{"as_of":"2026-08-07T07:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1a295926c3e8093d63f60d5bad79ef9425e78412111460abab4ea7caf3eed830","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T05:50:06.986471Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2604.17862/citation-record","integrity":"/paper/2604.17862/integrity","json":"/paper/2604.17862/citation-record.json","paper":"/paper/2604.17862"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":"2410.24164","doi":"10.48550/arxiv.2410.24164","metadata_source":"pith","pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","venue":"cs.LG","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:eb3d9e48692ca72640245fea34578594f7973c38bb84b0cac46e09e3f8ae11fd","observation_id":"02c8d619-9dee-4c09-9fec-9bc643c8b958","resolution":{"observed_at":"2026-05-10T12:38:24.665044Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06817","last_updated":"2023-08-11T17:45:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-12-13T18:55:15Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","version":2},"cited_work":{"arxiv_id":"2212.06817","doi":"10.48550/arxiv.2212.06817","metadata_source":"pith","pith_arxiv_id":"2212.06817","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","venue":"cs.RO","work_id":"e11bda85-8531-46bc-a07f-d0ade3643ab1","year":2022},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/2212.06817","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:12dd76ac5ffb94083a5fc8617eca0bad340b16332ae6133add8136f14fef88b8","observation_id":"fa59266e-d1fd-4220-8f30-0f82e676b043","resolution":{"observed_at":"2026-05-10T22:41:14.065009Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T08:56:59.442527Z","title":"Rt-2: Vision-language-action models transfer web knowledge to robotic control","venue":null,"work_id":"9898a490-983a-46cc-ad07-30f2e1ac1ad0","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:950689750665fe152b56a8f4e2c555a034a85a3b6239f88c24b1fc08dde43d51","observation_id":"c850b040-387f-40e2-bfb4-ee08392f2d0d","resolution":{"observed_at":"2026-05-21T18:45:29.350076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13139","last_updated":"2023-12-21T05:34:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-20T16:00:43Z","title":"Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation","version":2},"cited_work":{"arxiv_id":"2312.13139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.13139","snapshot_observed_at":"2026-07-04T21:00:09.556391Z","title":"Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation","venue":"cs.RO","work_id":"e92c2c13-4330-45fe-8231-34a6002626bd","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/2312.13139","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:5e6ba8660810cad484b98d32d301b4d88575c908f5c29220240e5de140b3e11c","observation_id":"deb00544-dd58-47ab-8194-91ad9770f4f0","resolution":{"observed_at":"2026-05-13T16:32:05.974462Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06158","last_updated":"2024-10-08T16:00:47Z","snapshot_observed_at":"2026-08-03T02:22:13.984027Z","submitted_at":"2024-10-08T16:00:47Z","title":"GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation","version":1},"cited_work":{"arxiv_id":"2410.06158","doi":"10.48550/arxiv.2410.06158","metadata_source":"pith","pith_arxiv_id":"2410.06158","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation","venue":"cs.RO","work_id":"843ab5eb-2815-4db8-b3bc-890b23fa5ffa","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/2410.06158","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:6c73f7542bc56218b98e19db1674c7fe93d1149aa42a9ba051333796155f9b34","observation_id":"5e9a00a5-00b6-481e-b19c-fb0b53e84d95","resolution":{"observed_at":"2026-05-12T01:09:34.215368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-22T23:23:05.602157+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T23:23:05.602157+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"NVIDIA Jetson AGX Orin Series, A Gi- ant Leap Forward for Robotics and Edge AI Applications, Techni- cal Brief","venue":null,"work_id":"04394d11-fcba-4f82-9284-a74cbfd66337","year":2022},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:93d9a2fee6f4e644adace92a6ab54f087cd99d5ba1bf25b9a1869118b869d3cd","observation_id":"1afe379a-f459-417a-9096-e392c9aa0ca0","resolution":{"observed_at":"2026-05-21T18:45:29.352699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"NVIDIA Jetson Thor","venue":null,"work_id":"cacf6053-11dc-4626-9ca8-eb6b31113f5b","year":2025},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:5aa969f36968fd0b3d0d89d2f156b30218ca4c44ed28ab96fc4168f02201de61","observation_id":"cb6a1d26-fbb7-4298-a905-39d212c71284","resolution":{"observed_at":"2026-05-21T18:45:29.355075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Computer and redundancy solution for the full self-driving computer","venue":null,"work_id":"62384b12-4c0f-4ce8-80c5-a1b250269a87","year":2019},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:781c09de338074ff35fcce69befdff1af563b66c43d99a99be84eae27548f1b5","observation_id":"75b45ec7-0b08-4e07-b0ad-d29e52d61cb0","resolution":{"observed_at":"2026-05-21T18:45:29.325861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Samsung to make tesla’s hw 4.0 self-driving auto chip","venue":null,"work_id":"4aa939a9-58f3-41c0-be1b-89850e65ceed","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:30cb6c790945ca9c4d94dda233579731c13b956f5a574722bc99b2c20d436095","observation_id":"c84e4a8e-589d-42c8-9faf-8cf90227cd67","resolution":{"observed_at":"2026-05-21T18:45:29.343533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Elon musk reveals the first details about hardware 5 autopilot computer and sensors","venue":null,"work_id":"4d5fec78-3325-4eca-a5af-f93467822a6d","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:ee49d7058972e46b7cb3a4d66c1c421719320b8c7826d9186a145d608a3e2711","observation_id":"97618fc5-6cb5-4e6e-8623-223a4dca1b50","resolution":{"observed_at":"2026-05-21T18:45:29.335640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Data flow supercomputers","venue":null,"work_id":"da2a52c2-5dfd-4c00-bc1d-2048ff0322a7","year":1980},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:1235ee702d92749fb6a93ee625c6aab05a07d34535b76114254c3b39628f1b55","observation_id":"35168c86-f655-42ef-abf8-f26e101397bd","resolution":{"observed_at":"2026-05-21T18:45:29.333343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Advances in the dataflow computational model","venue":null,"work_id":"1d42c66a-81f6-4c50-a04b-36eb38b2f00b","year":1907},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:ad83aa9ee0d43eea1606943b5f74e20977e5c07c073fa83e07da85c4d63a11b4","observation_id":"98629b02-763a-416c-b7d3-de33b3c8169e","resolution":{"observed_at":"2026-05-21T18:45:29.338499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Think fast: A tensor streaming processor (tsp) for accelerating deep learning workloads","venue":null,"work_id":"70f9aee0-24c9-4146-9d67-a1867ed96db8","year":2020},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:c0a88438cebe8ee6da651f97437d82e8a6b4db85b1f5b15fe346d307cb3c8340","observation_id":"018a7d51-b7e7-4e99-aed0-53a19e9e587f","resolution":{"observed_at":"2026-05-21T18:45:29.319259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0496.352740","doi":"10.1145/3470496.3527404","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lee, David Brooks, and Carole-Jean Wu","venue":null,"work_id":"c657dbfd-017b-4439-9dbb-c1d90e22f06d","year":2022},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:aea43b34a809caf825f0244bae2cf3d1f76e327b36141902ac97f4de9cf8b319","observation_id":"f3440b04-cada-4f8a-a95c-98fe51316354","resolution":{"observed_at":"2026-05-10T05:51:09.495835Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Plasticine: A reconfigurable architecture for parallel paterns","venue":null,"work_id":"82ce2bcf-c03e-4dd0-a8fd-825afa2bc2e8","year":2017},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:75c6a4692b4d59d3c7f4b6201bacff1ff6a31224af2335fd8b65f12fa08e11ce","observation_id":"db2e3795-7276-4a3e-bcb6-4ba406aae202","resolution":{"observed_at":"2026-05-21T18:45:29.323702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Sambanova sn10 rdu: Accelerating soft- ware 2.0 with dataflow","venue":null,"work_id":"09499c58-049b-4f47-987c-9a39e2bf0280","year":2021},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:392c0a0e085f519b6f7d85c543c2f84c8188082f8a0812bf593d1e6aa4de8b9f","observation_id":"38b3ad63-4ad1-49eb-a616-c7bfb60c1e24","resolution":{"observed_at":"2026-05-21T18:45:29.296205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Sambanova sn40l: Scaling the ai memory wall with dataflow and composition of experts","venue":null,"work_id":"e4e4d44b-dddb-485c-98b8-3f6d1aa1ed60","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:4b20a9a7fec359e66ab853ea08259cbedaaf2f721dec7d6cfe33060a64b4b612","observation_id":"94aae8ba-4717-4927-ae5b-b23b5c010c5e","resolution":{"observed_at":"2026-05-21T18:45:29.313783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1935.2024","doi":"10.1109/hcs61935.2024.10664673","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.1109/HCS61935.2024.10664717","venue":null,"work_id":"e963fe30-8a02-4eb3-bf90-0fa9cc9bacff","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:883079694f81924af5067e4bf99d92895b6a3b563c3dbefa03fe7b57ca6eea02","observation_id":"3195d33a-8c5d-4fe6-8b57-86f6d5d048cc","resolution":{"observed_at":"2026-05-10T05:51:09.501769Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Cerebras architecture deep dive: First look inside the hard- ware/software co-design for deep learning","venue":null,"work_id":"c0809d62-d2cc-4c32-915e-3936763b0ec0","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:948f9537546581e22d04eff7dc86b38d62a29342556c5dd779dae7404b1cfd49","observation_id":"7a081be5-4e62-4b71-bc67-08dc5b6a35cf","resolution":{"observed_at":"2026-05-21T18:45:29.301436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Tenstorrent scales ai performance: Architecture leads in data-center power efficiency","venue":null,"work_id":"73cdc851-a198-47b8-8bd0-0411ded7498a","year":2020},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:5cf11b51d1be686dddf2be97cffc6f68ff10611d22f414e40894d80304aa518b","observation_id":"a54ae7ed-f5d7-47fb-bf03-02cee1800e67","resolution":{"observed_at":"2026-05-21T18:45:29.294030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Blackhole & tt-metalium: The standalone ai computer and its programming model","venue":null,"work_id":"1e2994af-8187-4d89-b2f1-1929abecfc07","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:7bf005eadfea4f16c7b83fe4e08124145b22430d86540dde09a41f18c87b7711","observation_id":"2dbe784c-2d77-4764-95c7-2959f839fc79","resolution":{"observed_at":"2026-05-21T18:45:29.306278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"The microarchitecture of dojo, tesla’s exa-scale computer","venue":null,"work_id":"b6467bd6-6eec-4d9e-8ece-fc9c27f547b7","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:8113cfac89da833839135c7706d50f0da22aee433113ce5e700c74358bd3151e","observation_id":"27071b56-9a95-439e-978d-449ad19e16cb","resolution":{"observed_at":"2026-05-21T18:45:29.315968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Amd xdna™ npu in ryzen™ ai processors","venue":null,"work_id":"9bece1c3-70e6-4ca5-8d96-27caadcdce1e","year":2024},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:29440dcdf19181c0b84453a6c5b933de767ca68c477f93d463df5e7f34c8c410","observation_id":"f64352c9-7fb7-43f3-884f-528b6e25912c","resolution":{"observed_at":"2026-05-21T18:45:29.311114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Evaluation of xilinx versal architecture for next-gen edge computing in space","venue":null,"work_id":"2e272dd1-abe7-4491-83a4-f9520ec35424","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:9ee4532833e070063674d4be3a9538b9e7ef747976f8ca3fa411936efdfe8e4f","observation_id":"4b24a127-4ea6-40ee-9f4f-3b435919e049","resolution":{"observed_at":"2026-05-21T18:45:29.303841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Neu- ronflow: A hybrid neuromorphic–dataflow processor architecture for ai workloads","venue":null,"work_id":"926b541a-d238-434e-9b38-766d97efd666","year":2020},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:a33e211ceb20bbdf349f78e5138462363afb16df5079b45a5a02cbede2176ded","observation_id":"d3850fae-3ca8-4935-aeca-b10caef39fe0","resolution":{"observed_at":"2026-05-21T18:45:29.308698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-08T22:45:40.878024Z","title":"In-datacenter performance analysis of a tensor processing unit","venue":null,"work_id":"f2644ce3-b50a-4a01-94bc-dea0081cf41e","year":2017},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:d09cbdd942e9f7384d9965d3634d1288a172c4d5a3a69828defa1803d276286b","observation_id":"d27802ee-6c15-49dd-870b-6f3f673a96e6","resolution":{"observed_at":"2026-05-21T18:45:29.298983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware sup- port for embeddings","venue":null,"work_id":"2e634206-6139-4440-8dad-eff4dad462bc","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:0fd1f8084fb91f41b6db0c5ec66082a51f5da3db52422e147031ffa9ce55d701","observation_id":"96c02907-ceed-41e0-9fd5-61ef761fc99a","resolution":{"observed_at":"2026-05-21T18:45:29.330863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Mtia: First generation silicon targeting meta’s recommendation systems","venue":null,"work_id":"9b3fe1c9-9076-4a1d-b77f-25d154fc7898","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:1f76fde647f17d3e728d8f050e959d89b7b0c5d13d155694c923cdf0af539f18","observation_id":"ddf63aa9-833c-4d7f-a002-a5128eda4916","resolution":{"observed_at":"2026-05-21T18:45:29.328634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Meta’s second generation ai chip: Model-chip co-design and productionization experiences","venue":null,"work_id":"7934947d-074e-466c-bb2f-0a7c20ee694b","year":2025},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:fb6f767c8ccdf9939e0dab343b49be38067b09226691a224df795157dd413fee","observation_id":"31a987d7-b5e6-489b-8fe4-ed8f0cbc34e3","resolution":{"observed_at":"2026-05-21T18:45:29.282204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Pact xpp—a self-reconfigurable data processing architecture","venue":null,"work_id":"92367c02-883d-4c84-978e-c89b5e65436b","year":2003},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:d4b7519685ae913713ffad7914b7e21979da96160582a90a258d3572d9a910a0","observation_id":"8d7f4431-645b-4a59-adf3-dd9bc2127d26","resolution":{"observed_at":"2026-05-21T18:45:29.286933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Dynamically spe- cialized datapaths for energy efficient computing","venue":null,"work_id":"bf066b15-0d68-4994-9f1a-23c099b5ac9f","year":2011},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:8cdde9e442a33362257115c48e595984d578b8691cf71669a88fb0c1ebd7e9bd","observation_id":"435d6f18-b8ad-46bf-994a-4144036d3c80","resolution":{"observed_at":"2026-05-21T18:45:29.289461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Morphosys: an integrated reconfigurable system for data- parallel and computation-intensive applications","venue":null,"work_id":"4d939277-51f3-4f11-828a-ef2f2157c707","year":2000},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:ef30af28fb967d252042af8d6c8a52d6bed286925c45d619714f89be9c21ba74","observation_id":"de094dc8-d62f-45f9-903b-a339eac1435c","resolution":{"observed_at":"2026-05-21T18:45:29.284697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"The gpu computing era","venue":null,"work_id":"dd796533-9af8-4802-9455-2f30e1e95fcf","year":2010},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:8f41f1e937cd007f26acb1efcd4ee322dadbc1df3ef8800fa4403e9601895977","observation_id":"0106fb6c-3fb1-4e49-bf5a-fe5b33b0c36a","resolution":{"observed_at":"2026-05-21T18:45:29.340606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06826","last_updated":"2018-04-18T17:25:13Z","snapshot_observed_at":"2026-08-04T11:46:31.128381Z","submitted_at":"2018-04-18T17:25:13Z","title":"Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking","version":1},"cited_work":{"arxiv_id":"1804.06826","doi":"10.48550/arxiv.1804.06826","metadata_source":"pith","pith_arxiv_id":"1804.06826","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking","venue":"cs.DC","work_id":"8d6b9608-b887-4405-b38c-0e2725997256","year":2018},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/1804.06826","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:8258fe92071a3ec3f948622a92aa5bfb8eb937f0e50868838f7e1d1c5dc9518e","observation_id":"a43d099d-e40a-48ea-9626-59cd57442f6c","resolution":{"observed_at":"2026-05-10T05:51:09.774369Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"An implementation of the codelet model","venue":null,"work_id":"13e4e7ec-1f4e-474a-8e52-fc787386add5","year":2013},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:6d0ba93a9c53c9b153bb9348fe8030a94d5c20f5e8f341b97e2fea0cefbbfd36","observation_id":"8c24c562-d8be-42c5-a3af-8afda46244f0","resolution":{"observed_at":"2026-05-21T18:45:29.291502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"Earth: an efficient architecture for running threads","venue":null,"work_id":"f6375e79-9186-406d-b157-5b5e1c4eb2c6","year":1999},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:9f5000d5285db5874e908ad60c700407e4fd10565813a13c2c60add4725ab591","observation_id":"162a3342-7c45-47b1-b573-a4f0f4924a5e","resolution":{"observed_at":"2026-05-21T18:45:29.321461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-09T23:56:38.835071Z","title":"Planning-oriented autonomous driving","venue":null,"work_id":"fabc6632-1330-4ab3-874a-63607f047d7b","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:dec33ca91b254ae30001cf1dc8b2a180caee3877397e1e4bb26e3de6146be592","observation_id":"4bf4b323-9a9d-4782-b2c2-5f6420ef8d5a","resolution":{"observed_at":"2026-05-21T18:45:29.346532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-02T11:57:18.735747Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":"2307.09288","doi":"10.24963/ijcai.2025/706","metadata_source":"pith","pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","venue":"cs.CL","work_id":"68a5177f-d644-44c1-bd4f-4e5278c22f5d","year":2023},"citing_paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T05:50:06.986471Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2604.17862"},"observation_digest":"sha256:c7a091168ca02902619479f59fe383062449418129b4d8b517bdf50ed63bd74d","observation_id":"945bba57-f30a-4d50-a7a0-df97e0f23fb6","resolution":{"observed_at":"2026-05-10T05:51:09.771507Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.17862","last_updated":"2026-04-20T06:19:30Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:04:50.935335Z","submitted_at":"2026-04-20T06:19:30Z","title":"M100: An Orchestrated Dataflow Architecture Powering General AI Computing"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":0,"verified_exact":6,"verified_fuzzy":30},"total_outbound_references":38},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2604.17862."}