{"as_of":"2026-08-09T21:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2d0599ba23e11032d1893c38f2223f451b9db3cd0bb1c2c84f4ed6a899f379b8","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T13:18:18.331031Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.10650/citation-record","integrity":"/paper/2606.10650/integrity","json":"/paper/2606.10650/citation-record.json","paper":"/paper/2606.10650"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.03863","last_updated":"2024-05-23T06:08:37Z","snapshot_observed_at":"2026-08-02T01:09:26.116796Z","submitted_at":"2023-12-06T19:18:42Z","title":"Efficient Large Language Models: A Survey","version":4},"cited_work":{"arxiv_id":"2312.03863","doi":"10.48550/arxiv.2312.03863","metadata_source":"pith","pith_arxiv_id":"2312.03863","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Effi- cient large language models: A survey.arXiv preprint arXiv:2312.03863","venue":"cs.CL","work_id":"d859363b-b3a3-42c5-861b-b3b7776e4ff7","year":2023},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2312.03863","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:4af4bbbb577f614471016b02fbeb05d5631d1367c9a9266983e2a93eebd4ddc2","observation_id":"bf4af32d-1f4e-4508-8baf-1ea91200213a","resolution":{"observed_at":"2026-07-03T05:27:39.795371Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-20T14:22:10.21899+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T14:22:10.21899+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01923","last_updated":"2024-12-09T14:16:59Z","snapshot_observed_at":"2026-07-06T17:11:21.633421Z","submitted_at":"2024-01-03T18:08:57Z","title":"The Internet of Things in the Era of Generative AI: Vision and Challenges","version":4},"cited_work":{"arxiv_id":"2401.01923","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.01923","snapshot_observed_at":"2026-07-03T05:27:39.801986Z","title":"Iot in the era of generative ai: Vision and challenges.arXiv preprint arXiv:2401.01923, 2024","venue":null,"work_id":"cb79bff9-1ab9-4a11-80ce-7e077de2d42d","year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2401.01923","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:82b8638c3cdedd90a8b40ab9088553d276cec7aec55142af0212d4efbb7ba071","observation_id":"6bd91c5c-615c-4edd-a806-dfd06e9a4ac7","resolution":{"observed_at":"2026-07-03T05:27:39.803407Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T13:18:18.331031Z","title":"D2O: dynamic discriminative operations for efficient long-context inference of large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:1ee9845e3cbc1924b44e20289d601daeffd2937eb5c95f0979ded04c795c448e","observation_id":"a77c7bc3-5ca6-49e7-bf5f-1bfed029da4d","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Parallelizing linear transformers with the delta rule over sequence length","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:d9c12c2b12f2b1dc089afe814995d77b7fe4d2608ac1a88efa2c873a1f0fadf8","observation_id":"36fbe125-92d3-45c4-b5cd-8fae184dd0c1","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Gated delta networks: Improving mamba2 with delta rule","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:bf2f61d13848016a1a80e016ce3509c7c35e13c3161addf1705c17ab3b5fff0e","observation_id":"625553e2-6a4d-4c38-90ce-896250d76c28","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2506.04761","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T05:27:39.804512Z","title":"Log-linear attention","venue":null,"work_id":"9c67d35a-c055-4f41-862f-308bd2e3e832","year":2025},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:73b68eaaa2aa6cba24908693d4eca61de37d8a60e11b5a76e3d0115eb1e306ad","observation_id":"90562a5c-5c05-45f2-9c6c-b8e5dfd096e9","resolution":{"observed_at":"2026-07-03T05:27:39.805845Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2507.04416","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T05:27:39.806900Z","title":"Rat: Bridging rnn efficiency and attention accuracy via chunk-based sequence modeling.arXiv preprint arXiv:2507.04416","venue":null,"work_id":"498ae8df-64ef-4c16-819a-d925df2c70b0","year":2025},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:44938a101aefd4c19f5d4283cb140c9e075dbd9f8040879a2abf968ac5363671","observation_id":"ff44463c-5200-4e82-9e62-0fdd9a95fc3a","resolution":{"observed_at":"2026-07-03T05:27:39.808242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.10327","last_updated":"2025-07-14T14:36:34Z","snapshot_observed_at":"2026-08-06T17:31:33.231648Z","submitted_at":"2025-07-14T14:36:34Z","title":"Generalizing the Cauchy-Schwarz inequality: Hadamard powers and tensor products","version":1},"cited_work":{"arxiv_id":"2507.10327","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.10327","snapshot_observed_at":"2026-07-03T05:27:39.810152Z","title":"Generalizing the cauchy-schwarz inequality: Hadamard powers and tensor products.arXiv preprint arXiv:2507.10327, 2025","venue":null,"work_id":"d994280d-cc8e-44e6-8eee-8dcae3ae5589","year":2025},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2507.10327","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:6a397f9cd1fa4b705c8e97df5dc335ac692a758b842973bdfe8635a9a90de34b","observation_id":"54974d42-43be-4111-bf3a-8fdeb80fc1b2","resolution":{"observed_at":"2026-07-03T05:27:39.811665Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"4893.164491","doi":"10.1145/1644893.1644914","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kandula, S","venue":null,"work_id":"8b313404-93f0-4346-b97c-e9fae889431f","year":2009},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:9f441af5a952067d3fa9424345e3153a1f6c00ec2cd5658a8073c3be0ddcc10e","observation_id":"4773cfe9-f931-43a1-a442-9665796f4bfd","resolution":{"observed_at":"2026-06-27T13:20:56.498542Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T13:18:18.331031Z","title":"Transformers are ssms: Generalized models and efficient algorithms through structured state space duality","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:9d25a7c9d49d03354474bac71b430be13c6747859dfc5e4fcb6e1277da794caa","observation_id":"016d5bc2-a160-47d3-b5e8-d97d950a6ea2","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"The LAMBADA dataset: Word prediction requiring a broad discourse context","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:faf8f2ff148330ea031b5fa67fa5a3ed73e740ed9bd5d637d5d8a3cb07428dbe","observation_id":"90d8747d-add1-4450-8852-d819d4bc853f","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"PIQA: reasoning about physical commonsense in natural language","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:79a402b89747e3f53a61160993ab8cfee5d93e65264ffcfbea6e5c0e10bb9afb","observation_id":"8fff127f-341d-4211-91a6-71ebb0b55dfb","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Hellaswag: Can a machine really finish your sentence? InACL (1), pages 4791–4800","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:620d786de1fc1b64c056702a77e38d25db80deb74593efbd8c0bd8319ca2be3a","observation_id":"84aabda0-5d25-4bef-80aa-bebdb09d8771","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Winogrande: an adversarial winograd schema challenge at scale.Commun","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:faf26a802f6194a38980acf361104dd926ed5beeece58d67145977abc87990c2","observation_id":"06cfc6b1-ffe1-490e-8a13-5b680f82e7e3","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Can a suit of armor conduct electricity? A new dataset for open book question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:d0028e7638b80859be41dca4db363b0bcc3081e90aeb68a8ce3783e53c2f9de5","observation_id":"adfc1108-4f06-4c79-8f2c-de0b5bff0f16","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Commonsenseqa: A question answering challenge targeting commonsense knowledge","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:4ebb72b794805a7754b06b0596ba858090dcc67165950f72cf90f34e14bd4f8e","observation_id":"053248d3-f82e-4270-adb1-324ff19527fc","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.03315","last_updated":"2021-02-05T17:41:43Z","snapshot_observed_at":"2026-08-04T00:22:28.888492Z","submitted_at":"2021-02-05T17:41:43Z","title":"Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":"2102.03315","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.03315","snapshot_observed_at":"2026-07-03T19:08:50.555729Z","title":"Think you have solved direct-answer question answering? try arc-da, the direct-answer ai2 reasoning challenge","venue":null,"work_id":"8472317a-9c0a-42c0-8564-bc0710ee8dc6","year":2021},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2102.03315","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:983695cbf41e527fae3b52667661483d6f10e2c6b28eb71d3124faf6c5aebcc4","observation_id":"badf0a94-f75a-4475-b5d9-a3f734903f01","resolution":{"observed_at":"2026-07-03T05:27:39.814497Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T13:18:18.331031Z","title":"Openceres: When open information extraction meets the semi-structured web","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:f35757ad5a6d55ea3a08b1610c6933d07aa899c8c2ddb485bc89dc6043f66aac","observation_id":"6bf6d3b0-eaf7-446c-81c3-bee219b26ccd","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Know what you don’t know: Unanswerable questions for squad","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:db40ec7207d06026463b326c7e4abbb9728e7b94063557a2c292525dc7e4fe80","observation_id":"0f905f97-44c3-448a-9fce-3133cf7905a8","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Language models enable simple systems for generating structured views of heterogeneous data lakes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:0556b11e745420d2fbfa77bb9bcd3a8d47561682920ce78ca82db077d1585cf8","observation_id":"3cc1bd67-358b-40c3-9db4-da6f16386fab","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:6f00512efcbcae6d2e8b21c3c3f51d125fd89db5f76e29642aa9f6315abc947c","observation_id":"3937c890-a687-4f70-a02c-8708638c0379","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:783173c875a3903916a52ae7bc6b6670a649c2e1060227ec1235b96e162ecec0","observation_id":"5f65e2be-a459-4b7e-810f-645333d6221f","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"2026-06-27T13:18:18.331031Z","title":"Natural questions: a benchmark for question answering research","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:e5986ed1fe610320a173cd855cd51146c4aba920371ab261b693771d34f501fc","observation_id":"5938956f-fd71-4e2e-9b68-2d2a1a3a9879","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06654","last_updated":"2024-08-06T21:48:58Z","snapshot_observed_at":"2026-07-06T17:58:00.820879Z","submitted_at":"2024-04-09T23:41:27Z","title":"RULER: What's the Real Context Size of Your Long-Context Language Models?","version":3},"cited_work":{"arxiv_id":"2404.06654","doi":"10.48550/arxiv.2404.06654","metadata_source":"pith","pith_arxiv_id":"2404.06654","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RULER: What's the Real Context Size of Your Long-Context Language Models?","venue":"cs.CL","work_id":"c0bc4689-3ce8-4e3d-9442-bd74869445bb","year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2404.06654","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:a2915df1213abe13ecdfc2dc9552943fb07a5213fdb21fa88c912fa776d79ef3","observation_id":"cd79f933-d1d7-46da-b0f9-7c3e2fb5e1b8","resolution":{"observed_at":"2026-07-03T05:27:39.809094Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T13:18:18.331031Z","title":"Longbench: A bilingual, multitask benchmark for long context understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:e9d92e8f214a2270c69f266f02556bf74a96afea4d21f2f18dae197e2efc7178","observation_id":"447853f9-fd8a-48e1-a7f1-9952f0184784","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","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":"records/1260860","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T14:47:14.558044Z","title":"He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C","venue":null,"work_id":"e7e9d443-273d-4722-8ec7-59adb893de0e","year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:0d9b3a924564dc6ca2410fe27830bd15c98b36f8d0ab13b7e69d60f56dff78e9","observation_id":"583c6f59-8681-4a98-a4e0-24f35b1a4312","resolution":{"observed_at":"2026-07-03T05:27:39.813686Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T13:18:18.331031Z","title":"Simple linear attention language models balance the recall-throughput tradeoff","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:93790e18cb1e1b31ea8eee22036ee4abca4b5937b8cb14718c3912f1dbbff37f","observation_id":"b64b2ac2-527e-4c50-9608-7eccaacb0934","resolution":{"observed_at":"2026-06-27T13:18:18.331031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":"2312.00752","doi":"10.48550/arxiv.2312.00752","metadata_source":"pith","pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","venue":"cs.LG","work_id":"4ee75248-1199-492c-a52f-6661e0f4adff","year":2023},"citing_paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:18.331031Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2606.10650"},"observation_digest":"sha256:ab6957fe65ddbbdcecfc56a131db3598b31b3a5fd366cd8e61a6d1677f126b69","observation_id":"fe41f40b-c4eb-4e33-9c66-88efee403781","resolution":{"observed_at":"2026-07-03T05:27:39.806518Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-30T19:25:31.081761+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-30T19:25:31.081761+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.10650","last_updated":"2026-06-09T09:57:48Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T05:40:45.882137Z","submitted_at":"2026-06-09T09:57:48Z","title":"Dynamic Linear Attention"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":18,"verified_exact":8,"verified_fuzzy":0},"total_outbound_references":28},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.10650."}