{"as_of":"2026-08-18T08:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5f6a35a1b6880d94b7a7a91a6054a5f946b6392a7a968710e76705430fbb0c4","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T18:10:53.252922Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:19:45.943262Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T14:43:31.760566Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00688","snapshot_observed_at":"2026-08-08T16:39:14.107007Z","title":"High-order matching for one-step shortcut diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06167","last_updated":"2025-02-10T05:36:30Z","snapshot_observed_at":"2026-08-13T16:11:29.220404Z","submitted_at":"2025-02-10T05:36:30Z","title":"Universal Approximation of Visual Autoregressive Transformers","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-08T16:39:14.107007Z"},"links":{"cited_paper":"/paper/2502.00688","citing_paper":"/paper/2502.06167"},"observation_digest":"sha256:4a20979af93c07c308841f47af79a02ab465627051078e3e40bac35e5037ce1c","observation_id":"87802fc2-e708-444b-bf09-461ce0a1ea86","resolution":{"observed_at":"2026-08-08T16:39:14.107007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00688","snapshot_observed_at":"2026-08-15T23:19:45.943262Z","title":"High-order matching for one-step shortcut diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04946","last_updated":"2025-05-08T04:49:52Z","snapshot_observed_at":"2026-08-15T23:15:00.319223Z","submitted_at":"2025-05-08T04:49:52Z","title":"T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T23:19:45.943262Z"},"links":{"cited_paper":"/paper/2502.00688","citing_paper":"/paper/2505.04946"},"observation_digest":"sha256:d9a6725bc2154f07423385a5093fc931f8a0960b0e2f198bcae32534f31bf226","observation_id":"bb76a354-c470-4734-9513-319da0526a52","resolution":{"observed_at":"2026-08-15T23:19:45.943262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00688","snapshot_observed_at":"2026-08-15T20:56:33.855710Z","title":"High-order matching for one-step shortcut diffusion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11892","last_updated":"2025-05-17T08:03:50Z","snapshot_observed_at":"2026-08-16T00:05:45.198399Z","submitted_at":"2025-05-17T08:03:50Z","title":"Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T20:56:33.855710Z"},"links":{"cited_paper":"/paper/2502.00688","citing_paper":"/paper/2505.11892"},"observation_digest":"sha256:d1b08114a2260fcd7298908851a52c905a5417b676a6c2b4c673b27dd753ebad","observation_id":"69144943-d0d6-4943-ac1d-0af11df1a0e7","resolution":{"observed_at":"2026-08-15T20:56:33.855710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00688","snapshot_observed_at":"2026-08-07T15:10:57.772656Z","title":"High-order matching for one-step shor tcut diﬀusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16284","last_updated":"2025-05-22T06:26:28Z","snapshot_observed_at":"2026-08-15T08:26:08.362157Z","submitted_at":"2025-05-22T06:26:28Z","title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:10:57.772656Z"},"links":{"cited_paper":"/paper/2502.00688","citing_paper":"/paper/2505.16284"},"observation_digest":"sha256:0fbfd13486ce3c482fcc285c26758e71d3377e91b1aeb951ca8268cb8af18e30","observation_id":"397d7ef6-e300-4ac1-bb06-9320443501db","resolution":{"observed_at":"2026-08-07T15:10:57.772656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"cited_work":{"arxiv_id":"2502.00688","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.00688","snapshot_observed_at":"2026-08-06T14:43:31.760566Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","venue":"cs.CV","work_id":"38edf73a-a87b-4abf-b3b1-e90441b5bd3e","year":2025},"citing_paper":{"arxiv_id":"2507.18107","last_updated":"2025-07-24T05:37:08Z","snapshot_observed_at":"2026-08-17T01:41:24.598966Z","submitted_at":"2025-07-24T05:37:08Z","title":"T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:25.915523Z"},"links":{"cited_paper":"/paper/2502.00688","citing_paper":"/paper/2507.18107"},"observation_digest":"sha256:62f56eb556e8a45047bb8dc03f24599f2267637a69a8574b57a3153e8495936b","observation_id":"92c1b818-85ba-4dc2-b5df-3d432c487fe1","resolution":{"observed_at":"2026-08-06T14:43:31.834179Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.00688/citation-record","integrity":"/paper/2502.00688/integrity","json":"/paper/2502.00688/citation-record.json","paper":"/paper/2502.00688"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.12712","last_updated":"2023-04-13T20:41:31Z","snapshot_observed_at":"2026-08-03T04:49:15.195814Z","submitted_at":"2023-03-22T16:51:28Z","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12712","snapshot_observed_at":"2026-08-09T18:10:52.945877Z","title":"Sparks of artificial general intelligence: Early experiments with gpt-4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.945877Z"},"links":{"cited_paper":"/paper/2303.12712","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:adb0a3d1abf16477bb6513632a5f807d2b50ade873f0854d5f0bd634ce00cc41","observation_id":"a8a116ab-4b6d-4c32-85e6-b3f95a953620","resolution":{"observed_at":"2026-08-09T18:10:52.945877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17482","last_updated":"2023-05-27T14:23:14Z","snapshot_observed_at":"2026-08-16T15:29:02.606753Z","submitted_at":"2023-05-27T14:23:14Z","title":"Federated Empirical Risk Minimization via Second-Order Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17482","snapshot_observed_at":"2026-08-09T18:10:52.956380Z","title":"Federated empirical risk minimization via second-order method","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.956380Z"},"links":{"cited_paper":"/paper/2305.17482","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:f1355644dd89bed617184542fa3378444c2b14dcd2cb7ae7cd82893f81228674","observation_id":"a2ad1cf4-1db9-4825-bd38-259f5395da61","resolution":{"observed_at":"2026-08-09T18:10:52.956380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11416","last_updated":"2022-12-06T21:39:48Z","snapshot_observed_at":"2026-08-17T10:20:11.128461Z","submitted_at":"2022-10-20T16:58:32Z","title":"Scaling Instruction-Finetuned Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.11416","snapshot_observed_at":"2026-08-09T18:10:52.966342Z","title":"Scaling instruction-finetuned language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.966342Z"},"links":{"cited_paper":"/paper/2210.11416","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:fb56616ad685c70afdb5cf4b06a66749fe3253de7ec07b4711efd46ef16b1d02","observation_id":"f6270de9-581b-4ab9-a31f-77be2ebd15ce","resolution":{"observed_at":"2026-08-09T18:10:52.966342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11268","last_updated":"2025-02-28T22:12:33Z","snapshot_observed_at":"2026-08-16T13:09:20.125737Z","submitted_at":"2024-10-15T04:44:23Z","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.11268","snapshot_observed_at":"2026-08-09T18:10:52.981593Z","title":"Bypassing the exponential dependency: Looped transformers efficiently learn in-context by multi- step gradient descent","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.981593Z"},"links":{"cited_paper":"/paper/2410.11268","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:3b44b8eba5b77dd8eda66d0e49bb44a36490da4615eacf02dde7bb65d32fd448","observation_id":"6dfc83c9-49b8-42a4-bd67-5003348c49e6","resolution":{"observed_at":"2026-08-09T18:10:52.981593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06148","last_updated":"2025-02-20T18:38:08Z","snapshot_observed_at":"2026-08-16T15:11:45.900161Z","submitted_at":"2024-12-09T02:01:18Z","title":"The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06148","snapshot_observed_at":"2026-08-09T18:10:52.986333Z","title":"The compu- tational limits of state-space models and mamba via the lens of circuit complexity","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.986333Z"},"links":{"cited_paper":"/paper/2412.06148","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:de24b5e884c3355412230ecadbbb0e3b36b9cebc198f414833366037f680fc3c","observation_id":"740a45d7-fa27-4226-b724-3e5037942898","resolution":{"observed_at":"2026-08-09T18:10:52.986333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10165","last_updated":"2025-02-24T08:42:25Z","snapshot_observed_at":"2026-08-16T13:09:45.838435Z","submitted_at":"2024-10-14T05:18:02Z","title":"HSR-Enhanced Sparse Attention Acceleration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10165","snapshot_observed_at":"2026-08-09T18:10:52.995341Z","title":"Hsr-enhanced sparse attention acceleration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.995341Z"},"links":{"cited_paper":"/paper/2410.10165","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:dcc109fa9df4f689085e6b483934196a5ca5541b0fcc538315b61f07987fd139","observation_id":"6bb98ee1-d26e-4508-9481-e915e52aaafd","resolution":{"observed_at":"2026-08-09T18:10:52.995341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02311","last_updated":"2022-10-05T06:02:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-05T16:11:45Z","title":"PaLM: Scaling Language Modeling with Pathways","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02311","snapshot_observed_at":"2026-08-09T18:10:52.999696Z","title":"Palm: Scaling language modeling with pathways","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.999696Z"},"links":{"cited_paper":"/paper/2204.02311","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:45721e592a288f5c7b580d9a9053db8e9ce30d81c15b3d5c62d9c667fff9b617","observation_id":"5dcdfac2-9044-4406-ab0a-da765aed72dd","resolution":{"observed_at":"2026-08-09T18:10:52.999696Z","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":"2026-08-09T18:10:54.728834Z","title":"BERT: Pre- training of deep bidirectional transformers for language understanding","venue":null,"work_id":"26739ef2-853e-4d0f-988c-3bc467f83747","year":2019},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.004432Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:5281362abf21a1595ce83664bb1a1af3611b65b4930ab46c496dfe5a23fa46bb","observation_id":"54ea150b-7a9d-4f39-8df3-8f5eced66676","resolution":{"observed_at":"2026-08-09T18:10:54.733339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05715","last_updated":"2022-12-29T00:02:50Z","snapshot_observed_at":"2026-08-16T17:28:13.862171Z","submitted_at":"2022-01-14T23:56:19Z","title":"Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs","version":2},"cited_work":{"arxiv_id":"2201.05715","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.05715","snapshot_observed_at":"2026-08-09T18:10:54.241405Z","title":"Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs","venue":"cs.LG","work_id":"aa7ecf81-e918-4442-afe1-ef4f8ca285ba","year":2022},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.013527Z"},"links":{"cited_paper":"/paper/2201.05715","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:dc75ae68cf9eaa0c6dcc1875c4c19e2258121ce9b9aa294e8ff0dc0628a77fd3","observation_id":"93dda562-b80f-4817-8585-f1ff822c44bf","resolution":{"observed_at":"2026-08-09T18:10:54.246027Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08361","last_updated":"2022-10-15T19:20:17Z","snapshot_observed_at":"2026-08-16T16:23:52.559855Z","submitted_at":"2022-10-15T19:20:17Z","title":"A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.08361","snapshot_observed_at":"2026-08-09T18:10:53.018121Z","title":"A nearly optimal size coreset algorithm with nearly linear time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.018121Z"},"links":{"cited_paper":"/paper/2210.08361","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:5c83a35f00cc1d44bebd07ddf8667b28c7a0f3a754418a411b74840133b78e38","observation_id":"70de0130-5f35-4f9f-92aa-82ecb8c709d6","resolution":{"observed_at":"2026-08-09T18:10:53.018121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17161","last_updated":"2023-10-27T12:37:54Z","snapshot_observed_at":"2026-08-16T15:29:12.586578Z","submitted_at":"2023-05-26T18:00:01Z","title":"Flow Matching for Scalable Simulation-Based Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17161","snapshot_observed_at":"2026-08-09T18:10:53.023097Z","title":"Flow matching for scalable simulation-based infer- ence","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.023097Z"},"links":{"cited_paper":"/paper/2305.17161","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d1d47a04f4c7eabc165aeabda36b518f5a9bc5213ec7c18b4576063881731c2e","observation_id":"5454d6c3-f6d8-484b-bf30-e1fe2bc9413e","resolution":{"observed_at":"2026-08-09T18:10:53.023097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10313","last_updated":"2024-11-24T18:44:27Z","snapshot_observed_at":"2026-08-16T13:52:04.310486Z","submitted_at":"2024-05-16T17:59:02Z","title":"How Far Are We From AGI: Are LLMs All We Need?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10313","snapshot_observed_at":"2026-08-09T18:10:53.027667Z","title":"How far are we from agi","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.027667Z"},"links":{"cited_paper":"/paper/2405.10313","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:10f63f8cc3bd5b2aa28530a24f331bc24874e99f8ea62ebb838d3f3ac4671428","observation_id":"f9d84a16-42e1-4594-9e4b-de03e294fb8b","resolution":{"observed_at":"2026-08-09T18:10:53.027667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20879","last_updated":"2024-10-10T22:43:10Z","snapshot_observed_at":"2026-08-16T15:54:23.753977Z","submitted_at":"2024-05-31T14:54:51Z","title":"Flow matching achieves almost minimax optimal convergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20879","snapshot_observed_at":"2026-08-09T18:10:53.032405Z","title":"Flow matching achieves minimax optimal convergence","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.032405Z"},"links":{"cited_paper":"/paper/2405.20879","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:c37d4f02747b35d74295dd60cbb041e54d881735b1802009109fb1866af4c4a8","observation_id":"f975aa1e-2fc7-432b-b479-f6d5de375a0a","resolution":{"observed_at":"2026-08-09T18:10:53.032405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01367","last_updated":"2018-10-22T17:56:45Z","snapshot_observed_at":"2026-08-14T18:20:27.887044Z","submitted_at":"2018-10-02T16:56:37Z","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01367","snapshot_observed_at":"2026-08-09T18:10:53.037119Z","title":"Ffjord: Free-form continuous dynamics for scalable reversible generative models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.037119Z"},"links":{"cited_paper":"/paper/1810.01367","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:652726854a17e1302e152e5cf1fe6f9d758298aefeb1dcf959383a5abb8d5712","observation_id":"c9b0672f-8d32-4daa-a928-889bddea2141","resolution":{"observed_at":"2026-08-09T18:10:53.037119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16504","last_updated":"2023-03-29T07:29:07Z","snapshot_observed_at":"2026-08-16T15:44:28.871755Z","submitted_at":"2023-03-29T07:29:07Z","title":"An Over-parameterized Exponential Regression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.16504","snapshot_observed_at":"2026-08-09T18:10:53.046220Z","title":"An over-parameterized exponential regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.046220Z"},"links":{"cited_paper":"/paper/2303.16504","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:9abb5707bd1afaf8337a7f34d871405125e2e7e86e6736b5403773812d0f1c83","observation_id":"5d49fe6f-c0b7-44c6-b924-cc0538447dc9","resolution":{"observed_at":"2026-08-09T18:10:53.046220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.10502","last_updated":"2023-08-21T06:42:42Z","snapshot_observed_at":"2026-08-16T15:07:05.835485Z","submitted_at":"2023-08-21T06:42:42Z","title":"GradientCoin: A Peer-to-Peer Decentralized Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10502","snapshot_observed_at":"2026-08-09T18:10:53.054981Z","title":"Gradientcoin: A peer-to-peer decentralized large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.054981Z"},"links":{"cited_paper":"/paper/2308.10502","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:aac7f5d2e8f4bdc19762befb8b389eebd2930e71691ff91a5b7c27f431a4d207","observation_id":"f13f7b0b-9201-41b4-ad34-b47e7d401213","resolution":{"observed_at":"2026-08-09T18:10:53.054981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00660","last_updated":"2025-02-17T05:12:27Z","snapshot_observed_at":"2026-08-16T15:36:36.165904Z","submitted_at":"2023-05-01T05:16:07Z","title":"An Iterative Algorithm for Rescaled Hyperbolic Functions Regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.00660","snapshot_observed_at":"2026-08-09T18:10:53.059744Z","title":"An iterative algorithm for rescaled hyperbolic functions regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.059744Z"},"links":{"cited_paper":"/paper/2305.00660","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:dc86d4f03420aabc3cb7247bb32e2cf49046065204417dada8fed2ec9fe5b4e0","observation_id":"6c2d43a2-fb5e-4afe-8332-8b0d5823dd2c","resolution":{"observed_at":"2026-08-09T18:10:53.059744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03297","last_updated":"2024-11-27T15:10:12Z","snapshot_observed_at":"2026-08-16T13:37:18.192698Z","submitted_at":"2024-07-03T17:34:55Z","title":"Improved Noise Schedule for Diffusion Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.03297","snapshot_observed_at":"2026-08-09T18:10:53.065154Z","title":"Improved noise schedule for diffusion training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.065154Z"},"links":{"cited_paper":"/paper/2407.03297","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:545414e098b2cf7d785d4756a61df3162c595cf0772cd06ec31b838fa601c85d","observation_id":"b2c73810-c689-4461-9678-8eb147a6c4a4","resolution":{"observed_at":"2026-08-09T18:10:53.065154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16525","last_updated":"2025-06-05T23:04:39Z","snapshot_observed_at":"2026-08-13T12:09:17.275284Z","submitted_at":"2024-11-25T16:12:17Z","title":"Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16525","snapshot_observed_at":"2026-08-09T18:10:53.069659Z","title":"Fundamental limits of prompt tuning transformers: Universality, capacity and efficiency","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.069659Z"},"links":{"cited_paper":"/paper/2411.16525","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:012ecd5d10e1662e9a973738b9a2d202bd23823bda1a95a490065e5a47c6147f","observation_id":"2711a5c6-4232-449a-b24c-059d00dcfd1e","resolution":{"observed_at":"2026-08-09T18:10:53.069659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17522","last_updated":"2024-11-26T15:30:48Z","snapshot_observed_at":"2026-08-13T12:09:47.551763Z","submitted_at":"2024-11-26T15:30:48Z","title":"On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17522","snapshot_observed_at":"2026-08-09T18:10:53.074307Z","title":"On statistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.074307Z"},"links":{"cited_paper":"/paper/2411.17522","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:13db5c801389b37158a4f669b66e71482880c9dd9232117e06185dd65f7b6d81","observation_id":"9af00e30-069c-40a9-9c35-0261aed91a5e","resolution":{"observed_at":"2026-08-09T18:10:53.074307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01079","last_updated":"2024-10-31T16:59:13Z","snapshot_observed_at":"2026-08-16T13:38:12.922498Z","submitted_at":"2024-07-01T08:34:40Z","title":"On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01079","snapshot_observed_at":"2026-08-09T18:10:53.079075Z","title":"On statistical rates and provably efficient criteria of latent diffusion transformers (dits)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.079075Z"},"links":{"cited_paper":"/paper/2407.01079","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:6752f0535c26f97c2c7b445e4946b2a2650208eabc0641cd8e08c44bec11e03d","observation_id":"9f05d5d9-c265-4244-8d32-b6cd10fab35c","resolution":{"observed_at":"2026-08-09T18:10:53.079075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11279","last_updated":"2024-10-15T04:57:02Z","snapshot_observed_at":"2026-08-16T13:09:19.812609Z","submitted_at":"2024-10-15T04:57:02Z","title":"Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.11279","snapshot_observed_at":"2026-08-09T18:10:53.083672Z","title":"Advancing the understanding of fixed point iterations in deep neural networks: A detailed analytical study","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.083672Z"},"links":{"cited_paper":"/paper/2410.11279","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:41e8c8958cf948f2fd0fff3fc2d56f3ebca3304d7a46765add4e9ac5e06d7427","observation_id":"91020246-b202-4ad5-9386-d298df3d377b","resolution":{"observed_at":"2026-08-09T18:10:53.083672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04377","last_updated":"2025-02-02T23:48:36Z","snapshot_observed_at":"2026-08-13T12:10:23.400300Z","submitted_at":"2025-01-08T09:34:15Z","title":"On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.04377","snapshot_observed_at":"2026-08-09T18:10:53.088435Z","title":"On computational limits and provably efficient criteria of visual autoregressive models: A fine-grained complexity analysis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.088435Z"},"links":{"cited_paper":"/paper/2501.04377","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d42bd7ff7cb355f998cde00916b838257ee71ea5165bace5f60220bfe8eac975","observation_id":"aa912837-7d5e-44c9-a4ad-189f99102a26","resolution":{"observed_at":"2026-08-09T18:10:53.088435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04299","last_updated":"2025-01-08T06:07:33Z","snapshot_observed_at":"2026-08-13T12:09:44.937417Z","submitted_at":"2025-01-08T06:07:33Z","title":"Circuit Complexity Bounds for Visual Autoregressive Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.04299","snapshot_observed_at":"2026-08-09T18:10:53.093678Z","title":"Circuit complexity bounds for visual autoregressive model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.093678Z"},"links":{"cited_paper":"/paper/2501.04299","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:681800708fdf7b296501b59d6e9cf0c7232bab0c4e6c75f15be0e8f8f809ac8d","observation_id":"df3a235e-fd85-4c05-b0a5-cd95c1c89113","resolution":{"observed_at":"2026-08-09T18:10:53.093678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-16T02:30:42.660030Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-09T18:10:53.103238Z","title":"Flow matching for generative modeling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.103238Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:34f0b6697abbc639fe7ec5e0bc8d027f4f0c3bb7f5615998837767d6b406b2be","observation_id":"aba94119-76b4-4db7-b5eb-3e364891764e","resolution":{"observed_at":"2026-08-09T18:10:53.103238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-09T18:10:53.112812Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.112812Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:77193329b5363ab10089771008c22e454c7764c82b3146bbf88927b2d2b1eff2","observation_id":"2f0a2627-89fe-4d3a-b0c7-be70ffd950f2","resolution":{"observed_at":"2026-08-09T18:10:53.112812Z","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":"2410.09397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:10:53.929649Z","title":"Fine-grained at- tention i/o complexity: Comprehensive analysis for backward passes","venue":null,"work_id":"28b76807-979c-4a25-aa2d-633e579818d2","year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.117938Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d100d294c1c7382950a73af9ec1c7c7de20c7486652d6b89575b719c309b5a26","observation_id":"c64bf636-e496-4847-b28e-d2f40cf20fa1","resolution":{"observed_at":"2026-08-09T18:10:53.938034Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06444","last_updated":"2025-01-11T05:54:10Z","snapshot_observed_at":"2026-08-16T12:59:12.829800Z","submitted_at":"2025-01-11T05:54:10Z","title":"On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06444","snapshot_observed_at":"2026-08-09T18:10:53.122340Z","title":"On the computational capability of graph neural networks: A circuit complexity bound perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.122340Z"},"links":{"cited_paper":"/paper/2501.06444","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:c89b5e379392a31819d77969f5b87fb1df883d50ecd34812e76cd6268f26e0a4","observation_id":"1605ebe5-d8cc-44af-9dc0-e709c90d5226","resolution":{"observed_at":"2026-08-09T18:10:53.122340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12151","last_updated":"2024-10-18T03:36:03Z","snapshot_observed_at":"2026-08-16T13:24:42.416203Z","submitted_at":"2024-08-22T06:40:32Z","title":"A Tighter Complexity Analysis of SparseGPT","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12151","snapshot_observed_at":"2026-08-09T18:10:53.126661Z","title":"A tighter complexity analysis of sparsegpt","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.126661Z"},"links":{"cited_paper":"/paper/2408.12151","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:4c87fbb381a9c58bfb33896b453758bc290803c36fedcf52881cd330aad9b0a2","observation_id":"6488a6c2-b58a-4d47-8e2a-48a279687712","resolution":{"observed_at":"2026-08-09T18:10:53.126661Z","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":"2026-08-09T18:10:54.698301Z","title":"Fast second-order method for neural network under small treewidth setting","venue":null,"work_id":"1fd59826-efdd-4c13-a9cb-d894d1a1fac4","year":2024},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.130667Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:57f00292f2f365b30b717474e4d95f9c88f2f459efb419aa130202bf8214eca3","observation_id":"e0b3202c-653d-4ba1-9a46-7b79b072c294","resolution":{"observed_at":"2026-08-09T18:10:54.704164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09375","last_updated":"2025-02-20T10:24:53Z","snapshot_observed_at":"2026-08-16T13:10:07.189771Z","submitted_at":"2024-10-12T05:54:17Z","title":"Looped ReLU MLPs May Be All You Need as Practical Programmable Computers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09375","snapshot_observed_at":"2026-08-09T18:10:53.135095Z","title":"Looped relu mlps may be all you need as practical programmable computers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.135095Z"},"links":{"cited_paper":"/paper/2410.09375","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:afe295749ecec8818a79b0551cc9858fd8942d0090f8df2d50ac9373c4972219","observation_id":"c4185a59-c14c-47f1-ac1a-796dd1a041dd","resolution":{"observed_at":"2026-08-09T18:10:53.135095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13233","last_updated":"2024-10-15T04:11:19Z","snapshot_observed_at":"2026-08-16T13:24:16.127677Z","submitted_at":"2024-08-23T17:16:43Z","title":"Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13233","snapshot_observed_at":"2026-08-09T18:10:53.140406Z","title":"Multi-layer transformers gradient can be approximated in almost linear time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.140406Z"},"links":{"cited_paper":"/paper/2408.13233","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:36855dd7b40dae97d930d8987e10545169fb62c348d85e600ab3e1db097c7ad9","observation_id":"45c05b6c-2f9c-4757-a6eb-baa9b8f1fa92","resolution":{"observed_at":"2026-08-09T18:10:53.140406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13621","last_updated":"2024-11-02T05:30:20Z","snapshot_observed_at":"2026-08-18T05:29:22.703558Z","submitted_at":"2024-07-18T15:57:55Z","title":"Differential Privacy Mechanisms in Neural Tangent Kernel Regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13621","snapshot_observed_at":"2026-08-09T18:10:53.144828Z","title":"Differential privacy mech- anisms in neural tangent kernel regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.144828Z"},"links":{"cited_paper":"/paper/2407.13621","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:a6d95bf0f86c15c56e167e50568da619752e291d3498bdb38e5c035c50c7e226","observation_id":"231dd84d-b200-43b0-97a1-ab45170db7ee","resolution":{"observed_at":"2026-08-09T18:10:53.144828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16418","last_updated":"2024-10-14T03:59:47Z","snapshot_observed_at":"2026-08-16T13:49:23.593692Z","submitted_at":"2024-05-26T03:32:27Z","title":"Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16418","snapshot_observed_at":"2026-08-09T18:10:53.149284Z","title":"Unraveling the smooth- ness properties of diffusion models: A gaussian mixture perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.149284Z"},"links":{"cited_paper":"/paper/2405.16418","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:626bcba05471b2e700eb574174eb07a358842699a3ed20f0296b57475addf0d5","observation_id":"f660f7e7-b0a1-4579-ba86-858c682b41af","resolution":{"observed_at":"2026-08-09T18:10:53.149284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14018","last_updated":"2024-08-26T05:06:39Z","snapshot_observed_at":"2026-08-18T04:38:42.988561Z","submitted_at":"2024-08-26T05:06:39Z","title":"Quantum Speedups for Approximating the John Ellipsoid","version":1},"cited_work":{"arxiv_id":"2408.14018","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.14018","snapshot_observed_at":"2026-08-09T18:10:53.522340Z","title":"Quantum Speedups for Approximating the John Ellipsoid","venue":"cs.DS","work_id":"bd78dfe7-721f-46f5-8546-a41c57c21856","year":2024},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.153731Z"},"links":{"cited_paper":"/paper/2408.14018","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:7372c4844afd5aaaa18ea7c0f5487daa044f7a691c5bf531caca8c548fadcab5","observation_id":"c29ee117-9dae-4337-b787-28359cb0a1b5","resolution":{"observed_at":"2026-08-09T18:10:53.527058Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15579","last_updated":"2025-08-27T08:40:47Z","snapshot_observed_at":"2026-08-16T21:59:54.275896Z","submitted_at":"2024-12-20T05:23:45Z","title":"Score-based Generative Diffusion Models for Social Recommendations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15579","snapshot_observed_at":"2026-08-09T18:10:53.158232Z","title":"Score-based gener- ative diffusion models for social recommendations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.158232Z"},"links":{"cited_paper":"/paper/2412.15579","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d427188302ad0db4b0675ff40639a0f13a1855de984ee536c62b416cda5af404","observation_id":"379cf744-4675-492a-bce7-16932306c21a","resolution":{"observed_at":"2026-08-09T18:10:53.158232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.00114","last_updated":"2021-11-30T21:32:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-11-30T21:32:46Z","title":"Show Your Work: Scratchpads for Intermediate Computation with Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.00114","snapshot_observed_at":"2026-08-09T18:10:53.162588Z","title":"Show your work: Scratchpads for intermediate computation with language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.162588Z"},"links":{"cited_paper":"/paper/2112.00114","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:cd5e22fd73344b28e7b90d311ed529e69d9c64f3e511526f96d6a1fe8ecb93eb","observation_id":"c35e8525-8903-4c7e-986f-d43d99e98599","resolution":{"observed_at":"2026-08-09T18:10:53.162588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-09T18:10:53.167020Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.167020Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:2ca206e71850a8ac93c3b11837e5428ff66e718df4360417b60913c7e1d6406c","observation_id":"daff3232-7e78-4790-8242-af4e83dc2848","resolution":{"observed_at":"2026-08-09T18:10:53.167020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14772","last_updated":"2023-05-24T18:17:17Z","snapshot_observed_at":"2026-08-16T15:37:00.716328Z","submitted_at":"2023-04-28T11:33:08Z","title":"Multisample Flow Matching: Straightening Flows with Minibatch Couplings","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.14772","snapshot_observed_at":"2026-08-09T18:10:53.171729Z","title":"Multisample flow matching: Straight- ening flows with minibatch couplings","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.171729Z"},"links":{"cited_paper":"/paper/2304.14772","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:64a46e06211d44983fdcf27a427d461358060b3d61762454da823a2d2151b954","observation_id":"c432bab2-776b-4655-9fd2-b8e88a929928","resolution":{"observed_at":"2026-08-09T18:10:53.171729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07452","last_updated":"2023-09-14T06:24:33Z","snapshot_observed_at":"2026-08-17T22:31:04.861681Z","submitted_at":"2023-09-14T06:24:33Z","title":"Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?","version":1},"cited_work":{"arxiv_id":"2309.07452","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.07452","snapshot_observed_at":"2026-08-09T18:10:53.446663Z","title":"Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?","venue":"cs.LG","work_id":"a8988dde-2a8f-45b3-8163-4f36b35be76c","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.177424Z"},"links":{"cited_paper":"/paper/2309.07452","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:4a2a43a1f0418cfd6f964b971b741a8d11ff16d07db4dd15863a6e4ec7929785","observation_id":"352a31af-7092-41ea-acf0-61db3848f2b3","resolution":{"observed_at":"2026-08-09T18:10:53.451584Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"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":"2026-08-09T18:10:53.182448Z","title":"Denoising diffusion implicit mod- els","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.182448Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:81a57305d5e12e0897787af32d5069d414a5b778b6ac1c5f205903cd3d66cac0","observation_id":"c80f1561-9502-4547-843e-92bf7c1be88b","resolution":{"observed_at":"2026-08-09T18:10:53.182448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.06210","last_updated":"2023-03-10T21:18:34Z","snapshot_observed_at":"2026-08-16T15:49:07.090582Z","submitted_at":"2023-03-10T21:18:34Z","title":"A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph","version":1},"cited_work":{"arxiv_id":"2303.06210","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.06210","snapshot_observed_at":"2026-08-09T18:10:53.411866Z","title":"A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph","venue":"cs.LG","work_id":"f8af7584-2a1f-484e-a3b4-5fb111ae1987","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.187135Z"},"links":{"cited_paper":"/paper/2303.06210","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:8bc3c3ba54725721d495c44f6a37700d41343ac2fbd985ecc9cd33645a2ce5dc","observation_id":"03a95559-2b39-45c4-a563-e0aa47942ed0","resolution":{"observed_at":"2026-08-09T18:10:53.416965Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12444","last_updated":"2025-03-21T15:52:39Z","snapshot_observed_at":"2026-08-16T12:25:35.685966Z","submitted_at":"2024-12-17T01:12:35Z","title":"LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12444","snapshot_observed_at":"2026-08-09T18:10:53.196082Z","title":"Lazydit: Lazy learning for the acceleration of diffusion transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.196082Z"},"links":{"cited_paper":"/paper/2412.12444","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:282ea23dffd2fb30e3e4a23d012f1712dbc0ab7768121d3313448d0a7d07c9c5","observation_id":"0545fbc4-67d9-4dcd-8593-69bae2b3c86e","resolution":{"observed_at":"2026-08-09T18:10:53.196082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12441","last_updated":"2024-12-17T01:09:23Z","snapshot_observed_at":"2026-08-16T13:20:46.681231Z","submitted_at":"2024-12-17T01:09:23Z","title":"Numerical Pruning for Efficient Autoregressive Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12441","snapshot_observed_at":"2026-08-09T18:10:53.203470Z","title":"Numerical pruning for efficient autoregressive models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.203470Z"},"links":{"cited_paper":"/paper/2412.12441","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:8aa0df2737c7babf7db555fe453402fe0b24e3891693324c51b96ceafab91519","observation_id":"788d3fa3-001e-4dba-b718-7b1c1ed03a10","resolution":{"observed_at":"2026-08-09T18:10:53.203470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.13482","last_updated":"2023-09-23T21:41:01Z","snapshot_observed_at":"2026-08-16T14:58:10.557697Z","submitted_at":"2023-09-23T21:41:01Z","title":"A Unified Scheme of ResNet and Softmax","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.13482","snapshot_observed_at":"2026-08-09T18:10:53.207540Z","title":"A unified scheme of resnet and softmax","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.207540Z"},"links":{"cited_paper":"/paper/2309.13482","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:6fbb05d66c9db027d35573c0cd970f40c5b5bb0c64db0849c26e3ca889b91305","observation_id":"4bf0e8a4-07c3-4271-a0fe-6187a0a5dfe7","resolution":{"observed_at":"2026-08-09T18:10:53.207540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-09T18:10:53.211376Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.211376Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:cb922ac57eb7d6ca54c0e2f374ae54ecec75ddc6cfff370e15b65864cb796e58","observation_id":"2d7b70b0-7026-4ae3-bdb7-3aa533bf9c7c","resolution":{"observed_at":"2026-08-09T18:10:53.211376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.16305","last_updated":"2023-10-25T02:26:04Z","snapshot_observed_at":"2026-08-16T14:49:07.684207Z","submitted_at":"2023-10-25T02:26:04Z","title":"Dolfin: Diffusion Layout Transformers without Autoencoder","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.16305","snapshot_observed_at":"2026-08-09T18:10:53.219758Z","title":"Dolfin: Diffusion layout transformers without autoencoder","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.219758Z"},"links":{"cited_paper":"/paper/2310.16305","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:359168edca650040a60c4354a0aef222478efd27dce049785ed40b593022d6df","observation_id":"edd26f6b-04a2-4bcd-917b-86d90ebaa421","resolution":{"observed_at":"2026-08-09T18:10:53.219758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05699","last_updated":"2024-06-09T08:51:50Z","snapshot_observed_at":"2026-08-16T13:44:49.233954Z","submitted_at":"2024-06-09T08:51:50Z","title":"An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05699","snapshot_observed_at":"2026-08-09T18:10:53.224329Z","title":"An investigation of noise robustness for flow-matching-based zero-shot tts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.224329Z"},"links":{"cited_paper":"/paper/2406.05699","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:db906e84ee04abb2859273f2b9ef85f4db393f0f42f4cbd5c58e085168eedeb1","observation_id":"25cf9e42-d40c-4181-b1e1-daab68565502","resolution":{"observed_at":"2026-08-09T18:10:53.224329Z","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":"2026-08-09T18:10:54.667109Z","title":"Symbol tun- ing improves in-context learning in language models","venue":null,"work_id":"805c142c-2fdb-4fab-92e6-94276c81ce96","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.228879Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d525d2f885e4aca2277def4bd94aa48a4bf8382aa469cbd571785c0cf6e36cc6","observation_id":"09ca0546-2698-44cf-adf1-0e3ab8c7b1ac","resolution":{"observed_at":"2026-08-09T18:10:54.672713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03626","last_updated":"2024-06-23T23:50:59Z","snapshot_observed_at":"2026-08-16T14:37:05.826570Z","submitted_at":"2023-12-06T17:13:15Z","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","version":2},"cited_work":{"arxiv_id":"2312.03626","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.03626","snapshot_observed_at":"2026-08-09T18:10:53.305776Z","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","venue":"cs.CV","work_id":"926404b6-6ad0-4e05-afb6-c4bb50f0a5bd","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.233876Z"},"links":{"cited_paper":"/paper/2312.03626","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:228f62ae485c58bb4ecdb3ecff6e2eaaecb5e2d656c5b18dfdfc1d8cc21e9ae6","observation_id":"756aca09-de10-43ab-ba78-3159028a399c","resolution":{"observed_at":"2026-08-09T18:10:53.311838Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:10:54.652935Z","title":"Improving founda- tion models for few-shot learning via multitask finetuning","venue":null,"work_id":"f4c00a9f-7fe7-40c7-b420-d2665340ef2b","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.238525Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:af3d8d0a926cfb430de89f0e408deb3b79a9891d526c6d23782d017851eb7bf8","observation_id":"2aec2ab8-e63a-4fa6-8e5f-4842330f2ec2","resolution":{"observed_at":"2026-08-09T18:10:54.657039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16199","last_updated":"2024-09-18T23:54:36Z","snapshot_observed_at":"2026-08-13T10:46:50.834601Z","submitted_at":"2023-03-28T17:59:12Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.16199","snapshot_observed_at":"2026-08-09T18:10:53.243123Z","title":"Llama-adapter: Efficient fine-tuning of language models with zero-init attention","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.243123Z"},"links":{"cited_paper":"/paper/2303.16199","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:d1a8125d44eb50eef84796aea8f419e48e8a9eb5cb7a27f63c2a8f1901383137","observation_id":"3b39557c-6310-4825-aea7-55a736b91d84","resolution":{"observed_at":"2026-08-09T18:10:53.243123Z","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":"2026-08-09T18:10:54.640058Z","title":null,"venue":null,"work_id":"7a459c84-5fbb-485d-ad03-7de551908fcd","year":2000},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.247538Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:cd5d6bc8e19759247806bcee51ecac72fec4e9f67c767a5f6121c55ab365674a","observation_id":"9de7ec62-a42e-4f29-b880-771fc62bb7b5","resolution":{"observed_at":"2026-08-09T18:10:54.644010Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:10:54.625982Z","title":null,"venue":null,"work_id":"0f75d01c-a14f-4775-bd1a-1baa691ad42e","year":2000},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.252922Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:190ea2e1e35beec41a52fc92e8ec14be50b9ed6ae966fb6f1aff42e187043ffa","observation_id":"ae611811-2162-4851-b020-d95d3700d699","resolution":{"observed_at":"2026-08-09T18:10:54.630401Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:10:54.714773Z","title":"The power of scale for parameter- efficient prompt tuning","venue":null,"work_id":"e9de4e75-05a3-4c5b-9870-7c6070c562e6","year":2021},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":1992,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.098390Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:138ec86bac9bb656bcf50b2b30fb58784a1fc5a8d9aff3f92983e25c44b03d12","observation_id":"02e41a7a-4510-4234-bd15-13dc8a6cc742","resolution":{"observed_at":"2026-08-09T18:10:54.719410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10972","last_updated":"2023-05-21T07:07:55Z","snapshot_observed_at":"2026-08-17T13:04:22.753084Z","submitted_at":"2023-01-26T07:37:22Z","title":"On the Importance of Noise Scheduling for Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10972","snapshot_observed_at":"2026-08-09T18:10:52.961488Z","title":"On the importance of noise scheduling for diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":1994,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.961488Z"},"links":{"cited_paper":"/paper/2301.10972","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:29d8f3ad013061a94a526db90c7a99cba8de41eba08f63e896b62d05c4b48c3f","observation_id":"e859a5d1-ce54-47da-96af-7ad55d02d452","resolution":{"observed_at":"2026-08-09T18:10:52.961488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07418","last_updated":"2023-09-14T04:23:40Z","snapshot_observed_at":"2026-08-17T01:32:27.930022Z","submitted_at":"2023-09-14T04:23:40Z","title":"A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07418","snapshot_observed_at":"2026-08-09T18:10:53.050576Z","title":"A fast optimization view: Reformulating single layer attention in llm based on tensor and svm trick, and solving it in matrix multiplication time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.050576Z"},"links":{"cited_paper":"/paper/2309.07418","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:edd4647f594c102dc455e5705e3a75aebccc12b8dc8cdbda321d998f65b507a8","observation_id":"9fc34ebd-3520-4c75-9248-204fe52be610","resolution":{"observed_at":"2026-08-09T18:10:53.050576Z","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":"2026-08-09T18:10:54.682668Z","title":"Modeling the trade-off of privacy preservation and activity recognition on low-resolution images","venue":null,"work_id":"dc4465fb-fc80-48a9-ba57-33f71043462a","year":2023},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.215132Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:019a72c8cc847dbab8a6bed4d698eba11871752a191b6a601d2f896d90379c42","observation_id":"025b564e-064d-4787-a829-b61c1ba3903c","resolution":{"observed_at":"2026-08-09T18:10:54.687266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.04397","last_updated":"2023-04-10T05:52:38Z","snapshot_observed_at":"2026-08-16T15:41:40.150021Z","submitted_at":"2023-04-10T05:52:38Z","title":"Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.04397","snapshot_observed_at":"2026-08-09T18:10:53.008960Z","title":"Randomized and deterministic attention sparsification algorithms for over-parameterized feature dimension","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.008960Z"},"links":{"cited_paper":"/paper/2304.04397","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:b39a02564cbc5f67524d0ff2bcbfc7a9fba442e005d7e5e0001074d057601e8d","observation_id":"a864b27a-be2f-45f5-9602-736bd5c3503b","resolution":{"observed_at":"2026-08-09T18:10:53.008960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.15010","last_updated":"2023-04-28T17:59:25Z","snapshot_observed_at":"2026-08-12T23:40:42.885633Z","submitted_at":"2023-04-28T17:59:25Z","title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.15010","snapshot_observed_at":"2026-08-09T18:10:53.041680Z","title":"Llama-adapter v2: Parameter- efficient visual instruction model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:53.041680Z"},"links":{"cited_paper":"/paper/2304.15010","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:026c4c4eeaabc904e30a82efeeaeb8c0f9a33530447b72fa6f36d6c95ed02127","observation_id":"a72b2817-6108-4683-96e2-52a4543b019c","resolution":{"observed_at":"2026-08-09T18:10:53.041680Z","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":"2026-08-09T18:10:52.971123Z","title":"Fast gradient computation for rope attention in almost linear time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.971123Z"},"links":{"citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:ab99ff2bbf11dd6a995a7e7bebbb86163e19bd09d529976a01c8c3858e907ca7","observation_id":"9a98c84f-5748-4c35-80c8-1b32bd89c8f2","resolution":{"observed_at":"2026-08-09T18:10:52.971123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-09T18:10:52.951616Z","title":"On the opportunities and risks of foundation models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.951616Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:4826965241ac90311a3a158c22dcbd28d719c102f9ab6aa0a442e7a8734fbe3a","observation_id":"3fd53dbb-23cf-4f90-9ea2-428bc8ce0678","resolution":{"observed_at":"2026-08-09T18:10:52.951616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.07602","last_updated":"2024-12-01T06:39:41Z","snapshot_observed_at":"2026-08-16T13:00:54.576654Z","submitted_at":"2024-11-12T07:24:41Z","title":"Circuit Complexity Bounds for RoPE-based Transformer Architecture","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.07602","snapshot_observed_at":"2026-08-09T18:10:52.976926Z","title":"Circuit complexity bounds for rope-based transformer architecture","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.976926Z"},"links":{"cited_paper":"/paper/2411.07602","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:c1036dc562f16d277a533be3ff38c9e7600b442b52dc9df3ade49f0377973b9c","observation_id":"d50ca951-3574-4f63-9b86-0bb08052c43e","resolution":{"observed_at":"2026-08-09T18:10:52.976926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17107","last_updated":"2025-03-05T07:29:42Z","snapshot_observed_at":"2026-08-15T02:20:16.458743Z","submitted_at":"2024-12-22T17:39:32Z","title":"Grams: Gradient Descent with Adaptive Momentum Scaling","version":3},"cited_work":{"arxiv_id":"2412.17107","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.17107","snapshot_observed_at":"2026-08-09T18:10:54.309001Z","title":"Grams: Gradient Descent with Adaptive Momentum Scaling","venue":"cs.LG","work_id":"c9b4842a-de81-43eb-b7ea-9344fc86cdf1","year":2024},"citing_paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-09T18:10:52.990784Z"},"links":{"cited_paper":"/paper/2412.17107","citing_paper":"/paper/2502.00688"},"observation_digest":"sha256:ac87272a285f9ca695279585718936e712cb3f0ed709f6af470f7b7b893a129e","observation_id":"32a24b45-d249-44d7-9b00-a45a07b65086","resolution":{"observed_at":"2026-08-09T18:10:54.314524Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.00688","last_updated":"2025-02-02T06:19:59Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T13:03:01.988904Z","submitted_at":"2025-02-02T06:19:59Z","title":"High-Order Matching for One-Step Shortcut Diffusion Models"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":50,"verified_exact":7,"verified_fuzzy":6},"total_outbound_references":64},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 5 inbound Pith citation observations for arXiv:2502.00688."}