diffusion","version":2},"cited_work":{"arxiv_id":"2301.11529","doi":"10.48550/arxiv.2301.11529","metadata_source":"pith","pith_arxiv_id":"2301.11529","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"PLay: Parametrically Conditioned Layout Generation using Latent Diffusion","venue":"cs.LG","work_id":"177a9e3c-ea61-4f1c-9b02-35eaca0bd4ab","year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2301.11529","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:a86642c3b85f2c82a6ed5a05851612b264ceb510eb35e166934e80d7dcc6ddb8","observation_id":"87905578-2d6d-4006-80bc-9d978abc4ff7","resolution":{"observed_at":"2026-07-14T06:50:17.750487Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-07-14T13:49:58.65752+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T13:49:58.65752+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14430","last_updated":"2025-12-03T10:59:23Z","snapshot_observed_at":"2026-08-08T21:50:02.096694Z","submitted_at":"2024-05-23T11:00:07Z","title":"PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14430","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2405.14430","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:1ae8978739a8423a0a68e3f3080f59f54b6f7ad898d17972912929076ee116f7","observation_id":"5ef2f101-8787-4b9d-8b7a-8096669ca2fe","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:bc47d007c28efeb965f65b023bd488ec59fc4c4ad22e025c644c985fb2017c13","observation_id":"bcc86e14-76e9-41be-b127-394d7b36e42c","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00103","last_updated":"2024-12-30T19:00:25Z","snapshot_observed_at":"2026-07-06T20:14:54.297131Z","submitted_at":"2024-12-30T19:00:25Z","title":"LTX-Video: Realtime Video Latent Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00103","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2501.00103","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:0f29599963107089a2554b20891ff55a96c74635cd3917fd1afe6d010ccaa0ca","observation_id":"9da2bfcf-7b86-4872-9546-5b82d2a8277b","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02303","last_updated":"2022-10-05T14:41:38Z","snapshot_observed_at":"2026-07-06T13:59:57.800591Z","submitted_at":"2022-10-05T14:41:38Z","title":"Imagen Video: High Definition Video Generation with Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02303","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Kingma, Ben Poole, Mohammad Norouzi, David J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2210.02303","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:44c9972fddfa065912d8eefd5eeb3a59a232cf5e967340f9126cf7c0301fc5c6","observation_id":"233a8f4e-9883-4bb4-a63b-ca6f2a5bd307","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:bf4f0a518aa7a216a85e7666a85188fd840ca1ff3340ee1dfdd44b74e565adac","observation_id":"a1213959-2085-42e9-a882-a61c61c4e4cd","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:682d415c58fc8e0f11c5edc17bce91e89b6cbca5e5d850a2fc241586aa652552","observation_id":"f673e84c-7ab7-4c8f-998c-6b22b4424d16","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.02653","last_updated":"2020-05-14T17:46:43Z","snapshot_observed_at":"2026-08-10T05:42:28.168378Z","submitted_at":"2019-10-07T07:54:06Z","title":"Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.02653","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Gonzalez, and Ion Stoica","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/1910.02653","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:2f8dec87399349766d995fe69cec103de05e58e5188d3a9e057648bc6a404b09","observation_id":"63342bb5-4f5d-4604-8bd8-8959d02b57a2","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:b93354a2a48a949e922b58565ea92a879b78eaa941de7d0f77fae71918f7d6e3","observation_id":"30d46957-d819-4a1d-89ba-098a36e78d92","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02397","last_updated":"2024-11-07T17:06:32Z","snapshot_observed_at":"2026-07-06T19:45:00.034364Z","submitted_at":"2024-11-04T18:59:44Z","title":"Adaptive Caching for Faster Video Generation with Diffusion Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02397","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Ryoo, and Tian Xie","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2411.02397","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:7f41555a44f53d78109074b57c083e2d4dda2f3be129e036a7594f0deae3eb72","observation_id":"d55c3d17-3912-40e7-889a-2c94d61d2cf1","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09616","last_updated":"2021-03-18T06:20:23Z","snapshot_observed_at":"2026-08-11T15:51:05.764065Z","submitted_at":"2020-06-17T02:49:59Z","title":"Dynamic Tensor Rematerialization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09616","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2006.09616","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:20374d3df3600557a56e566859ec99d67925c2b2d70dba5c0d3ea342d6b3e12e","observation_id":"4fdda188-073d-4a17-ba5d-a9556ff2be17","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:4e614923086b4a7fea027d8f0c5da30696f37e0dcfa19f58b46d954cad30d569","observation_id":"208bd600-6585-429a-9b22-a5c22170a53a","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.00448","last_updated":"2022-02-23T05:28:18Z","snapshot_observed_at":"2026-08-01T23:30:59.007847Z","submitted_at":"2022-02-23T05:28:18Z","title":"Memory Planning for Deep Neural Networks","version":1},"cited_work":{"arxiv_id":"2203.00448","doi":"10.48550/arxiv.2203.00448","metadata_source":"pith","pith_arxiv_id":"2203.00448","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Memory Planning for Deep Neural Networks","venue":"cs.LG","work_id":"bff037fc-00f9-4110-9637-fe854a21be5d","year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2203.00448","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:03e58c804c84712a1148e5feac4a9afd4ac8527c22f57a1bd465ae39298c01ff","observation_id":"57073254-8486-4e51-b60e-19d6c4b09430","resolution":{"observed_at":"2026-07-14T06:50:17.802559Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-07-14T13:50:00.405024+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T13:50:00.405024+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19481","last_updated":"2024-07-14T21:30:14Z","snapshot_observed_at":"2026-08-06T04:59:18.961611Z","submitted_at":"2024-02-29T18:59:58Z","title":"DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19481","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2402.19481","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:65ce7c3e0ba60c53d466da79f6da92071ad82fe1c2fbbf2e4428dc04c510eeac","observation_id":"426c0ca2-4267-4710-a975-ff60c842c739","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02031","last_updated":"2024-12-06T11:47:06Z","snapshot_observed_at":"2026-08-10T11:32:01.566164Z","submitted_at":"2024-07-02T07:59:08Z","title":"SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02031","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2407.02031","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:82627f65d12a12d14b9e6e0b5aa5d419c870eaeda6c635af856ff974fb84a59d","observation_id":"a4146c13-23cf-4bce-813f-480c4ab585ce","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:09b205b88299a236c2b3c51cb156139801f09fa717686eee5ac5008e888268b8","observation_id":"e799dda9-2e11-4f8b-8968-1e14353af1fb","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10440","last_updated":"2023-03-25T17:32:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T18:59:59Z","title":"Magic3D: High-Resolution Text-to-3D Content Creation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10440","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2211.10440","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:0bb547dd7b039664581af432746f87862594dba0af786171feec85d5944dd1f4","observation_id":"eedc6551-8c78-4bd0-b452-61f396fc5646","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:64446c6745c4ff637663b39c666d2e289c1f2e317854a08e9b501994e03f9c45","observation_id":"3b0a19ea-60e4-4498-b7b1-5f8b37f4ef6c","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19108","last_updated":"2025-03-18T04:49:23Z","snapshot_observed_at":"2026-07-06T19:58:31.184255Z","submitted_at":"2024-11-28T12:50:05Z","title":"Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19108","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2411.19108","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:284196cfc0bb2255fa3a2f8d1115c1c75e3db7fcf2373680bad53ce9252cd3f6","observation_id":"f425a1f5-fdf3-410c-8feb-a0ac9bdfc956","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01323","last_updated":"2025-03-03T09:04:51Z","snapshot_observed_at":"2026-08-07T17:34:21.569243Z","submitted_at":"2025-03-03T09:04:51Z","title":"CacheQuant: Comprehensively Accelerated Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01323","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2503.01323","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:0dd4535f6c9b4a710afabb59efa87310efa7ed8b77196dc5903e8ab4f4895977","observation_id":"f695e495-bf1f-4545-94bc-df744ddf51b0","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.01565","last_updated":"2026-06-18T01:08:24Z","snapshot_observed_at":"2026-08-10T05:37:30.922463Z","submitted_at":"2025-10-02T01:23:32Z","title":"TetriServe: Efficiently Serving Mixed DiT Workloads","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.01565","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Ma, Ang Chen, and Mosharaf Chowdhury","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2510.01565","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:6bdc33f09e8d0d72ae455b658f5fe54760533cd4cefe72ef0ca252123c97a786","observation_id":"229f64de-d2be-4d61-983a-90bab428e4f4","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:5bab73652f4dd044fe000b52619b2bc6ca3ca9d6868f94d9bbefe7d999b7119e","observation_id":"a8c88670-e3a0-42a4-aebd-048c88a46977","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.01073","last_updated":"2022-01-05T00:07:35Z","snapshot_observed_at":"2026-08-08T06:35:17.078531Z","submitted_at":"2021-08-02T17:59:47Z","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.01073","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2108.01073","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:d954f4ea0d4c8e2ad3be65d77bd4fbdf4eb8006032688f650122d34587967d46","observation_id":"1ce83165-3c93-4898-8b7a-33056e2c2d7c","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10741","last_updated":"2022-03-08T18:18:49Z","snapshot_observed_at":"2026-08-07T12:21:17.790675Z","submitted_at":"2021-12-20T18:42:55Z","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10741","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2112.10741","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:78e1a96334538c967d0f67b239a0460f9a201e2b943565bf5e52955093ed4754","observation_id":"f876c6b6-e0b0-45c9-9682-9f41b8fe9c57","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:2416b9716e7b859209d999d21167e0ebda84dc6b6eaaeb9740ee5160bc03b71e","observation_id":"283db593-dbf8-4ce4-973c-d0150d1084be","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09748","last_updated":"2023-03-02T09:06:55Z","snapshot_observed_at":"2026-07-06T14:32:37.317828Z","submitted_at":"2022-12-19T18:59:58Z","title":"Scalable Diffusion Models with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09748","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2212.09748","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:20a196f3f606bb191473e2100c38de67440387ce2e8d8adec055fae965d1d320","observation_id":"53ecb73e-96a5-4425-9b31-eaaee6ca75bc","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14988","last_updated":"2022-09-29T17:50:40Z","snapshot_observed_at":"2026-07-06T13:57:54.539656Z","submitted_at":"2022-09-29T17:50:40Z","title":"DreamFusion: Text-to-3D using 2D Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14988","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Barron, and Ben Milden- hall","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2209.14988","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:bad892b82b59b3c60e15997640626321b634b56d3f27d05588ba2dc5d5c70e53","observation_id":"81d58303-1a83-47c2-9bdd-46bbbb7f56dc","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:e609c623320b6e095e19abe06802c9419f7c45ce9b8e4b6e0f35886e4e161f99","observation_id":"9558cdae-7b0c-4033-a8b0-ad9793b826ee","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:d295652d906b213a8d52bdbadefe9df10ad1d8afb96052d04084e3fd2675a29b","observation_id":"022b5a1d-e607-4e73-be90-fae2496a384a","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:7ff90278620aa936f5586bffa14c2cc8d214cf9cb0be859864ad1081b17ba1b3","observation_id":"6a283efb-c4f1-46a9-89b3-9ebe72f36ab3","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1405.2020","last_updated":"2014-05-08T17:04:15Z","snapshot_observed_at":"2026-08-04T16:50:32.529025Z","submitted_at":"2014-05-08T17:04:15Z","title":"Generalized Slow Roll for Tensors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1405.2020","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"InInternational Conference for High Performance Comput- ing, Networking, Storage and Analysis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/1405.2020","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:0c91a869a8133f57eca68ab3d9a70692f78ac8bd7f24e2567ff8a82bba85e1fe","observation_id":"2a35cd99-44c7-45cf-8fda-d2b5f8895139","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:66300ad51de8086d1e28feac560e431e5f175471483ec2dd1af3d6afebc599a5","observation_id":"b532abc5-4285-48be-b925-073f0ddf5e64","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:2c48f6f6524cffbb26436ead185f881102f281bc3e7e4e4bbcd5cff2ec49256d","observation_id":"d22e3aed-eda0-4316-8912-dd9b8e66fbad","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11487","last_updated":"2022-05-23T17:42:53Z","snapshot_observed_at":"2026-07-06T13:12:59.688989Z","submitted_at":"2022-05-23T17:42:53Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11487","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":"Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2205.11487","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:c9f2840c86a32b0b086f9c4542167e7c0070ceec35c2ffe3bac90aedc48a35bd","observation_id":"daba0f61-7d68-4822-b99d-304d2de10018","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:625d82ae035dd245649ea12e6ad3834f9ae5d272f27fa5eb9f32df4a8336eeb1","observation_id":"3114bc1d-4fb7-4d23-85f1-58e74d78dc00","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14435","last_updated":"2024-04-07T18:04:04Z","snapshot_observed_at":"2026-08-03T12:28:52.482674Z","submitted_at":"2023-06-26T06:04:09Z","title":"DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.14435","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2306.14435","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:05442b110c5370afca7ee657b2c23044b054508c0188d5afc59b10385e0977ab","observation_id":"61bdc386-4d85-44a3-bcb1-b9bf98a117d0","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16317","last_updated":"2023-10-16T01:51:04Z","snapshot_observed_at":"2026-08-04T19:45:50.364771Z","submitted_at":"2023-05-25T17:59:42Z","title":"Parallel Sampling of Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16317","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2305.16317","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:0180ce0c71ddb5e0cddf6fcb6778968bbbed9518833e1a368c9aadd99dd3e97a","observation_id":"fde3cd28-1f2a-4723-aa96-84fbae84fd1b","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14792","last_updated":"2022-09-29T13:59:46Z","snapshot_observed_at":"2026-07-06T13:57:47.051387Z","submitted_at":"2022-09-29T13:59:46Z","title":"Make-A-Video: Text-to-Video Generation without Text-Video Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14792","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2209.14792","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:b3a8c4fb1bcbe8002857a8d12eaab9f1ae3251d435962838617cc401ecd6cd4f","observation_id":"e703b66d-a711-4d2f-8fb6-5a50d59e85dd","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:b10ec5bf519500dc3e1b5a249f46d880195da0f62ab59eefce82bf1cc7b167c5","observation_id":"7689a897-a902-4d2c-9d68-47d2163adfc2","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:51ba94d063e257d26e75d122ea2f53949279df9023f43e2220cf571c2d3e17b4","observation_id":"63658f72-e59d-4fff-b20b-fa3266141501","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15980","last_updated":"2022-06-21T14:52:56Z","snapshot_observed_at":"2026-08-04T04:53:17.741219Z","submitted_at":"2022-03-30T01:40:25Z","title":"DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation","version":2},"cited_work":{"arxiv_id":"2203.15980","doi":"10.48550/arxiv.2203.15980","metadata_source":"pith","pith_arxiv_id":"2203.15980","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation","venue":"cs.LG","work_id":"f1932858-d1ec-44b3-8873-624b44f1beea","year":2022},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2203.15980","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:70d7770cf1d1390d91064cd8f4febbdc0eaf1e60001f9069cc36ae9909fa3bd6","observation_id":"b86a84fe-6658-4a29-a40d-757dccce7c08","resolution":{"observed_at":"2026-07-14T06:50:17.829889Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-07-14T13:50:03.175725+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T13:50:03.175725+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:57da8c2b51b8b9d9a4c7e325265874a04de6eb002a3373eccf23fb3aac59123b","observation_id":"fb8a7af3-a22d-4c2f-bb33-b911f47fc807","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:955bea5b58970bc277ee148563adfdab8afed042905ceb228476808109fcd6ac","observation_id":"b08d6257-796c-4029-b842-7c9284fce05a","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:b21c6264c72e5471a8d395f5c5b953b949c229e1065a8f3c5feab719b821090f","observation_id":"808ac683-9eca-40de-8f42-0126329e20af","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:bf48c872984f01a73350f941ae6b738ff5b8211706d5b81d7c25d9100c0f85f7","observation_id":"501773a7-bba0-4ec0-bf70-971ff5ad1c75","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:77d0f798637aecf000ce622be8d9d04793b1ad8864e0be3411d2d0a8f17d3296","observation_id":"08e677e0-7b57-45dc-b7ab-25050ab97802","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2408.06072","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:2276f5b7c26519f8da73a89e7e148364f5eaa111fa43a3879b60ce2790f34791","observation_id":"fac48595-db39-4d04-a3fe-7dd37fdadd83","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:414bc26f0a8f3f94daeb155c589a04dd1c46d6792e33a906e4ec4db84bf4cb0a","observation_id":"8984dfde-4658-4a87-8ad6-404964589871","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05543","last_updated":"2023-11-26T22:26:12Z","snapshot_observed_at":"2026-08-04T15:27:22.366324Z","submitted_at":"2023-02-10T23:12:37Z","title":"Adding Conditional Control to Text-to-Image Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05543","snapshot_observed_at":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"cited_paper":"/paper/2302.05543","citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:7e9d84363fe59cd6b7a68c2a0b435b59e6d384e495237a16dd893a07d263f020","observation_id":"ec6de809-379f-488b-bc3b-5b3f1729352a","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","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":"2026-07-14T06:49:04.349040Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-14T06:49:04.349040Z"},"links":{"citing_paper":"/paper/2607.11136"},"observation_digest":"sha256:c15712a4b5b638f41550e8e9988e40830667d7069e7e2fb92aa00ec8ee4c8ef8","observation_id":"258259b8-8ebc-42e7-9e43-7ac3812b3d32","resolution":{"observed_at":"2026-07-14T06:49:04.349040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11136","last_updated":"2026-07-13T06:12:41Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-11T08:55:37.982854Z","submitted_at":"2026-07-13T06:12:41Z","title":"Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":49,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":54},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.11136."}