{"as_of":"2026-08-06T16:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:24ef6e2bf723326a1c6ed4272c2aa5a08b2dfe96e668360e67eb94952d9a551a","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T11:40:41.762364Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T17:14:17.683649Z","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":"2026-08-05T17:14:19.073912Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"cited_work":{"arxiv_id":"2511.02043","doi":null,"metadata_source":"pith","pith_arxiv_id":"2511.02043","snapshot_observed_at":"2026-08-05T17:14:19.073912Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","venue":"cs.LG","work_id":"a29f5a56-8d08-4db5-837a-06389beec8dd","year":2025},"citing_paper":{"arxiv_id":"2608.03537","last_updated":"2026-08-04T12:15:28Z","snapshot_observed_at":"2026-08-06T15:37:03.596766Z","submitted_at":"2026-08-04T12:15:28Z","title":"ComFuse: Fusing Complex Memory-Intensive Subgraphs with Compute-Intensive Kernels For Modern GPU Architectures","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T17:14:17.683649Z"},"links":{"cited_paper":"/paper/2511.02043","citing_paper":"/paper/2608.03537"},"observation_digest":"sha256:325c1e9b13ba5a8fab0d1d96e55ac85f7b7f92e8fa4944a885aef82c8033e218","observation_id":"2129f888-f11e-474b-b1e0-4ac00c7a7479","resolution":{"observed_at":"2026-08-05T17:14:19.222523Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2511.02043/citation-record","integrity":"/paper/2511.02043/integrity","json":"/paper/2511.02043/citation-record.json","paper":"/paper/2511.02043"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T08:36:07.226215Z","title":"write newline","venue":null,"work_id":"8481976a-f196-4822-833d-e487ae5a1e81","year":null},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:2bf5889940b7b8470269b0d9a02b670361b07840c61e7969b406f5fe787c9d5c","observation_id":"3c54ad46-0aed-46b8-ac50-58fcf117c489","resolution":{"observed_at":"2026-05-22T11:41:30.503489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X","venue":null,"work_id":"09d1b796-c124-437d-a420-b5b0b0388f0b","year":2016},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:ced5f0bb044ddec5fe3a1e0d506b0ce45e66b90c0a7c69197ce1daf897ba0ef1","observation_id":"35d5c54e-2735-4f82-9516-b33ac755b533","resolution":{"observed_at":"2026-05-22T11:41:30.507002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2022.11.20.517210","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"O pen F old: R etraining A lpha F old2 yields new insights into its learning mechanisms and capacity for generalization","venue":"bioRxiv (Cold Spring Harbor Laboratory)","work_id":"400ce9a6-64a6-4d39-8f0e-3ea31c580d90","year":2022},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:4e9c21f03635ecd7e499e42ef423f8e58bc74db756a185144612086a30d86bbd","observation_id":"069154db-f17a-4773-a321-5bbe5d4b5fe7","resolution":{"observed_at":"2026-05-22T11:41:29.014457Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0665.364036","doi":"10.1145/3620665.3640360","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transforma- tion and Graph Compilation","venue":null,"work_id":"abd261ad-4aca-4ea3-8090-344914daba35","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:c5379832fccd6be7a855cfccaf7c1a284eb031b5557bcc2e5dffc82d1b6ca082","observation_id":"b8360ff1-d459-4fa4-8568-c67f3bce2dfc","resolution":{"observed_at":"2026-05-22T11:41:29.004952Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":"2004.05150","doi":"10.48550/arxiv.2004.05150","metadata_source":"pith","pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Longformer: The Long-Document Transformer","venue":"cs.CL","work_id":"abea7a44-6668-4de7-aab6-f53a6e5aa088","year":2020},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:3d78c3919cae36b4e2674f1280132f862b5cbd19fad476f28ed5519d9992e1de","observation_id":"fce0f1d4-885b-4700-b684-cd0ca8219d2b","resolution":{"observed_at":"2026-05-22T11:41:30.027162Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-12T21:49:59.161233+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T21:49:59.161233+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"\\ TVM \\ : An automated \\ End-to-End \\ optimizing compiler for deep learning","venue":null,"work_id":"f84c5c75-72e2-46d8-863a-896b88817dba","year":2018},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:e3f5e7570d64620a3fab3ec9cadb3bd45448c7f0da276d172510b4cadfb33d86","observation_id":"078617f0-fa1b-441a-abe4-8b03631dd240","resolution":{"observed_at":"2026-05-22T11:41:30.519053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":"2307.08691","doi":"10.48550/arxiv.2307.08691","metadata_source":"pith","pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","venue":"cs.LG","work_id":"fff3953b-5efb-4753-bee4-002f59995810","year":2023},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:6270da52ca1c8108b9c4b55a857a0c6a9a4721b78d647022ea1a24dbee979616","observation_id":"e7efc840-b665-4dec-ba98-be10fa428965","resolution":{"observed_at":"2026-05-22T11:41:30.062158Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:50:18.243793+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:50:18.243793+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14135","last_updated":"2022-06-23T17:53:32Z","snapshot_observed_at":"2026-07-06T13:14:48.753329Z","submitted_at":"2022-05-27T17:53:09Z","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","version":2},"cited_work":{"arxiv_id":"2205.14135","doi":"10.48550/arxiv.2205.14135","metadata_source":"pith","pith_arxiv_id":"2205.14135","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","venue":"cs.LG","work_id":"efa96825-0830-4cfc-a250-fdaf6af302ab","year":2022},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2205.14135","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:0764e506b6086adfd60a0838563ef833ebcc6db2d83a2dd9304bba1252a5d3b6","observation_id":"8ed6cfc0-780e-41c1-9b02-dc0bacafe086","resolution":{"observed_at":"2026-05-22T11:41:30.067015Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:50:18.534404+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:50:18.534404+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05496","last_updated":"2024-12-07T01:46:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-07T01:46:38Z","title":"Flex Attention: A Programming Model for Generating Optimized Attention Kernels","version":1},"cited_work":{"arxiv_id":"2412.05496","doi":"10.48550/arxiv.2412.05496","metadata_source":"pith","pith_arxiv_id":"2412.05496","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flex Attention: A Programming Model for Generating Optimized Attention Kernels","venue":"cs.LG","work_id":"692b9d44-343b-4635-a0dc-1ee8fe539aa3","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2412.05496","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:8b9d93cdb2205eaa457395031e03b47619deebd6250e813965b0a0d2bbb04861","observation_id":"e9e1915f-9d0b-4a1d-a714-1ef8b72a85d7","resolution":{"observed_at":"2026-05-22T11:41:30.072583Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-24T05:54:39.898364+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-24T05:54:39.898364+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Flexattention: The flexibility of pytorch with the performance of flashattention, Aug 2024","venue":null,"work_id":"d7e6412f-7d12-44c1-ac4d-e4a9ccecdb50","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:a4bb69f80126881b5dfbac608bf77571b101cf8612a8e7605b22ad43ca6d9d70","observation_id":"a0706ce2-7703-40c5-99e7-3e281332bcb3","resolution":{"observed_at":"2026-05-22T11:41:30.522197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.25080/majora-92bf1922-003","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"T heano: A C P U and G P U M ath C ompiler in P ython","venue":"Proceedings of the Python in Science Conferences","work_id":"9746b51a-1d67-49c8-9f15-ef145b48acd9","year":2010},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:c6d46e67842a62fee16e397720cd446a002548c286e1e17ff0eb658ea66dbb7e","observation_id":"222f3df3-160b-49ae-a3c1-b4f4841f9d89","resolution":{"observed_at":"2026-05-22T11:41:28.992453Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-021-03819-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T16:27:21.523331Z","title":"Jumper , author R","venue":"Nature","work_id":"655b2dc8-8df1-4dd8-9792-25aa2d8895e1","year":2021},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:0a9c96ac367a0d3a60a0ef603bf7639b13669357143b37d542646fe864044c9b","observation_id":"3327bfd7-d863-46c2-9855-9d373dba3179","resolution":{"observed_at":"2026-05-22T11:41:29.019953Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-20T16:52:45.231626+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-20T16:52:45.231626+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.02867","last_updated":"2018-07-28T06:51:27Z","snapshot_observed_at":"2026-07-06T06:37:55.065033Z","submitted_at":"2018-05-08T07:34:17Z","title":"Online normalizer calculation for softmax","version":2},"cited_work":{"arxiv_id":"1805.02867","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.02867","snapshot_observed_at":"2026-07-04T17:50:00.641865Z","title":"Online normalizer calculation for softmax","venue":"cs.PF","work_id":"69eba45e-8338-46b7-b28a-e99bb687af56","year":2018},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/1805.02867","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:18a17cb667ab6a789214ed01c7d95b2d865dd694fae7dc58641e7abe6f8bec3d","observation_id":"03480638-3ab7-4471-9169-d3f5d42d025f","resolution":{"observed_at":"2026-05-22T11:41:30.057410Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.12409","last_updated":"2022-04-22T18:20:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-08-27T17:35:06Z","title":"Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation","version":2},"cited_work":{"arxiv_id":"2108.12409","doi":"10.48550/arxiv.2108.12409","metadata_source":"pith","pith_arxiv_id":"2108.12409","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation","venue":"cs.CL","work_id":"145b1374-5258-4c00-a433-4db0f5a50749","year":2021},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2108.12409","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:7b360644ce756e066a68b17f1dad0fbc032e0e3b9e4295814cc692ded11ee904","observation_id":"4452f23b-53ca-418a-a195-37e526a34eed","resolution":{"observed_at":"2026-05-22T11:41:30.053065Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"torch.fx: Practical program capture and transformation for deep learning in python","venue":null,"work_id":"4e5bcc4b-d6bf-41e6-b5b4-f7e6a8aef084","year":2022},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:d1ec3fdd80133954133a2718895b79b87f8e6a46aa298d5c0080fd87bb1875c9","observation_id":"497d71f0-2bfc-4136-8e7c-5785fa9313af","resolution":{"observed_at":"2026-05-22T11:41:30.516094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08608","last_updated":"2024-07-12T22:15:02Z","snapshot_observed_at":"2026-07-06T18:44:53.587276Z","submitted_at":"2024-07-11T15:44:48Z","title":"FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision","version":2},"cited_work":{"arxiv_id":"2407.08608","doi":"10.48550/arxiv.2407.08608","metadata_source":"pith","pith_arxiv_id":"2407.08608","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision","venue":"cs.LG","work_id":"12a0982c-8c36-42c7-88e1-4a8bfb2aa44d","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2407.08608","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:4ce3933b8a325bf3512f500241323b485bbba31d2e786a3770da1aeec7613e2d","observation_id":"a4f182e1-9eda-4f43-af0e-85269ec903c9","resolution":{"observed_at":"2026-05-22T11:41:30.077659Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20399","last_updated":"2024-10-27T10:07:16Z","snapshot_observed_at":"2026-07-06T19:40:21.797132Z","submitted_at":"2024-10-27T10:07:16Z","title":"ThunderKittens: Simple, Fast, and Adorable AI Kernels","version":1},"cited_work":{"arxiv_id":"2410.20399","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.20399","snapshot_observed_at":"2026-07-04T06:29:38.279290Z","title":"F., Arora, S., Singhal, A., Fu, D","venue":null,"work_id":"ca5bbec4-e143-40f1-bfa8-dd63c8e514bd","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2410.20399","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:0b2cce40997b3926e2caec96169c28cd93f5d1e76c4df9dc5ce4f65b5cea798d","observation_id":"448fe839-69e8-429f-a150-98c7e1b23b9d","resolution":{"observed_at":"2026-05-22T11:41:30.048020Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04108","last_updated":"2025-06-05T05:39:48Z","snapshot_observed_at":"2026-07-06T21:36:40.449308Z","submitted_at":"2025-06-04T16:01:48Z","title":"Rectified Sparse Attention","version":2},"cited_work":{"arxiv_id":"2506.04108","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.04108","snapshot_observed_at":"2026-07-02T13:46:58.816966Z","title":"Rectified sparse attention","venue":null,"work_id":"aa22dbb5-75b6-4873-be14-90ee8a85e41a","year":2025},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2506.04108","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:2407cad3ab4948358203df36b2c7120c61e522ff88bb3689013e54b19164b1ee","observation_id":"3fd29474-9a0f-4b7a-89b8-11bf887c5093","resolution":{"observed_at":"2026-05-22T11:41:30.042725Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5508.332997","doi":"10.1145/3315508.3329973","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"T., and Cox, D","venue":null,"work_id":"36502ac3-807e-4ab8-ac44-c87017160e32","year":2019},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:f794c52c95bea46347cf48cadfd25443a9ea0ce97e131a2547438793cf7882ce","observation_id":"23c579cd-8b2c-48bf-9a80-c3dd887fc0fb","resolution":{"observed_at":"2026-05-22T11:41:28.984555Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T16:49:01.192843+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T16:49:01.192843+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"N., Kaiser, ., and Polosukhin, I","venue":null,"work_id":"b7ef62a8-a859-4ea3-a079-01be8f3def03","year":2017},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:5d10ff4c4e26b130a2201c95ceebebf5832750013c8ef810b4491231268fec26","observation_id":"656fa5ec-9723-4fc3-8622-83f7393c6898","resolution":{"observed_at":"2026-05-22T11:41:30.512830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"K., Velliengiri, P., Miao, X., Padon, O., and Jia, Z","venue":null,"work_id":"68e0612e-0a29-4b6e-8fd3-f389c1eb4c04","year":2025},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:76293b484e66c3882bd6f7f935adbc262ffd1beba6b71530ba383ff138f5dc18","observation_id":"23c10a99-c45a-464d-a350-67c8abca2aaf","resolution":{"observed_at":"2026-05-22T11:41:30.509938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05258","last_updated":"2025-04-07T12:04:28Z","snapshot_observed_at":"2026-08-03T16:01:59.392774Z","submitted_at":"2024-10-07T17:57:38Z","title":"Differential Transformer","version":2},"cited_work":{"arxiv_id":"2410.05258","doi":"10.48550/arxiv.2410.05258","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.05258","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Differential transformer","venue":"arXiv (Cornell University)","work_id":"4a130c7c-dbb1-4cc0-a1ef-92972e8bad0e","year":2024},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2410.05258","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:5073a8b58ff8150762c20458a04b7c90e3ac250243e54ef9655d52cd9b77c057","observation_id":"3b38fc14-9229-4461-a6b1-e11f1d61a5ab","resolution":{"observed_at":"2026-05-22T11:41:30.037579Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01005","last_updated":"2025-04-21T20:10:11Z","snapshot_observed_at":"2026-07-06T20:15:36.280948Z","submitted_at":"2025-01-02T02:02:20Z","title":"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving","version":2},"cited_work":{"arxiv_id":"2501.01005","doi":"10.48550/arxiv.2501.01005","metadata_source":"pith","pith_arxiv_id":"2501.01005","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving","venue":"cs.DC","work_id":"a153885b-2460-4177-9053-8d0011adfcb9","year":2025},"citing_paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-22T11:40:41.762364Z"},"links":{"cited_paper":"/paper/2501.01005","citing_paper":"/paper/2511.02043"},"observation_digest":"sha256:5cff05826a2bb485f5bda5a8256f965a53a80f2c0b3196a2797ff5ac35710479","observation_id":"cf12c40c-05bb-42b3-8faf-96bef3355b8e","resolution":{"observed_at":"2026-05-22T11:41:30.032495Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2511.02043","last_updated":"2026-05-20T23:03:49Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T22:34:48.431368Z","submitted_at":"2025-11-03T20:25:19Z","title":"Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":0,"verified_exact":12,"verified_fuzzy":7},"total_outbound_references":23},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2511.02043."}