{"as_of":"2026-08-23T05:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:71cbfa9aed48cd8546e3ea9acebce073d04709b1ee31d3baa0f743beea3acfb2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:54:31.027136Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-28T23:52:49.120111Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-08-11T10:54:31.027136Z","title":"When linear attention meets autoregressive decoding: Towards more effective and efficient linearized large lan- guage models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-08-15T20:19:47.193159Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T10:54:31.027136Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2412.16112"},"observation_digest":"sha256:bdc85c931b7293804104dfb3b1c00425486322eb64d82ad9bb2878d6093723ef","observation_id":"320b531b-7454-44da-b713-e61df6029348","resolution":{"observed_at":"2026-08-11T10:54:31.027136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-08-07T11:16:25.744942Z","title":"(C.).: When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05387","last_updated":"2025-06-11T16:08:29Z","snapshot_observed_at":"2026-08-13T07:03:43.882155Z","submitted_at":"2025-06-03T14:25:23Z","title":"Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:16:25.744942Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2506.05387"},"observation_digest":"sha256:dde5852295c5134bcf7ee4194b81d38e99b5d486facd06fe0f1a894bd155d456","observation_id":"c94554d6-a974-4217-8e4f-1c17264422d3","resolution":{"observed_at":"2026-08-07T11:16:25.744942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-08-06T17:47:40.284010Z","title":"When linear attention meets autoregressive decoding: Towards more effec- tive and efficient linearized large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09955","last_updated":"2025-07-14T06:10:30Z","snapshot_observed_at":"2026-08-20T23:44:49.241075Z","submitted_at":"2025-07-14T06:10:30Z","title":"DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models","version":1},"reference_index":109,"source":"pdf_text","source_observed_at":"2026-08-06T17:47:40.284010Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2507.09955"},"observation_digest":"sha256:50987fa2ea594ae73370270cf858259bd1dafdc21c9fe37cc58c67ff3514d429","observation_id":"1c00f209-f76d-45a7-af3a-176649b03151","resolution":{"observed_at":"2026-08-06T17:47:40.284010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-08-06T14:59:21.394850Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17245","last_updated":"2025-07-23T06:29:38Z","snapshot_observed_at":"2026-08-18T13:12:24.804116Z","submitted_at":"2025-07-23T06:29:38Z","title":"DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T14:59:21.394850Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2507.17245"},"observation_digest":"sha256:313e76e0a9bcf1a99b5e038ad08447eaed45c915188acf5e21f3136db312de9b","observation_id":"8b144df7-8c9a-4500-ba33-d383d03d7bc4","resolution":{"observed_at":"2026-08-06T14:59:21.394850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.07368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-06-28T23:52:49.120111Z","title":"When linear attention meets autoregressive decoding: Towards more effective and efficient linearized large language models","venue":null,"work_id":"89c4dd02-ea92-4575-b93c-372c8035bea5","year":2024},"citing_paper":{"arxiv_id":"2605.06548","last_updated":"2026-05-07T16:44:56Z","snapshot_observed_at":"2026-08-14T20:14:56.366023Z","submitted_at":"2026-05-07T16:44:56Z","title":"Continuous Latent Diffusion Language Model","version":1},"reference_index":104,"source":"pdf_text","source_observed_at":"2026-05-08T10:04:09.646578Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2605.06548"},"observation_digest":"sha256:a31c6efe0e627e19cadacc451df0b0084f741b28fe4e0d86f0b4d6f145dc4e07","observation_id":"4334fd61-d186-4da1-97b9-d008cb97a82a","resolution":{"observed_at":"2026-05-11T20:11:10.904962Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.07368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07368","snapshot_observed_at":"2026-06-28T23:52:49.120111Z","title":"When linear attention meets autoregressive decoding: Towards more effective and efficient linearized large language models","venue":null,"work_id":"89c4dd02-ea92-4575-b93c-372c8035bea5","year":2024},"citing_paper":{"arxiv_id":"2605.29639","last_updated":"2026-05-28T09:07:06Z","snapshot_observed_at":"2026-08-21T18:55:36.948343Z","submitted_at":"2026-05-28T09:07:06Z","title":"RTP-LLM: High-Performance Alibaba LLM Inference Engine","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-28T23:52:40.763228Z"},"links":{"cited_paper":"/paper/2406.07368","citing_paper":"/paper/2605.29639"},"observation_digest":"sha256:4133c103be4efddc2d08a3668dda6aa8a17e9241c69da2e34db937aeda2d5e8b","observation_id":"6c185146-0e92-4076-ac44-43551f7269fc","resolution":{"observed_at":"2026-06-28T23:52:49.121631Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.07368/citation-record","integrity":"/paper/2406.07368/integrity","json":"/paper/2406.07368/citation-record.json","paper":"/paper/2406.07368"},"outbound":[],"paper":{"arxiv_id":"2406.07368","last_updated":"2024-07-25T17:18:01Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T13:44:07.761054Z","submitted_at":"2024-06-11T15:34:43Z","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.07368."}