{"as_of":"2026-08-09T06:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c82a03c97266ebae2288f97ff8d2bde98a82a76dde70593a3670e28f3e21e534","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:40:32.352322Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T03:38:26.132412Z","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":"2026-07-02T11:36:55.093343Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"cited_work":{"arxiv_id":"2502.06924","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.06924","snapshot_observed_at":"2026-07-02T11:36:55.093343Z","title":"XAMBA: Enabling efficient state space models on resource-constrained neural processing units,","venue":null,"work_id":"7aa9dbc0-ac02-4241-87e0-1a656266bb72","year":2025},"citing_paper":{"arxiv_id":"2606.05271","last_updated":"2026-06-03T17:09:41Z","snapshot_observed_at":"2026-08-01T20:07:03.702960Z","submitted_at":"2026-06-03T17:09:41Z","title":"BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T03:38:26.132412Z"},"links":{"cited_paper":"/paper/2502.06924","citing_paper":"/paper/2606.05271"},"observation_digest":"sha256:61556e646c9b3e5bb6a0e14cacd089eb164b12f485bcdc8ed2384cdc86b4985a","observation_id":"566f115c-609b-4810-b1da-d3900215f90e","resolution":{"observed_at":"2026-07-02T11:26:54.823745Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"cited_work":{"arxiv_id":"2502.06924","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.06924","snapshot_observed_at":"2026-07-02T11:36:55.093343Z","title":"XAMBA: Enabling efficient state space models on resource-constrained neural processing units,","venue":null,"work_id":"7aa9dbc0-ac02-4241-87e0-1a656266bb72","year":2025},"citing_paper":{"arxiv_id":"2606.05362","last_updated":"2026-06-06T05:44:46Z","snapshot_observed_at":"2026-08-06T22:00:33.534591Z","submitted_at":"2026-06-03T19:10:07Z","title":"MOSAIC: A Workload-Driven Simulation and Design-Space Exploration Framework for Heterogeneous NPUs","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T03:21:27.016397Z"},"links":{"cited_paper":"/paper/2502.06924","citing_paper":"/paper/2606.05362"},"observation_digest":"sha256:53a2d11785cbf40369a311286c75761c25ab0376afad7c38b889ce05ea5dc345","observation_id":"dcce505d-611d-42cc-84f3-0fff19638981","resolution":{"observed_at":"2026-07-02T11:36:55.094709Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.06924/citation-record","integrity":"/paper/2502.06924/integrity","json":"/paper/2502.06924/citation-record.json","paper":"/paper/2502.06924"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:40:32.815507Z","title":"Transformers are SSMs: generalized models and efficient algorithms through structured state space duality","venue":null,"work_id":"4acb5946-d259-4770-b5cb-c5f805760830","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.221482Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:63a9dffdbae4471619082d51f06c2dc7d1aa3d0eef510630b47339fa29e4e6b5","observation_id":"29334535-2412-408a-ab8a-554646494f75","resolution":{"observed_at":"2026-08-08T14:40:32.818947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11053","last_updated":"2024-12-15T05:15:54Z","snapshot_observed_at":"2026-07-06T20:07:14.913902Z","submitted_at":"2024-12-15T05:15:54Z","title":"NITRO: LLM Inference on Intel Laptop NPUs","version":1},"cited_work":{"arxiv_id":"2412.11053","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.11053","snapshot_observed_at":"2026-08-08T14:40:32.536828Z","title":"NITRO: LLM Inference on Intel Laptop NPUs","venue":"cs.CL","work_id":"5ebd3b79-a985-4332-8416-a50c17948df2","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.232630Z"},"links":{"cited_paper":"/paper/2412.11053","citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:74572d2910f27effdf217bdb719b0e906bba91a44fa0a9378580b605b72722e5","observation_id":"64e97389-5dcf-496c-9a3e-9e46b63b2568","resolution":{"observed_at":"2026-08-08T14:40:32.541837Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.807395Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","venue":null,"work_id":"7a30530e-49d0-482e-82de-5f8f85ddbb19","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.238986Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:ad59431eae608ded98798e0891dfebcf5cea365eb1325c8c8d5c0442ea381165","observation_id":"15f2dd0c-28e2-4076-ad8d-1e7c5f72b96f","resolution":{"observed_at":"2026-08-08T14:40:32.810692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.798409Z","title":"HiPPO: Recurrent Memory with Optimal Polynomial Projections","venue":null,"work_id":"ddf3dc50-7fb4-4f03-a397-3301348c6c19","year":2020},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.241959Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:713ac869541ad143abb234929e2031ea9000fa7cc46bf8da16e86e2d1317a695","observation_id":"874b3a82-befc-4a48-9934-bfc80f3efc50","resolution":{"observed_at":"2026-08-08T14:40:32.801452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.791393Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","venue":null,"work_id":"9db3676d-75c6-4f7c-95af-940bb66bfb87","year":2022},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.245076Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:5b4df1232f94b9c38e5c7ae92c91eaf41b95a1e501deb2affbc73bedfa2478c9","observation_id":"46f3667c-0ba2-4c5d-abec-bcf28b92ddc3","resolution":{"observed_at":"2026-08-08T14:40:32.793522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.785130Z","title":"Intel \\ Distribution of OpenVINO \\ Toolkit","venue":null,"work_id":"98789334-6351-4eab-8dd9-31ee74f2a929","year":2025},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.248108Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:20a181c8c29c20a4c16c5ec238a2058e82e3aec7f9e588ce3775b573ca894914","observation_id":"d010c0a8-502d-407d-9027-2dc487909491","resolution":{"observed_at":"2026-08-08T14:40:32.787375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.778484Z","title":"Intel® Core™ Ultra series mobile processors product brief , 2024 a","venue":null,"work_id":"59437452-d292-4635-afcc-3e9e58344c4f","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.251208Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:dec0f9f235b34bab4729dae25b9ee1e924b5fa439aed36f5145fe831639999cc","observation_id":"76545994-62f5-45ad-9fea-a4b577dd7ae9","resolution":{"observed_at":"2026-08-08T14:40:32.780755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.771826Z","title":"Intel® Core™ Ultra series 1 product brief , 2024 b","venue":null,"work_id":"4a015cb8-7e9b-4a92-9c75-a45786f6fe2a","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.253770Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:7668afec7617ee8905d8fe1431dfc72834ab65c468914433567717ae06694418","observation_id":"61bb0858-5a2f-4cc2-9eda-f8532b332bb4","resolution":{"observed_at":"2026-08-08T14:40:32.774218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.11440","last_updated":"2024-09-16T15:18:33Z","snapshot_observed_at":"2026-07-06T19:17:02.745056Z","submitted_at":"2024-09-16T15:18:33Z","title":"MARCA: Mamba Accelerator with ReConfigurable Architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.11440","snapshot_observed_at":"2026-08-08T14:40:32.256179Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.256179Z"},"links":{"cited_paper":"/paper/2409.11440","citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:557c356348e172049824c9e6a65a1da9d6f31fb46cd633fe1eaba0684c7c15c2","observation_id":"1ae3cd47-e04c-4d62-8978-b11920626a40","resolution":{"observed_at":"2026-08-08T14:40:32.256179Z","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":"2026-08-08T14:40:32.761807Z","title":"MobileLLM: optimizing sub-billion parameter language models for on-device use cases","venue":null,"work_id":"c3b6fe27-cccb-4b12-8c6c-d6191d1c97d3","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.258936Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:3d0b1b7d7b9eb56a2a697313dadb0b21532f5ac47a24cdf5512681381420567f","observation_id":"b58741e0-ba0c-4719-a41b-7609b17f34d3","resolution":{"observed_at":"2026-08-08T14:40:32.767135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.674426Z","title":"OpenVINO IR Format: Operation Sets and Specifications","venue":null,"work_id":"9f4afc87-11b5-4ef4-a96e-b976d43a2324","year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.261492Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:78c1079b36b264111538b7645d8ce11c3576a305dc0bf4f60d56f8bff5b5d60b","observation_id":"2fa352ca-ab8a-49fd-8865-d2ca26225dcd","resolution":{"observed_at":"2026-08-08T14:40:32.728807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16112","last_updated":"2024-04-24T18:10:31Z","snapshot_observed_at":"2026-08-08T08:03:41.810567Z","submitted_at":"2024-04-24T18:10:31Z","title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16112","snapshot_observed_at":"2026-08-08T14:40:32.264351Z","title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.264351Z"},"links":{"cited_paper":"/paper/2404.16112","citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:eea6cae479dd683d922c956403fc76c5e328851b171157412c6fb068674f8886","observation_id":"78189654-2062-4724-a573-4e11b52d9863","resolution":{"observed_at":"2026-08-08T14:40:32.264351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09026","last_updated":"2024-04-11T23:26:33Z","snapshot_observed_at":"2026-07-06T17:44:21.659847Z","submitted_at":"2024-03-14T01:39:12Z","title":"FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09026","snapshot_observed_at":"2026-08-08T14:40:32.267690Z","title":"Mathaikutty, Soumendu K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.267690Z"},"links":{"cited_paper":"/paper/2403.09026","citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:0c42be3824876c7ff3df6b39fe4dde1b82f2ddf1885670661739391ec51294b8","observation_id":"77a29c69-b3fc-4761-be98-e7bd36247b74","resolution":{"observed_at":"2026-08-08T14:40:32.267690Z","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":"2026-08-08T14:40:32.606758Z","title":"Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation","venue":null,"work_id":"a3985eda-c671-4eb0-bd68-e1e9a96dee1e","year":2023},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.271028Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:546cc7beb7f8a25b280a780b10a6c9c139876c9c81c43d81ae6083a2b3c4008f","observation_id":"62eaa70b-c22d-4577-9438-dc0833b70ac6","resolution":{"observed_at":"2026-08-08T14:40:32.650856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.577296Z","title":null,"venue":null,"work_id":"145465e3-5436-43f2-80e8-5e0ebd28578e","year":2018},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.274587Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:69f4e6f0dbb3ad1db75dabae92592911417d9c1dd360839cf4bf4d1675feae0c","observation_id":"f125ec69-9ba3-4680-85cc-dd92e3a9d245","resolution":{"observed_at":"2026-08-08T14:40:32.579897Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.568734Z","title":"Wang et al","venue":null,"work_id":"bb3d7074-fc2f-4a4d-88b2-591037383e3d","year":2018},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.277157Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:16fe1e7006175cc7b2c0825b0d3e5cda6e891328c5b17caca87b82a1ae00b95e","observation_id":"b0ddade6-9230-4587-8430-5fcb380f1d4e","resolution":{"observed_at":"2026-08-08T14:40:32.572111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T14:40:32.560904Z","title":"Kinsy, Nanning Zheng, and Pengju Ren","venue":null,"work_id":"a2207c21-c7e2-4f27-919d-eab250156577","year":2019},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.280033Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:8785f56199cf58e9304a26a07e21c6d5f33d9ea9f21cf5a444cfcb7138ddeefb","observation_id":"783e6986-ba81-41f5-8ad7-4dd8b7cdd340","resolution":{"observed_at":"2026-08-08T14:40:32.563978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:40:32.282639Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.282639Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:fa18ae8127db05742691b8d0efee94751a96ee857911be1186c5272b2f7d9a3d","observation_id":"40f3898c-52ce-4571-8b62-53b0e3fed8f4","resolution":{"observed_at":"2026-08-08T14:40:32.282639Z","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":"2026-08-08T14:40:32.286121Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.286121Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:330dab2a1757f2644fe760facea1927979e18d045ff7c9fa77a94cb1ff939ed0","observation_id":"1112620a-1c96-4f92-9a45-9922445a7685","resolution":{"observed_at":"2026-08-08T14:40:32.286121Z","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":"2026-08-08T14:40:32.318670Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.318670Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:93b05cf97e7a6ab790fca0e367e4e01dd3b8e1997010e40c7b6b9f3cfe5d297f","observation_id":"7730c6e0-037f-4ab5-aa2a-a34782904a6e","resolution":{"observed_at":"2026-08-08T14:40:32.318670Z","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":"2026-08-08T14:40:32.352322Z","title":"jU HA ZUu^ޚz HcYqYIcv/IO[x r@z V = <V Lj +2Ǥ=Fiͬ1 d>v qC ѨM@ ȡ6q 6( [Aeiw w'8 H : Z׿ w |o [ zj <>uwj^\\ ҄;?հ#FO2&=̹ڈ","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T14:40:32.352322Z"},"links":{"citing_paper":"/paper/2502.06924"},"observation_digest":"sha256:701d5186b9191a2602683f669aa0a498fcc957b9ed3f12b27e7a2a98158d8afc","observation_id":"06ac8035-bfa4-4718-a57d-351d06525227","resolution":{"observed_at":"2026-08-08T14:40:32.352322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.06924","last_updated":"2025-03-31T03:26:29Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T14:33:23.373251Z","submitted_at":"2025-02-10T17:33:30Z","title":"XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":12},"total_outbound_references":21},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 2 inbound Pith citation observations for arXiv:2502.06924."}