{"as_of":"2026-08-17T21:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b1a231038873ba8b8c57807d659c20fb800bb3b06e0e99f319cc537d609c1a5","coverage":[{"denominator":96,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":96,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T18:35:14.174280Z","state":"measured"},{"denominator":98,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":98,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T17:31:13.043232Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T19:33:53.911788Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11211","snapshot_observed_at":"2026-08-05T17:31:13.043232Z","title":"arXiv preprint arXiv:2501.11211","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.16211","last_updated":"2025-08-22T08:34:03Z","snapshot_observed_at":"2026-08-17T04:26:24.384790Z","submitted_at":"2025-08-22T08:34:03Z","title":"Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T17:31:13.043232Z"},"links":{"cited_paper":"/paper/2501.11211","citing_paper":"/paper/2508.16211"},"observation_digest":"sha256:6575c39451106442e6e37d61ae8315d4b04ad6c619093f66aaf13804b8642e18","observation_id":"0d67f577-97a3-4981-b603-7a593a19b6d7","resolution":{"observed_at":"2026-08-05T17:31:13.043232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"cited_work":{"arxiv_id":"2501.11211","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11211","snapshot_observed_at":"2026-06-29T19:33:53.911788Z","title":"Ditto: Accel- erating diffusion model via temporal value similarity,","venue":null,"work_id":"41904710-72d5-4131-95e8-a1b30416613d","year":2025},"citing_paper":{"arxiv_id":"2605.25798","last_updated":"2026-05-25T12:48:17Z","snapshot_observed_at":"2026-08-15T05:46:05.169415Z","submitted_at":"2026-05-25T12:48:17Z","title":"DiSC: Resolution-Scalable Acceleration of Diffusion Models by Exploiting Sparsity and Cached Token Reuse with Hash-based Distribution","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T19:33:05.248105Z"},"links":{"cited_paper":"/paper/2501.11211","citing_paper":"/paper/2605.25798"},"observation_digest":"sha256:0a4d5953f10500ff8737d080b21a0b9ba155a5dc16c74c73aa717170d429254b","observation_id":"0a83f1a8-1cb1-459e-8c50-41b389b25b12","resolution":{"observed_at":"2026-06-29T19:33:53.913614Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.11211/citation-record","integrity":"/paper/2501.11211/integrity","json":"/paper/2501.11211/citation-record.json","paper":"/paper/2501.11211"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:13.790984Z","title":"Slid: Exploiting spatial locality in input data as a computational reuse method for efficient cnn,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.790984Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2f41fa6d3c5467b2d3bfbe215d3e1006a4f66b6dad67f1f2b7ea3c9ecaf2929d","observation_id":"d21a32b6-6c7f-4bb4-8972-75e9b06e1414","resolution":{"observed_at":"2026-08-10T18:35:13.790984Z","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":"2026-08-10T18:35:13.795739Z","title":"Bit-pragmatic deep neural network computing,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.795739Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:37ca6b0d490003f3a07ee4aac48ec416b4237facfc1a8a3adb05eaf28764a97b","observation_id":"41fa4384-3210-4b11-b162-c73b5f9aa1cd","resolution":{"observed_at":"2026-08-10T18:35:13.795739Z","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":"2026-08-10T18:35:13.800115Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.800115Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:a687e660c462f8a52da5c00001e754219a0721b89c9ac414b1cf0bc902073470","observation_id":"77bc4b17-9e4d-44a7-a55c-9e961ec0bb65","resolution":{"observed_at":"2026-08-10T18:35:13.800115Z","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":"2026-08-10T18:35:13.804054Z","title":"A multi-neural network acceleration architecture,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.804054Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:167f81144b2a8b369aa9611b35300a0e02a36974951625f14a5b7ce693e85c0e","observation_id":"ded67450-3700-4704-bf6d-4ee6930fcb8c","resolution":{"observed_at":"2026-08-10T18:35:13.804054Z","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":"2026-08-10T18:35:13.807779Z","title":"Uniq: Uniform noise injection for non-uniform quantization of neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.807779Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b8bc8db442247b03884cd7950bd911563d716b5bbaa5679bbe6924cbd6d07e30","observation_id":"e04de64d-49fa-42f5-a0e6-3cd5cc36ca1e","resolution":{"observed_at":"2026-08-10T18:35:13.807779Z","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":"2026-08-10T18:35:13.811763Z","title":"A survey on generative diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.811763Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:6d42fce8024df84ff3e64ccbbd665fdcc0ca85cac102a84b9317e6b4618f444c","observation_id":"d6ed1acb-3793-4eca-ac97-7b4439ba1f7b","resolution":{"observed_at":"2026-08-10T18:35:13.811763Z","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":"2026-08-10T18:35:13.815550Z","title":"Mix and match: A novel fpga-centric deep neural network quantization framework,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.815550Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8b48c699c024f1c6fafc40040c3618d357374aaaee985034d3f1198d939b384a","observation_id":"8c58d800-75b2-4020-9e21-7b41ea3fa97c","resolution":{"observed_at":"2026-08-10T18:35:13.815550Z","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":"2026-08-10T18:35:13.818805Z","title":"Point cloud acceleration by exploiting geometric similarity,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.818805Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ecbee2d9586b16078048d290951ff58d31191bd4781187e9236837edc82bf931","observation_id":"f5524070-75d6-4aab-a66f-ab942b91a38d","resolution":{"observed_at":"2026-08-10T18:35:13.818805Z","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":"2026-08-10T18:35:13.822015Z","title":"Characterization and analysis of text-to- image diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.822015Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:db714465085ae745a41465016518545f532ed39b69072b609ad077125ddf02bb","observation_id":"13f436a1-2327-4bb2-b3b3-6af5575c5c49","resolution":{"observed_at":"2026-08-10T18:35:13.822015Z","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":"2026-08-10T18:35:13.825147Z","title":"Diffusion models in vision: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.825147Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c0b74a55fa7c938507d78457cb0ff7fbe510df1e8e24aad3d9254527aae0d6b0","observation_id":"d5a3eeac-2e8f-46a0-b31d-5ae5b4eec996","resolution":{"observed_at":"2026-08-10T18:35:13.825147Z","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":"2026-08-10T18:35:13.828351Z","title":"Bit-tactical: A software/hardware approach to exploiting value and bit sparsity in neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.828351Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:bf87c4d2cb202e046cce7f070161bcb0de122f0064c82993564c4855366c6f21","observation_id":"de7569f7-899d-42e6-a940-5abbca988e06","resolution":{"observed_at":"2026-08-10T18:35:13.828351Z","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":"2026-08-10T18:35:13.831757Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.831757Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c0205e016af022ddf37e5d3f5321d7f53bb88ed616ba60212893c6d5140180dd","observation_id":"c6c524ac-fedc-4e19-ae45-20376be47c9f","resolution":{"observed_at":"2026-08-10T18:35:13.831757Z","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":"2026-08-10T18:35:13.834901Z","title":"Diffusion models beat gans on image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.834901Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:66ccf256d568d68bbe07c04e5912af54208f695739e6a7e1d5285e04b5d3c46a","observation_id":"ed291df2-3cd4-40f1-8518-6a9aa203162b","resolution":{"observed_at":"2026-08-10T18:35:13.834901Z","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":"2026-08-10T18:35:13.838514Z","title":"Sparse-dysta: Sparsity- aware dynamic and static scheduling for sparse multi-dnn workloads,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.838514Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:bf469f2ffbd450f49ef4528ca77c89a1f7c46867b764503ef3b328b85a8870c1","observation_id":"d3f9a989-f679-4db8-92c9-1af9f5c6e92c","resolution":{"observed_at":"2026-08-10T18:35:13.838514Z","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":"2026-08-10T18:35:13.842063Z","title":"Deltarnn: A power-efficient recurrent neural network accelerator,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.842063Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:18d1711e178c7348181294ec3fa4133a6638749db94ddeb5e71966e2fdabae81","observation_id":"4b61987c-a15d-4f82-bd28-16a00b785ac2","resolution":{"observed_at":"2026-08-10T18:35:13.842063Z","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":"2026-08-10T18:35:13.845363Z","title":"Edge- drnn: Recurrent neural network accelerator for edge inference,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.845363Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8b890f69e2bba4838e8f5577dfbba8371aed5b20d6d51822f748e177bf5ca435","observation_id":"7b34efd6-e0c8-4b4c-8c08-e8337e1a2d75","resolution":{"observed_at":"2026-08-10T18:35:13.845363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:15.070132Z","title":"Generative ai beyond llms: System implications of multi-modal generation,","venue":null,"work_id":"4cbbfd46-3757-47ef-8815-07ad5e03fb8d","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.848686Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2b9e7884a0f8c903a9476643d2e93cdd7af5df877c08bcafe1c15fa4f917f272","observation_id":"8a7cf0ea-1b5c-4ee0-ab8c-8a3b16bf055c","resolution":{"observed_at":"2026-08-10T18:35:15.074653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:15.056758Z","title":"Sparten: A sparse tensor accelerator for convolutional neural networks,","venue":null,"work_id":"6d87db85-d80e-411a-8210-22e9267216db","year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.852032Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c8ce287767d032e110fb464fd98ea15d1b5aae83b3c477bab25c4d741df1c3c4","observation_id":"d9c91d40-75e2-401e-9728-8fbdeebda404","resolution":{"observed_at":"2026-08-10T18:35:15.060475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:15.043776Z","title":"Eureka: Efficient tensor cores for one-sided unstructured sparsity in dnn inference,","venue":null,"work_id":"8dfcb744-4281-43aa-9d77-4801710775f9","year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.855645Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ef927ed18745e67f76fd6ede4741279709816eba7b7a6c0faaca734939aa7633","observation_id":"30eb593f-17aa-4366-9750-8f6d4b4ee45f","resolution":{"observed_at":"2026-08-10T18:35:15.048054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:13.859065Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.859065Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:d404d8a76eea5192ba58473b479a1b158b21a683f9dec1e4f04bc3c82d759130","observation_id":"ac8e5238-15a5-4aab-a926-4f90cd8540d3","resolution":{"observed_at":"2026-08-10T18:35:13.859065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:15.024228Z","title":"Olive: Accelerating large language models via hardware- friendly outlier-victim pair quantization,","venue":null,"work_id":"5cfd2ea3-1e55-4a3a-8d39-424fd7f9412c","year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.862492Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:6a82d54841ad51f111b0659a9572f7d560cc835a905232e1ad95dc86d73f86b2","observation_id":"00cb102d-8562-4676-a584-787e15747f48","resolution":{"observed_at":"2026-08-10T18:35:15.028621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:15.012350Z","title":"Ant: Exploiting adaptive numerical data type for low-bit deep neural network quantization,","venue":null,"work_id":"2842f3ef-93ae-4cab-9998-2c187e0621fc","year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.865867Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:4da2159669d674fd19ad871c4d0fff20980a5ec3e1422864b361f1a4e3707e69","observation_id":"22da5656-b139-4cd3-89c7-5f50a18cd6d0","resolution":{"observed_at":"2026-08-10T18:35:15.016584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.998987Z","title":"20.2 a 28nm 74.34 tflops/w bf16 heterogenous cim- based accelerator exploiting denoising-similarity for diffusion models,","venue":null,"work_id":"b64f9cb8-4632-4f43-a69e-8746eb155919","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.869609Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:6bd36fc4dbcdad34058d0852f7bc4e9bb3d9f2363d1367c8a24841cb4634d7c8","observation_id":"37be8786-b693-4ffd-8d4f-9f7bef9894af","resolution":{"observed_at":"2026-08-10T18:35:15.004419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1510.00149","last_updated":"2016-02-15T06:25:40Z","snapshot_observed_at":"2026-08-04T16:59:47.843960Z","submitted_at":"2015-10-01T09:03:44Z","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.00149","snapshot_observed_at":"2026-08-10T18:35:13.873128Z","title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.873128Z"},"links":{"cited_paper":"/paper/1510.00149","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:99e819b38df92cd299d8c61c4da96cf733653a694de8e88a24dce60fae97ba09","observation_id":"4e60e65b-7909-4015-88f2-98ab7a7b0f52","resolution":{"observed_at":"2026-08-10T18:35:13.873128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.986943Z","title":"Flexible diffusion modeling of long videos,","venue":null,"work_id":"b7e068d5-5d79-4ccc-8917-158722e7f399","year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.877328Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:7174de9c90fa01271108367bbcb49f28eb50671f8b1767ec9ce45c6929e09530","observation_id":"a2e60c36-e266-4420-ba07-8a52d27e5ca3","resolution":{"observed_at":"2026-08-10T18:35:14.991209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:13.881069Z","title":"Ptqd: Accurate post-training quantization for diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.881069Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:36ca76013f58f4af76320b2780d1db4726f1a2bd7ef94bba802009ce21ecda4b","observation_id":"8dea357d-322a-424a-941c-6a01556093c0","resolution":{"observed_at":"2026-08-10T18:35:13.881069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08718","last_updated":"2022-03-23T19:47:21Z","snapshot_observed_at":"2026-07-06T11:01:02.207193Z","submitted_at":"2021-04-18T05:00:29Z","title":"CLIPScore: A Reference-free Evaluation Metric for Image Captioning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08718","snapshot_observed_at":"2026-08-10T18:35:13.885166Z","title":"Clipscore: A reference-free evaluation metric for image captioning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.885166Z"},"links":{"cited_paper":"/paper/2104.08718","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8c968c658bfba8ba029e8af9709608a558bc1f1bcfe792f035779659e6d0511f","observation_id":"46c64ec4-ab35-4d02-96b1-d7ef3065d8ca","resolution":{"observed_at":"2026-08-10T18:35:13.885166Z","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":"2026-08-10T18:35:13.889389Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.889389Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:9b870d9db491fcaf80d2cf3b3ee50255013712b038b82915b9b7c4e788853faa","observation_id":"2fff56be-a375-4057-b66f-7e9aa44146b7","resolution":{"observed_at":"2026-08-10T18:35:13.889389Z","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":"2026-08-10T18:35:13.892900Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.892900Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:21f8a8df9e15431f823269328fe61adf24d5cd2b58b17d796241d4dd4590b6af","observation_id":"609b44a6-2d59-4bba-a91f-c40920f6cfbb","resolution":{"observed_at":"2026-08-10T18:35:13.892900Z","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":"2026-08-10T18:35:13.896355Z","title":"Cascaded diffusion models for high fidelity image generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.896355Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:921c59d52f1807681669c1d40d0d479a6371b43f0fefe4d16c81c8f537b18b35","observation_id":"99e6a419-fa2f-4676-a634-a91be73c5ffe","resolution":{"observed_at":"2026-08-10T18:35:13.896355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.945302Z","title":"Learning a continuous and reconstructible latent space for hardware accelerator design,","venue":null,"work_id":"2d42cf51-78d0-4b19-a91f-fc0592a89cb6","year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.899914Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:cb70de8bac04885a4d9ef5ab3614c2e0377bd63874b857bfb714c6050fa56d3b","observation_id":"d1e5e2db-1cb0-422b-becb-d60adb4a8757","resolution":{"observed_at":"2026-08-10T18:35:14.949891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:13.903455Z","title":"Tfmq-dm: Temporal feature maintenance quantization for diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.903455Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ca41774b9930e8717c05a29053c7c47b7c48b253a09f070236efb8034382b990","observation_id":"1b5ca1bd-8206-49a3-8cc0-a45ea4ec6d84","resolution":{"observed_at":"2026-08-10T18:35:13.903455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.925882Z","title":"Sibia: Signed bit-slice architecture for dense dnn acceleration with slice-level sparsity exploita- tion,","venue":null,"work_id":"4d678e33-d762-4009-adf9-6549e435b8c3","year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.906875Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:93a5fbda5abdbc011415c38fb1b7036692fee5aa0025e039620c3d0f5a8d1f44","observation_id":"a58f1bd2-865c-4695-80d1-8f5145676422","resolution":{"observed_at":"2026-08-10T18:35:14.930193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.913486Z","title":"Mercury: Accelerating dnn training by exploiting input similarity,","venue":null,"work_id":"46a0df18-4335-4baf-bce8-5b3544885e74","year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.910507Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2112d8c7e4fc94b060c5a18109b1f3779b39473b950c71e03de80fa6d837d986","observation_id":"f3989c25-429e-463e-9688-0026ddb6a417","resolution":{"observed_at":"2026-08-10T18:35:14.917664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.901116Z","title":"Sparsity-aware and re-configurable npu architecture for samsung flagship mobile soc,","venue":null,"work_id":"43bd39b1-9f4e-44b1-bca2-d7d803e4b6c7","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.914354Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c73d8fee49d211d6844ec4fe0d5e24928a5209d1d7a2fac6178da4b75e2e365c","observation_id":"ab42f378-569e-45a6-9e2d-d5d7b3efd115","resolution":{"observed_at":"2026-08-10T18:35:14.905401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06826","last_updated":"2018-04-18T17:25:13Z","snapshot_observed_at":"2026-08-14T19:24:34.372879Z","submitted_at":"2018-04-18T17:25:13Z","title":"Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.06826","snapshot_observed_at":"2026-08-10T18:35:13.917937Z","title":"Dissecting the nvidia volta gpu architecture via microbenchmarking,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.917937Z"},"links":{"cited_paper":"/paper/1804.06826","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:122185dd40dec9d98ad1dbe9b5f92dec96abdbfe8794f630dacf8d05b18fa991","observation_id":"ac3593ce-b2e3-4675-812a-e91987bc5fba","resolution":{"observed_at":"2026-08-10T18:35:13.917937Z","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":"2026-08-10T18:35:13.921827Z","title":"Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware sup- port for embeddings,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.921827Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:d860bb77a4b745c16eab9ec9349db07aec32f80ad39978233faed69391e6a589","observation_id":"902cb957-3d0f-4750-8d10-ef43680a36a3","resolution":{"observed_at":"2026-08-10T18:35:13.921827Z","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":"2026-08-10T18:35:13.925123Z","title":"Stripes: Bit-serial deep neural network computing,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.925123Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:db069b3ee993e32e6a2565546164a62ee417c727a2fbc8f8f02505bb94ac6fdf","observation_id":"b1b64107-bc1f-443d-acee-a28f2f2cd12f","resolution":{"observed_at":"2026-08-10T18:35:13.925123Z","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":"2026-08-10T18:35:13.928683Z","title":"Bk-sdm: Archi- tecturally compressed stable diffusion for efficient text-to-image gen- eration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.928683Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b5c7878a64ab955871bf65ec398e0824d5a5158adf108f39d8a35f9ed18727ee","observation_id":"8abcebea-776a-4767-9715-303f5459bc39","resolution":{"observed_at":"2026-08-10T18:35:13.928683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.864718Z","title":"An energy- efficient gan accelerator with on-chip training for domain-specific op- timization,","venue":null,"work_id":"26e8946c-7d6f-4bec-864e-130b92b56540","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.932180Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b8cf9f8c1e97e0381202c166fcb325f8b217c8a04215b835dde564a1be15e0ad","observation_id":"0f4d298f-854a-4f4f-ad6e-b5949722dfdd","resolution":{"observed_at":"2026-08-10T18:35:14.870076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.851922Z","title":"Airgun: Adaptive granularity quantization for accelerating large language models,","venue":null,"work_id":"16e326fe-2a05-4f1c-8a38-01addbdcb458","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.935614Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:edffd89b78ce669916e44d05fa96944543ca1606db13eddaccc4968f8051cf83","observation_id":"46f6f21b-34f8-4fbf-9fa7-957603f6066d","resolution":{"observed_at":"2026-08-10T18:35:14.856661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-10T18:35:13.938976Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.938976Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:1afe43062c48f040935aa6a2065134ab27b107e7286326869360a0c62964add7","observation_id":"45ea6777-4473-44f0-8940-619e562c35b5","resolution":{"observed_at":"2026-08-10T18:35:13.938976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.839302Z","title":"Cambricon-d: Full-network differential acceleration for diffusion models,","venue":null,"work_id":"71efa5cf-c8ec-443d-9bd1-a0c15f734dd7","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.942861Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2c09f49aad73d292b05881b8e12fbc4a6952e928696d58436b6a1d0f9bc37ee0","observation_id":"565fecaa-3cc2-4393-b97e-d0ab364de382","resolution":{"observed_at":"2026-08-10T18:35:14.843386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00132","last_updated":"2021-06-23T18:28:15Z","snapshot_observed_at":"2026-08-16T18:20:43.657233Z","submitted_at":"2021-05-31T23:00:54Z","title":"On Fast Sampling of Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00132","snapshot_observed_at":"2026-08-10T18:35:13.946401Z","title":"On fast sampling of diffusion probabilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.946401Z"},"links":{"cited_paper":"/paper/2106.00132","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:4876744f2b98c20972f7a8f7e839b90cf6ea06e8195d455538877e4081698462","observation_id":"02cf0c5a-f767-4a35-9f11-27efe8492a21","resolution":{"observed_at":"2026-08-10T18:35:13.946401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.826969Z","title":"Lang, Introduction to linear algebra","venue":null,"work_id":"9a43c175-2b0f-4b4e-a692-b1b6baa2a07d","year":2012},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.950087Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c9707222736a6dce9b36575cea220cbfb3d4bb3df14e7343d5d8db1fb8e0e352","observation_id":"0b3440c0-fb62-480f-8dac-434565de259e","resolution":{"observed_at":"2026-08-10T18:35:14.831075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.815702Z","title":"Shapeshifter: Enabling fine-grain data width adaptation in deep learning,","venue":null,"work_id":"1fbe6438-06d0-4ec0-8885-6b6f175001ce","year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.956887Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:204a8627a1bcbfd1e140d4f1b0861a58d9dd9e87d2d7411f4e1cad4fa80d4e38","observation_id":"00d69da5-fa61-4d33-b3d1-a994fe448cb7","resolution":{"observed_at":"2026-08-10T18:35:14.819527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.802495Z","title":"Exploit- ing inherent properties of complex numbers for accelerating complex valued neural networks,","venue":null,"work_id":"80eb766c-0865-491c-b7e9-59d3de3aaf32","year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.960314Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:0912e583a91e2c597531df92e8c5ad9335ddc7769beb17d6677829d31066a3b1","observation_id":"528acce7-1b71-4fc3-9bc1-d2e56d9568b1","resolution":{"observed_at":"2026-08-10T18:35:14.806650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.789752Z","title":"Independently recurrent neural network (indrnn): Building a longer and deeper rnn,","venue":null,"work_id":"4c526645-5a38-4796-925c-a82d05cca039","year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.963568Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8dbcc7236dbb02477089e2a0371bcf3d9fa9f272682ed5f6124c0cc4ad053a9b","observation_id":"74aeea4d-6e6f-433c-92d7-878e6d7c91c3","resolution":{"observed_at":"2026-08-10T18:35:14.793972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:13.967143Z","title":"Q-diffusion: Quantizing diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.967143Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b36c381f7b542bf5807c4253ec6a477757b4f12b4170d94cdcec5b4f22edc4bd","observation_id":"77aa3913-6f8e-42e1-a9c5-50e29c3a4f38","resolution":{"observed_at":"2026-08-10T18:35:13.967143Z","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":"2026-08-10T18:35:13.971231Z","title":"Snapfusion: Text-to-image diffusion model on mobile devices within two seconds,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.971231Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:1996ca7fcbba3a267fc6d29d9bf6cb9aaa4d94dd0e95681df9fa901b33e144b9","observation_id":"e3150f77-c2f7-432c-aa98-2ff50dfa25c9","resolution":{"observed_at":"2026-08-10T18:35:13.971231Z","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":"2026-08-10T18:35:13.975119Z","title":"E-rnn: Design optimization for efficient recurrent neural networks in fpgas,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.975119Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2364ab2f46ad3fa3018acdc24034c1b5a177b4135f7f30d8b629675cbf95086b","observation_id":"7ba86cbe-9ce9-4768-9df5-e10a78fee526","resolution":{"observed_at":"2026-08-10T18:35:13.975119Z","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":"2026-08-10T18:35:13.978447Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.978447Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:3dd239b9bdb55dc6749b5df4660d912df65377abde6ed4f08a91c81ab932b7ba","observation_id":"73e9e77a-db87-4919-afc4-5ebd723af75e","resolution":{"observed_at":"2026-08-10T18:35:13.978447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.09778","last_updated":"2022-10-31T09:05:25Z","snapshot_observed_at":"2026-08-16T17:20:02.890207Z","submitted_at":"2022-02-20T10:37:52Z","title":"Pseudo Numerical Methods for Diffusion Models on Manifolds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.09778","snapshot_observed_at":"2026-08-10T18:35:13.982149Z","title":"Pseudo numerical methods for diffusion models on manifolds,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.982149Z"},"links":{"cited_paper":"/paper/2202.09778","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:83c697b7ace62c697bfc9a5428b1fffff7f52ca64e60c73446500f040316008c","observation_id":"fb11c616-837a-4735-aab2-ae94d0cda271","resolution":{"observed_at":"2026-08-10T18:35:13.982149Z","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":"2026-08-10T18:35:13.986144Z","title":"S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.986144Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:1b6faa458cbef92b8388ea7815f29a5b3f6871e785de010d6442ae6a56e3bf07","observation_id":"093e5052-20b6-41ed-bc67-39212019d2c9","resolution":{"observed_at":"2026-08-10T18:35:13.986144Z","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":"2026-08-10T18:35:13.989824Z","title":"Repaint: Inpainting using denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.989824Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:5244ce5dd9b5173f2696091c0b37d937b489d09ff0155905b6b7b2dc4fd53cd8","observation_id":"1789337d-d0a2-4160-8928-59cc760bc163","resolution":{"observed_at":"2026-08-10T18:35:13.989824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03048","last_updated":"2025-05-01T09:40:21Z","snapshot_observed_at":"2026-08-13T10:28:06.516901Z","submitted_at":"2024-01-05T19:55:15Z","title":"Latte: Latent Diffusion Transformer for Video Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03048","snapshot_observed_at":"2026-08-10T18:35:13.993361Z","title":"Latte: Latent diffusion transformer for video generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.993361Z"},"links":{"cited_paper":"/paper/2401.03048","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:d85ffd79a6f99b6c44e4859167908393f24f9b0e2d0f89db6581b1a2a49311b5","observation_id":"a7055538-d958-4b69-a83a-0927d34b0278","resolution":{"observed_at":"2026-08-10T18:35:13.993361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.731831Z","title":"Diffy: A d ´ej`a vu-free differ- ential deep neural network accelerator,","venue":null,"work_id":"a3ef8600-4071-4745-8905-3e40d69151ba","year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:13.997073Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:787038b7c4d88989e2d257410dc4a4baec5f449a90ddc4037e0726ebb954d64a","observation_id":"f91442d5-f7db-4d61-bcb0-b428e58de316","resolution":{"observed_at":"2026-08-10T18:35:14.736137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.000920Z","title":"Cacti 6.0: A tool to model large caches,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.000920Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:f35e0b50dac6adf8d24fa4dedf5e61e925b638615eb9623d271109cab08a7178","observation_id":"e7dcbd6d-0437-4dd7-a529-88eac9dea548","resolution":{"observed_at":"2026-08-10T18:35:14.000920Z","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":"2026-08-10T18:35:14.004526Z","title":"Improved denoising diffusion probabilis- tic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.004526Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8ce86eef54ef65ef06fb0f1827b9f06a38d4c371ae03063d2f7d5e3119c1fceb","observation_id":"a14db3b3-cb67-4dc0-8b64-207d9b14e7d4","resolution":{"observed_at":"2026-08-10T18:35:14.004526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.704273Z","title":"Glide: Towards photorealistic image gen- eration and editing with text-guided diffusion models,","venue":null,"work_id":"17360c01-ee4e-498c-9985-1d5280530dac","year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.008227Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:5bf0b914c61bb20ee5aee323151d1b4f6380e4d25c66a55206bfe466e7a00299","observation_id":"3ea35717-a2bb-460b-9735-3d8d88649b27","resolution":{"observed_at":"2026-08-10T18:35:14.709074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.691436Z","title":"Deep reuse: Streamline cnn inference on the fly via coarse-grained computation reuse,","venue":null,"work_id":"89a93416-6ed2-4b00-9635-0a4baad58ca0","year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.011953Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:3b00167fdebd32126fcc7fbf6d1228f565eba09dca4584d9c0e01f372ff05249","observation_id":"f481aeb0-604b-4556-8dc2-0acbad806d1a","resolution":{"observed_at":"2026-08-10T18:35:14.695835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.678466Z","title":"Nvidia a100 tensor core gpu architecture,","venue":null,"work_id":"dbc534d1-1bdc-4152-b48a-4d6f05c4bc22","year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.015461Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:af05e4c0f53c7cc305f58df3f660b194f4b2064c4632c5c5e1e631a3a9d40f8d","observation_id":"bcd2eefb-dbd6-474a-a2ce-4b88e0760c05","resolution":{"observed_at":"2026-08-10T18:35:14.682754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.019146Z","title":"Scnn: An accelerator for compressed-sparse convolutional neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.019146Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:20306b55d9a3eaa79628d2799b9864bcd8cf0e8f058f63318c0a264ba177ea59","observation_id":"f8d08e58-e4ef-4310-a2d2-abcba8d188ce","resolution":{"observed_at":"2026-08-10T18:35:14.019146Z","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":"2026-08-10T18:35:14.022625Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.022625Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:f09b12bc494e92e885daadf1d5f3bef109a274c489411c903e310e96b238efa5","observation_id":"f2931b85-d1a6-4a3d-9aed-a019c376ed5c","resolution":{"observed_at":"2026-08-10T18:35:14.022625Z","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":"2026-08-10T18:35:14.026520Z","title":"Scalable diffusion models with transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.026520Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:a5498cf210f9dcc2b35db0df1f6fad2cbc1ed034193274e7d9d07828667334b6","observation_id":"a09d88be-38fb-41e6-a997-f1b8bab5aaec","resolution":{"observed_at":"2026-08-10T18:35:14.026520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.642048Z","title":"Computation reuse in dnns by exploiting input similarity,","venue":null,"work_id":"4aea090b-ab7b-4cea-bb5d-67211f4eb045","year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.030478Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:5ab3774d95de4bbca03aa73d3a2dba1c612e9675cc5a7dad164bf858eced19ba","observation_id":"0c2d1eab-0855-4851-8a58-8fa61c4c7dc0","resolution":{"observed_at":"2026-08-10T18:35:14.646186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.034224Z","title":"High- resolution image synthesis with latent diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.034224Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b9702a7838db67801829b4ad5ba15b7a1926f3c0d8f897890bc4a2d0f74a81c6","observation_id":"634541c6-f938-4574-a31a-18a6d1bcdfa3","resolution":{"observed_at":"2026-08-10T18:35:14.034224Z","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":"2026-08-10T18:35:14.038021Z","title":"Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.038021Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:9e9ad4c31359b2198270ef776f72b83683db8ca9a9b06e417aae62b511929b18","observation_id":"70ff9e85-0e64-43d6-a2a4-b11b02709dba","resolution":{"observed_at":"2026-08-10T18:35:14.038021Z","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":"2026-08-10T18:35:14.041977Z","title":"Bitblade: Area and energy- efficient precision-scalable neural network accelerator with bitwise summation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.041977Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:49ac72de34efab67669e4aff39c26b6ddfd3b4f8a0f7f1a9725e1dc4de99a967","observation_id":"49a01e2a-d5ca-44f0-bba7-b8238b73063c","resolution":{"observed_at":"2026-08-10T18:35:14.041977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.607021Z","title":"Similarity- aware cnn for efficient video recognition at the edge,","venue":null,"work_id":"3328ff65-66fa-4ed4-ac25-311c6bcf20e4","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.053141Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:05400d8a7aed35a6852a556fea3ce5e2a1ff04ebfbff32106f88d058e05214ad","observation_id":"ef244507-89f5-48c3-a6a8-98b599bfe427","resolution":{"observed_at":"2026-08-10T18:35:14.611390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.058369Z","title":"Palette: Image-to-image diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.058369Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:8a9c2635f9e4540dd1c8b4fd371e1b5781f00167c87bfeee37ef86e5ed21c674","observation_id":"792a5e87-2dcb-4eae-890f-45db322de17b","resolution":{"observed_at":"2026-08-10T18:35:14.058369Z","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":"2026-08-10T18:35:14.062912Z","title":"Photorealistic text-to-image diffusion models with deep language understanding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.062912Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:e48a1ccffda09a584941f95f5cc31ea33adf344c8917c3c8711ba8324c53acfe","observation_id":"cdc5b7a8-767b-41f2-9cb0-acabef635b74","resolution":{"observed_at":"2026-08-10T18:35:14.062912Z","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":"2026-08-10T18:35:14.067304Z","title":"Image super-resolution via iterative refinement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.067304Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:2a0816cb71609d024ca999634067b01c566c9575c4ff4f38a8bc8ee4f81c4f40","observation_id":"79768709-68c4-40d2-9f2f-829f16fa46c7","resolution":{"observed_at":"2026-08-10T18:35:14.067304Z","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":"2026-08-10T18:35:14.072095Z","title":"Improved techniques for training gans,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.072095Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:c79538b7865ccfdff385ac2600497027dfa6d386056eea6da2398b5201fc0c55","observation_id":"b778be45-535c-4f3b-bbe6-746b3ee3b615","resolution":{"observed_at":"2026-08-10T18:35:14.072095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02600","last_updated":"2021-09-12T07:49:25Z","snapshot_observed_at":"2026-08-16T18:33:32.401082Z","submitted_at":"2021-04-06T15:46:16Z","title":"Noise Estimation for Generative Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02600","snapshot_observed_at":"2026-08-10T18:35:14.076349Z","title":"Noise estimation for generative diffusion models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.076349Z"},"links":{"cited_paper":"/paper/2104.02600","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:47ab7cbcb49778b98342b6e8e674d7f03132b5ee7b4c04798f753e8b7f9b0462","observation_id":"5e955b04-afb4-43ab-aead-bd16534f7ec5","resolution":{"observed_at":"2026-08-10T18:35:14.076349Z","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":"2026-08-10T18:35:14.081425Z","title":"Post-training quantiza- tion on diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.081425Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:a93d51f887f30f54953a4a35563a2792079b4ba8b34618326217f7c549a043f5","observation_id":"74093251-cc01-439f-b906-284babd5134f","resolution":{"observed_at":"2026-08-10T18:35:14.081425Z","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":"2026-08-10T18:35:14.085805Z","title":"Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.085805Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:86d171fffe2106ce522ee203d9aecc0b7bb2006874606f2fb5f299a6b37c03f2","observation_id":"de751f1c-30e0-4d20-bdfa-735f1581c893","resolution":{"observed_at":"2026-08-10T18:35:14.085805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.538576Z","title":"Neuron-level fuzzy memoization in rnns,","venue":null,"work_id":"ba8bbeb4-d18f-456e-b27a-ca21500933f7","year":2019},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.090392Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:7cda75167d34c5481214384ef985261e32bb23c51cfc8e15781667367f87508c","observation_id":"6c5c8d37-9f67-4e54-bd56-fc074887caab","resolution":{"observed_at":"2026-08-10T18:35:14.542699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.526702Z","title":"Temporal dynamic quantization for diffusion models,","venue":null,"work_id":"09fc37e7-2dbe-4b4e-ac74-eb6decba8797","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.094832Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:97043960766ce5ce4c79a028bda4e861dc16f131ba2ce6ae65774ef5123b0d8f","observation_id":"361d1438-b47a-457f-8655-a596eaa4a48a","resolution":{"observed_at":"2026-08-10T18:35:14.531102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-10T18:35:14.099539Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.099539Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:b595fd720c0375ef14aacbf04b924afafdd093c1a4ee7b1bbe2934cbc721c029","observation_id":"513d7ea9-d335-48b2-9854-c5784e60fc56","resolution":{"observed_at":"2026-08-10T18:35:14.099539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.512731Z","title":"Drq: dynamic region-based quantization for deep neural network ac- celeration,","venue":null,"work_id":"832d9ec9-ef69-43b6-859f-508b5f4a2157","year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.104696Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ade108de5fec077f2e2a653337eb11820abfb87d1621200fe878c7dd661c42a2","observation_id":"a0ac5515-3563-47e3-86cb-ca0f89454c3b","resolution":{"observed_at":"2026-08-10T18:35:14.517688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.0402","last_updated":"2012-12-03T14:45:31Z","snapshot_observed_at":"2026-08-16T21:21:44.768787Z","submitted_at":"2012-12-03T14:45:31Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1212.0402","snapshot_observed_at":"2026-08-10T18:35:14.110052Z","title":"Ucf101: A dataset of 101 human actions classes from videos in the wild,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.110052Z"},"links":{"cited_paper":"/paper/1212.0402","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:0903402551259677c2be34d216f4cf66b0beee64fa22c3596a09f2ab4c86b66c","observation_id":"54b60d80-64d8-43cb-a9df-cd4ac193baa5","resolution":{"observed_at":"2026-08-10T18:35:14.110052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.499513Z","title":"Freepdk: An open-source variation-aware design kit,","venue":null,"work_id":"4641b58f-e8cd-486b-af5d-98984d578fb3","year":2007},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.115741Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:7c761b39aea217765ba8b448f2257c5e003cbaeeefa26dfc0dca14904024eb49","observation_id":"0a73ce41-d185-4747-96fb-815b9c9a939e","resolution":{"observed_at":"2026-08-10T18:35:14.503703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.486284Z","title":"Strang, Linear algebra and its applications , 2012","venue":null,"work_id":"20bbbfd4-9384-4ea4-9b90-1a321291dd84","year":2012},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.119781Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ad7545a89f887e393fabcb02f07e44a930689b3c5a10a7e77f9fe43fb98aed4f","observation_id":"ca6d8a0e-822d-4f62-995b-5568c4b9d6d6","resolution":{"observed_at":"2026-08-10T18:35:14.490752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.473314Z","title":"Convolutional tensor-train lstm for spatio-temporal learning,","venue":null,"work_id":"2e31597f-c055-4238-a261-e12679d098ba","year":2020},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.124124Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:5932d6cd4cdea97f82e82caa79a144a527f3112bea91506618edec4b39327d43","observation_id":"56d88194-3ad5-45fc-bbd6-e0a16e4b4654","resolution":{"observed_at":"2026-08-10T18:35:14.477311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.460411Z","title":"Spatten: Efficient sparse attention architecture with cascade token and head pruning,","venue":null,"work_id":"753930dc-e782-40ba-bed9-10b34c21c4d7","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.127981Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:eb73b732e41f7aef7d07cfb37fd4803161defcb67ca9ca5beadc1c11e8b5b621","observation_id":"fb35eaae-0740-487e-b7b4-3a0dab1bf34a","resolution":{"observed_at":"2026-08-10T18:35:14.465099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.448648Z","title":"Dual- side sparse tensor core,","venue":null,"work_id":"0f45bca0-6d29-4dcd-b1e7-69fd19af04fb","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.132212Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:35a9c7083dd01138d52b98833d144b6d631003926127e28d22b9433632827a20","observation_id":"0fc20c0f-7d69-441f-aab3-178f0a2b1fff","resolution":{"observed_at":"2026-08-10T18:35:14.452415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.136546Z","title":"Diffusion models: A comprehensive survey of methods and applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.136546Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:f81735d047d212dd9c229f0768517245942b57d4d03617ce59129b5c03c50f69","observation_id":"c9f7adc5-aa4a-411e-8c64-a0b0a67a63d2","resolution":{"observed_at":"2026-08-10T18:35:14.136546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.03365","last_updated":"2016-06-04T09:51:30Z","snapshot_observed_at":"2026-08-08T13:26:17.517503Z","submitted_at":"2015-06-10T15:38:47Z","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.03365","snapshot_observed_at":"2026-08-10T18:35:14.140882Z","title":"Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.140882Z"},"links":{"cited_paper":"/paper/1506.03365","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:be092e8791a7d5722982972c349692a214c03ce4cb9d840720fab9386a7dc327","observation_id":"21fc51bf-13d6-4b5a-8f95-ac13d020361e","resolution":{"observed_at":"2026-08-10T18:35:14.140882Z","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":"2026-08-10T18:35:14.145661Z","title":"Mokey: Enabling narrow fixed-point inference for out-of-the-box floating-point transformer models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.145661Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:68b26f8027a8af550153ceaa57f4e16ba2dca302e22b1dcf4a99510c061a97c5","observation_id":"277766f1-c2e9-4767-a969-bc9f34e5b4b2","resolution":{"observed_at":"2026-08-10T18:35:14.145661Z","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":"2026-08-10T18:35:14.150187Z","title":"Adding conditional control to text-to-image diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.150187Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:db45554549403c54483621695325c47adce40bf452bb594e5724884492990a3b","observation_id":"b624bbb4-5e83-4ffd-871f-e21304d104ff","resolution":{"observed_at":"2026-08-10T18:35:14.150187Z","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":"2026-08-10T18:35:14.154874Z","title":"Mo- tiondiffuse: Text-driven human motion generation with diffusion model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.154874Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:3bb9db1c95c4fe6a591a332c1e2d1188f54be6e533de539fa8a9fcb5b0ba1487","observation_id":"f587b792-ba74-4845-b627-aa0e10260a43","resolution":{"observed_at":"2026-08-10T18:35:14.154874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.408236Z","title":"Training for multi- resolution inference using reusable quantization terms,","venue":null,"work_id":"ff80f79b-6504-40b3-8ebb-c16c0d3a0801","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.159400Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:38f817124a46f0dad4bb756ec9a533da98bb497eddf07bf6807141e2416438af","observation_id":"4b8282fe-0ec1-4686-8c9e-2898094f242a","resolution":{"observed_at":"2026-08-10T18:35:14.412547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.395387Z","title":"η-lstm: Co-designing highly-efficient large lstm training via exploiting memory-saving and architectural design opportunities,","venue":null,"work_id":"8c21b410-1c7b-4ca2-81a2-d0d01e38cc5b","year":2021},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.164436Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:d55c454da688288c623c30b8f774fb1a00b38eddd8910dd602f0a96645e3f406","observation_id":"b369e891-6cb1-4c8a-a15d-64a0806194a4","resolution":{"observed_at":"2026-08-10T18:35:14.399866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.09671","last_updated":"2023-09-07T14:08:07Z","snapshot_observed_at":"2026-08-16T17:20:05.913487Z","submitted_at":"2022-02-19T20:18:49Z","title":"Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.09671","snapshot_observed_at":"2026-08-10T18:35:14.169168Z","title":"Truncated diffusion proba- bilistic models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.169168Z"},"links":{"cited_paper":"/paper/2202.09671","citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:ab29a63e82c650a17cbc8402e1358cfe826461710699ec5a0b2212651bc1cb70","observation_id":"f19a27d9-9ef3-4eff-b45b-0038fd351cd3","resolution":{"observed_at":"2026-08-10T18:35:14.169168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:35:14.380800Z","title":"Open-sora: Democratizing efficient video production for all,","venue":null,"work_id":"f0ebecad-06e4-4153-8bcf-a61dc0f2077f","year":2024},"citing_paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-10T18:35:14.174280Z"},"links":{"citing_paper":"/paper/2501.11211"},"observation_digest":"sha256:49a4869b77853c26c4400d719d3840706255e3e847ebb2e67176b286e6e73cf0","observation_id":"96b2811f-12aa-4b7b-b95d-30770abba184","resolution":{"observed_at":"2026-08-10T18:35:14.386821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.11211","last_updated":"2025-01-20T01:03:50Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-17T13:44:00.120667Z","submitted_at":"2025-01-20T01:03:50Z","title":"Ditto: Accelerating Diffusion Model via Temporal Value Similarity"},"reference_resolution":{"displayed":96,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":61,"verified_exact":0,"verified_fuzzy":35},"total_outbound_references":96},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 2 inbound Pith citation observations for arXiv:2501.11211."}