{"as_of":"2026-08-20T23:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:647980eaf712c20ee6e44332bfe3396ab5332e1f8c371a0272ab5df9a4e21f5e","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T01:07:27.426260Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2506.12210/citation-record","integrity":"/paper/2506.12210/integrity","json":"/paper/2506.12210/citation-record.json","paper":"/paper/2506.12210"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:28.371988Z","title":"Efficient processing of deep neural networks: A tutorial and survey","venue":null,"work_id":"a711eaa8-06ec-4405-9e74-276036567adf","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.598710Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:810d8092426a7b52d3107bc3f2ec452d87dc90e6f0c14c1102906ccf8069e65e","observation_id":"e1f9acad-cc37-47c3-9dd5-0d338a825c6d","resolution":{"observed_at":"2026-08-07T01:07:28.376017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.361509Z","title":"Computing’s energy problem (and what we can do about it)","venue":null,"work_id":"20ba77f3-6f02-467d-a933-18e0dda013ad","year":2014},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.635056Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:a7c37231661cc80cf18477ecf8ccd70b9e40557e78d2ef1d0fcecd23a2c79198","observation_id":"cfb7f3b5-a6d2-4441-9e93-f55933096f66","resolution":{"observed_at":"2026-08-07T01:07:28.364661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.351350Z","title":"In-datacenter per- formance analysis of a tensor processing unit","venue":null,"work_id":"a0b2fb7d-7f78-414d-b925-b57facd15b9b","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.748555Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:398bf8a38e11bc21976e9730a55d67bee928029c0fbcc78ef830eeffdff4f7da","observation_id":"3b09e82d-2fc2-43fd-830f-ea0a15b08a8e","resolution":{"observed_at":"2026-08-07T01:07:28.354518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.340213Z","title":"The emergence of edge computing","venue":null,"work_id":"48dccbae-143e-4aac-a11f-1f2892ffdec0","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.835838Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:838cbdf4cd56457d7b7b5a535fdca346952ee0842f5ca10e4585bdea397d071e","observation_id":"17026e21-f437-4652-bc5e-4381da86e1c5","resolution":{"observed_at":"2026-08-07T01:07:28.343883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.330049Z","title":"A method to estimate the energy consumption of deep neu- ral networks","venue":null,"work_id":"287e7d21-a3d9-46fb-a09a-cec252401e09","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.899305Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:8ac1683e5b18dc6662765abfe097342c9457b2f6d530b2e549ffd68c5c47d74f","observation_id":"d77e2563-a226-4b8d-9177-5b13e7699253","resolution":{"observed_at":"2026-08-07T01:07:28.333433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.00564","last_updated":"2018-12-03T05:43:20Z","snapshot_observed_at":"2026-08-14T23:16:38.988686Z","submitted_at":"2018-12-03T05:43:20Z","title":"Split learning for health: Distributed deep learning without sharing raw patient data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.00564","snapshot_observed_at":"2026-08-07T01:07:22.963609Z","title":"Split learning for health: Distributed deep learning without sharing raw patient data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:22.963609Z"},"links":{"cited_paper":"/paper/1812.00564","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:2d8f6c7f6ae6982a7dd63e66f4b8d51769eabec0e19c95c96fe43c99b11bc8fa","observation_id":"b77e87c0-3ba0-4905-b674-5dee9941da05","resolution":{"observed_at":"2026-08-07T01:07:22.963609Z","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":"2026-08-07T01:07:28.318825Z","title":"Multi-key privacy-preserving deep learning in cloud computing","venue":null,"work_id":"cb6464dc-e661-49e6-a25b-23bd7c6b073a","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.035008Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:45efb9c2e2297e603021b96988da4145981abea16d0fe6981f263bc47ed6c748","observation_id":"e18bbb3a-384b-402a-bf88-61a8b112a1cb","resolution":{"observed_at":"2026-08-07T01:07:28.322750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:23.083418Z","title":"Quantum-secure multi- party deep learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.083418Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:40fabd87ebd02830554bc19cf224d277204f0d1c17725227794b76a0d91b1848","observation_id":"164b3faa-d299-4a61-8488-7c21bb384e3c","resolution":{"observed_at":"2026-08-07T01:07:23.083418Z","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":"2026-08-07T01:07:23.137141Z","title":"Wright, Peter L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.137141Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:0768792270b1ac2d418f86874c52dc99b316e155ad71a09e6aec23a8e91fb0f4","observation_id":"481c50ac-ffa8-4261-a388-fdc5e8e9575b","resolution":{"observed_at":"2026-08-07T01:07:23.137141Z","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":"2026-08-07T01:07:28.302372Z","title":"Backpropagation-free train- ing of deep physical neural networks","venue":null,"work_id":"9ee3f607-033d-4112-b4a7-d6f14d4c9141","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.208729Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:0c774205d723144d093c6f4ce84b912c4c9caeb2476bf29a9233d4272a12e58e","observation_id":"42a5bba5-1f30-445c-ae31-c26582090cc5","resolution":{"observed_at":"2026-08-07T01:07:28.305432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:23.276781Z","title":"Deep physical neural networks trained with backpropagation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.276781Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:0c7695be9889103b70a9dc51b38c619ea9530df7a28f85c96d94af4130cc5bb3","observation_id":"d85c1b95-7cc8-4e0c-87bb-f8db581025c0","resolution":{"observed_at":"2026-08-07T01:07:23.276781Z","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":"2026-08-07T01:07:28.284629Z","title":"Inference in artificial intelligence with deep optics and photonics","venue":null,"work_id":"7f3f6c3f-1f80-4a87-8d1b-84b7c0a40438","year":2020},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.342941Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:b8755873e2b92632999fe06ce8419a8a924a7d21599ae91d6365fa8098994119","observation_id":"28ae8c3a-c2e2-4617-a515-53e07b987723","resolution":{"observed_at":"2026-08-07T01:07:28.287805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.274303Z","title":"Memory devices and applications for in-memory computing","venue":null,"work_id":"fc980883-89ee-4add-a041-3ab489488d2b","year":2020},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.408095Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:c910864beda360bcab75c9db4b0eaf66b5d2d39f88e4cff2cf61d7b6b923bc76","observation_id":"045869dd-f08d-426b-8be0-6df63a3df79b","resolution":{"observed_at":"2026-08-07T01:07:28.277551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.263970Z","title":"Photonics for artificial intelligence and neuro- morphic computing","venue":null,"work_id":"c4386f58-b4e2-4d05-b5c8-65c6696ef20d","year":2021},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.480911Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:f755534dbd506f8e1ed232ddc3fa72b4643e03e3e0f90a3aca863e45ef37eb59","observation_id":"63c6cce6-75d2-4093-8dbc-9bbef0e8f77b","resolution":{"observed_at":"2026-08-07T01:07:28.267204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.253473Z","title":"Fully forward mode training for optical neural networks","venue":null,"work_id":"9c74006f-62cd-4156-abe5-f7c2cdaa97aa","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.553035Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:5f770d6d2c2ae3c07e76d984d5e09d722128551fbbf98debdcf65fee41bbae22","observation_id":"83e27fb6-e2c5-470d-802e-1e9d8c0c94bf","resolution":{"observed_at":"2026-08-07T01:07:28.256799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.243299Z","title":"Nonlinear processing with lin- ear optics","venue":null,"work_id":"89ca878a-c15d-4060-b05b-2dfbc1a9c9fd","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.618441Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:2ac22450b0a0e19fba12c0c8ac30e3b772be997956a3b45ed30f4cbf6bdd6933","observation_id":"bc2c192c-8414-491a-99cb-4912882123ca","resolution":{"observed_at":"2026-08-07T01:07:28.246775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:23.670683Z","title":"The physics of optical computing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.670683Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:95e8f34de728b4557c1ff051707ebf961c5071baa98270c2008f433b0218fd8a","observation_id":"88bf81bf-1a49-4d76-a16e-d1ee4a2a4f6f","resolution":{"observed_at":"2026-08-07T01:07:23.670683Z","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":"2026-08-07T01:07:28.225958Z","title":"An on-chip photonic deep neural network for image classifica- tion","venue":null,"work_id":"bd6181e7-7d51-4807-89dd-a1720150cdf2","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.735221Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:8d0b70a6f57a1436e7e984ac7221323660e33dbdc86b08b51395bf55b68d8475","observation_id":"46fac8e6-3201-4dde-9dd5-7eb2c91c40d0","resolution":{"observed_at":"2026-08-07T01:07:28.229311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.215814Z","title":"Parallel convolutional processing using an integrated photonic tensor core","venue":null,"work_id":"f592d605-1858-496b-99ef-7a1c36730a8b","year":2021},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.804565Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:0ff2fd2f6f2abf0c88687b8844b51172bd3b9182e13047a0cbf5f8333af6fa87","observation_id":"56db5db3-37c0-4cc5-be06-b7e2af1716e0","resolution":{"observed_at":"2026-08-07T01:07:28.219066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.206092Z","title":"Deep learning with coherent vcsel neural networks","venue":null,"work_id":"d3e98cbf-516c-48c2-9008-95a698e47c2b","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.871064Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:3bc4bb0e72e76ff2229d074ff8c9ebf7550106ee056609a491aa591aa6898221","observation_id":"aedad37c-f50d-44ef-893c-fc5422f632a1","resolution":{"observed_at":"2026-08-07T01:07:28.209535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.196949Z","title":"An optical neural network using less than 1 photon per multiplication","venue":null,"work_id":"4663d689-dbd5-48c8-bc99-0c968d6feb10","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.933094Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:41b5fcc6ca68b0c44cb2ad4c747c3e002b3bd917daa9cb83991eccc006e7e05f","observation_id":"36c38793-7dc1-4f5a-8d65-cb63c8a158ee","resolution":{"observed_at":"2026-08-07T01:07:28.200014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.186540Z","title":"11 tops photonic convolutional accelerator for optical neural networks","venue":null,"work_id":"e4d73ca1-b4f7-40a3-a41d-aeab7b6e7553","year":2021},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:23.997867Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:468c522950524f0e6c8882fadc3a42f1df88454dc143470b4ff2f1dacaef4839","observation_id":"48315775-2a58-4a7e-be22-c161cac52c92","resolution":{"observed_at":"2026-08-07T01:07:28.190127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.176967Z","title":"Programmable photonic circuits","venue":null,"work_id":"d2ea80f6-dc32-4a59-a956-4b0f48fc8c40","year":2020},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.065282Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:7682dac5fd3a46f6a204a7ddbc57809213a35faa32dd83803d9e7492291cf802","observation_id":"e7cad960-364d-4393-814a-ecc22c489763","resolution":{"observed_at":"2026-08-07T01:07:28.180012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.166160Z","title":"A three- terminal nanophotonic integrator for deep neural networks","venue":null,"work_id":"93a77056-ab45-4670-91e8-22d4c75e4ac2","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.131365Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:617b72b49b24525efcce5e80969e635afc9064fdd0c59dab4ebabd0cacbbdd76","observation_id":"05ffdacd-0318-4973-b739-fe673e29f5a2","resolution":{"observed_at":"2026-08-07T01:07:28.170034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12305","last_updated":"2024-09-20T02:50:39Z","snapshot_observed_at":"2026-08-16T13:17:22.150271Z","submitted_at":"2024-09-18T20:30:19Z","title":"QAMNet: Fast and Efficient Optical QAM Neural Networks","version":2},"cited_work":{"arxiv_id":"2409.12305","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.12305","snapshot_observed_at":"2026-08-07T01:07:27.509726Z","title":"QAMNet: Fast and Efficient Optical QAM Neural Networks","venue":"cs.ET","work_id":"ef8f2535-d0c7-4a0f-a14a-e2f7e9718286","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.171976Z"},"links":{"cited_paper":"/paper/2409.12305","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:34874cf4f19b06d9549c5b22b79deeb6c2e7742f4678d806fdf165792d61d1b7","observation_id":"0b644ee3-f9f6-4958-9509-771b0a5648ef","resolution":{"observed_at":"2026-08-07T01:07:27.513150Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.156486Z","title":"Attojoule optoelectronics for low-energy information processing and communications","venue":null,"work_id":"ecbee7da-d028-4804-9987-cef9f974087f","year":2017},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.241502Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:ca48a4c8d7d8788755080cfe07f541e2718fd5ab288b186c87208ea6483a3f5f","observation_id":"5e46ff03-42e9-4fa6-a2c8-84bb940da7c0","resolution":{"observed_at":"2026-08-07T01:07:28.159866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.146049Z","title":"Analog optical computer for ai inference and com- binatorial optimization","venue":null,"work_id":"d1fc5c60-fa3b-45c8-9e6a-73ae31613dda","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.323393Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:04e08394a47d1d4a3115d99cc7c25aaa40134a5ef0c5785291d6120604e04e76","observation_id":"c3cd2d3c-a271-4745-89cc-9851d9464169","resolution":{"observed_at":"2026-08-07T01:07:28.149497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.135861Z","title":"Large-scale photonic chiplet taichi empowers 160-tops/w artificial general intelligence","venue":null,"work_id":"2bd41bc1-8d54-4eae-9674-155048d24a6b","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.473823Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:b6b638b7ffaca10da1e451c1523e355a76b47c22fc0126cbc209acefd51aab61","observation_id":"6dde5f69-6efe-47b8-a5a8-e93658f0fc60","resolution":{"observed_at":"2026-08-07T01:07:28.138854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.126855Z","title":"All-optical ma- chine learning using diffractive deep neural networks","venue":null,"work_id":"c2c5336e-1f22-4bea-9061-1a35081248f9","year":2018},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.560349Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:971051abb0bc9d3ba7bf67de9953745f1735a41b49d490e11f3c9fcf81792fae","observation_id":"d2ba3811-ca3a-4e8f-a4fe-f18c53d93509","resolution":{"observed_at":"2026-08-07T01:07:28.130015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.116546Z","title":"Isaac: A convolutional neural network accelerator with in-situ analog arithmetic in cross- bars","venue":null,"work_id":"8cb7ddfd-7def-4d7d-ab3d-cf440e2c186c","year":2016},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.685496Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:36a8dca1a03f5f01a21fc6f1007e18a68c10c0c319db54633a4f4e82ce3593dd","observation_id":"3b38a61c-aa0a-4d49-8784-eff4c6b4c688","resolution":{"observed_at":"2026-08-07T01:07:28.120142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.106375Z","title":"Prime: A novel processing-in-memory architecture for neural network compu- tation in reram-based main memory","venue":null,"work_id":"33c0616d-5b92-434b-8549-e8c537e49a3e","year":2016},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.690460Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:060c3bc99a413f7d8fdf9e07d189c065d597dc5614e4a8bc3410b1ada0bca813","observation_id":"04465b8b-0992-4f0e-b631-9637c769a6c9","resolution":{"observed_at":"2026-08-07T01:07:28.109881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.096165Z","title":"PUMA: A programmable ultra-efficient memristor-based accelerator for machine learning inference","venue":null,"work_id":"33dfe4ac-be71-4e07-a5da-b97c49b05611","year":2019},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.745489Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:80d6b4ca63d7a8548c9ff88cd3e9c61e0528eae662d106b0d1aaeec102e96c1f","observation_id":"9b2bcb70-9e16-4bf4-b2a9-7acebbd3d79e","resolution":{"observed_at":"2026-08-07T01:07:28.099691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.085823Z","title":"A computing-in-memory macro based on three-dimensional resistive random-access memory","venue":null,"work_id":"64d343a0-681f-40a3-a406-e6faa2350468","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.853571Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:bd8458bdd42e9039f00a43be09cf1faf20c67b9deca8d61fd0e9e50f29d50dda","observation_id":"29ddc0de-f928-488f-90f3-3a1c8f65798d","resolution":{"observed_at":"2026-08-07T01:07:28.089635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.076196Z","title":"Fast and robust analog in-memory deep neu- ral network training","venue":null,"work_id":"f1fe2eb8-3a48-44eb-8a71-3198d16016c3","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:24.956457Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:f0e8c2413e9f22a34132b79d9a3fe677d0128566f29234884b4003c6f3b8f59a","observation_id":"78102055-c7b8-43b4-8045-c453bf4f6911","resolution":{"observed_at":"2026-08-07T01:07:28.079286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.066425Z","title":"The inherent adversarial robustness of analog in-memory computing","venue":null,"work_id":"97e5d593-6eee-4e78-b5c4-dd99b6645e92","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.061144Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:00aa7d9a7133deb6e2d61ab4f985444b456625eeb2cdbffc4390d4a3824e9c85","observation_id":"529b91d9-0a4c-4dca-a290-c250f123c82e","resolution":{"observed_at":"2026-08-07T01:07:28.069722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.055732Z","title":"In-memory computing with resistive memory circuits: Status and outlook","venue":null,"work_id":"d837b2f7-8034-4b68-ad87-8ca5aae8c85e","year":2021},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.140283Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:4f78f2bc386c27352e1408dfe80336fd160f7993ad2bf1f267f910963e762c87","observation_id":"2b629d85-83d9-456b-bef0-7c2b4769485f","resolution":{"observed_at":"2026-08-07T01:07:28.059361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.045259Z","title":"A crossbar array of magnetoresistive memory devices for in-memory computing","venue":null,"work_id":"aab87b76-e549-4c0c-a584-3223f0bbd9bd","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.212883Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:dcc93ea20a11ddd0572feea3058723d11050df1f03611686a7d115a207b4b6fd","observation_id":"d97c3cb8-4cb8-4ae5-889c-10d2d0477e22","resolution":{"observed_at":"2026-08-07T01:07:28.049068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.035768Z","title":"A 2941-tops/w charge-domain 10t sram compute-in-memory for ternary neural network","venue":null,"work_id":"c4dc9c44-9ca9-4eba-a502-40a44b661a40","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.295312Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:6407a742b07e7b1001f842ca9ba6b18af173780d06ec05051252f17b2e017869","observation_id":"ea4c213e-a6ed-4da2-8635-7aa45122983b","resolution":{"observed_at":"2026-08-07T01:07:28.038691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.025346Z","title":"Dou- ble mac on a cell: A 22-nm 8t-sram based analog in-memory 13 accelerator for binary/ternary neural networks featuring split wordline","venue":null,"work_id":"0926feab-4344-4b27-a11c-0061038769d1","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.388568Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:4ea41820d789abe10525eefbeff08f62d2a922dde6da14741425f08dc4af6526","observation_id":"cb7c2692-af22-4c14-b447-dfac4deb8f42","resolution":{"observed_at":"2026-08-07T01:07:28.029051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.015258Z","title":"Power-efficient combinatorial opti- mization using intrinsic noise in memristor hopfield neural networks","venue":null,"work_id":"dbb2005e-836c-4b5c-85d0-55d609e5556d","year":2020},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.544438Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:291b2b9dede3c6ac87d72f74806e3716ae3693f553f6281ad043ac8f86b81ccd","observation_id":"ff43301d-94a4-419a-b74c-42a1c134ab4d","resolution":{"observed_at":"2026-08-07T01:07:28.018578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:28.004791Z","title":"A 64-core mixed-signal in- memory compute chip based on phase-change memory for deep neural network inference","venue":null,"work_id":"71766a6e-ad70-405c-bc6d-bd98026b7ee5","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.685336Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:feb4c1289a92dc38bfaa1b103ad36982e0b4976d0ad963e7a25ccd052d59f0b0","observation_id":"65670fca-5391-4a74-bd2e-e3bb8036b44e","resolution":{"observed_at":"2026-08-07T01:07:28.008506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.995645Z","title":"Multpim: Fast stateful multiplication for processing-in- memory","venue":null,"work_id":"19f8fdd3-333e-4fe9-a90c-ace41c5d7df5","year":2021},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.806526Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:6321e0b57f4533fb949651a44121929d3ed5d637ad79367e644a791c199034f5","observation_id":"8d0bcc0d-bd7d-4217-9b80-39ff3fb592f6","resolution":{"observed_at":"2026-08-07T01:07:27.998895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.986647Z","title":"Mat- pim: Accelerating matrix operations with memristive stateful logic","venue":null,"work_id":"72b6c9f2-66e7-4b07-90ac-be5edd49f004","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:25.929144Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:09d6eb0a01e536de188ef93357db2258fca54f77aa7904effea036ba7a35f56f","observation_id":"5d2814ca-58d5-4799-a5a9-8276c68e2100","resolution":{"observed_at":"2026-08-07T01:07:27.989663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.977399Z","title":"Magic—memristor-aided logic","venue":null,"work_id":"88670ed5-edbc-444f-bfe1-dff390b6c5ce","year":2014},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.050854Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:ead512fe5519369b7f106719f8830f8454f47517228dfd2c7f4adc483d0ca55d","observation_id":"73aeb4ec-a0b9-4175-aa67-8c815d00c4d3","resolution":{"observed_at":"2026-08-07T01:07:27.980283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.967475Z","title":"Quantized neural networks: Train- ing neural networks with low precision weights and activa- tions","venue":null,"work_id":"f7521e5d-bca8-4aa1-9384-e6cd32137807","year":2018},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.137092Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:7348811545c971fc5e4da1ce143cc5cdd2b08f7771a4b0addc6e310abace5907","observation_id":"18239d83-9294-40a9-a9ac-8217572b8390","resolution":{"observed_at":"2026-08-07T01:07:27.971008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.958012Z","title":"Binarized neural networks","venue":null,"work_id":"09b1f1d1-73fe-40a8-bf95-9f1c9505b2a6","year":2016},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.247123Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:6204fd6f4f97d2905121306309f205f4a442f93d9e8b7a27093344d06b56f7bd","observation_id":"296cbfec-c92c-4867-9c7d-9af13c634bb0","resolution":{"observed_at":"2026-08-07T01:07:27.960822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.947308Z","title":"Powering ai at the edge: A robust, memristor- based binarized neural network with near-memory computing and miniaturized solar cell","venue":null,"work_id":"f213176d-4b2c-43fa-8991-d15ed3253012","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.388015Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:537cc58fc7b96fa980838cb6d2e77ea06441721aa0b7fa0c3944d9f9e8703c07","observation_id":"490e3062-342b-4647-8d11-45321bd1003b","resolution":{"observed_at":"2026-08-07T01:07:27.950755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.938005Z","title":"Computation over multiple-access channels","venue":null,"work_id":"d2d6442a-cea0-4a2f-8413-67a0642a2a56","year":2007},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.488535Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:cbdef5b554f08e53a409c1901f7b90ba754f9fdb5fdacf9186d3a89eec58ad63","observation_id":"dc8955e7-c9a6-4c90-8bac-a6b69633efa6","resolution":{"observed_at":"2026-08-07T01:07:27.940947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.928893Z","title":"Robust ana- log function computation via wireless multiple-access chan- nels","venue":null,"work_id":"3db99504-ed03-4b75-ba80-a33f088df549","year":2013},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.607509Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:8003827b4076a9a97735f1a32dbf013c1511e62eb42a6422866c19fea9086e4b","observation_id":"25df58de-0bdf-4796-afce-571b5fbaf85b","resolution":{"observed_at":"2026-08-07T01:07:27.932099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.918372Z","title":"Nomographic functions: Efficient computation in clustered gaussian sensor networks","venue":null,"work_id":"5a7e5e23-51d0-474e-8a2f-800a1d013c80","year":2014},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.730869Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:e25497ffc77b21ad39e3f29cabb8b5c57469680b5e29804adb13612ade29a4cf","observation_id":"4a6b8649-f64e-4048-be0c-cf0cd68d9fa9","resolution":{"observed_at":"2026-08-07T01:07:27.921716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.907706Z","title":"A survey on over-the-air com- putation","venue":null,"work_id":"fb96d9f6-e498-4026-a6ba-98e9f685b7d9","year":1908},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.821222Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:afec7416d3c940002c59f5868cb8a9246ce339f2ba808887a419d99fff1b0b31","observation_id":"60779c29-1c4b-4bcc-a51a-c1034f2246d8","resolution":{"observed_at":"2026-08-07T01:07:27.911118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.897462Z","title":"AirNN: Over-the- air computation for neural networks via reconfigurable intel- ligent surfaces","venue":null,"work_id":"5e8a52b7-7374-422e-9067-3106e21097db","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:26.980471Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:a26b4a1079432d2e2c26b035f17ac9eae35987f4626ffbafb3502b069db7d8ac","observation_id":"58a7e4de-1607-4052-b84d-450ea25d5bfc","resolution":{"observed_at":"2026-08-07T01:07:27.901195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.886766Z","title":"AirFC: Designing fully connected layers for neural networks with wireless sig- nals","venue":null,"work_id":"87c56252-c66c-4dd2-9ade-f4e085a0785f","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.106196Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:0b293fee31b0c906507cfef31fa0d69cc2c4e7ade11235babb2f03d1abb04d07","observation_id":"959b11a2-71c5-4967-8daa-aa0b4faf3898","resolution":{"observed_at":"2026-08-07T01:07:27.889690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.783377Z","title":"Wireless distributed matrix-vector multiplica- tion using over-the-air computation and analog coding","venue":null,"work_id":"4ec0b7c5-2036-4d54-ac29-d600e4e5fde0","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.256854Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:efb91d70fe8ac9cb590d42b2c86bf89cde44f1ec174258e891801786c9125374","observation_id":"be82e15d-a05e-4650-b84d-118ad3312b3c","resolution":{"observed_at":"2026-08-07T01:07:27.879231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12758","last_updated":"2026-04-10T05:12:28Z","snapshot_observed_at":"2026-08-15T05:26:19.947808Z","submitted_at":"2025-04-17T08:53:30Z","title":"Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.12758","snapshot_observed_at":"2026-08-07T01:07:27.367161Z","title":"Uni- versal approximation with xl mimo systems: Ota classi- fication via trainable analog combining","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.367161Z"},"links":{"cited_paper":"/paper/2504.12758","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:7339bc4eedd8742a2c5b27efe8e4fa8ec98076d4afcce1b82e5bdf5532ade327","observation_id":"31c3919a-bc13-442a-b5c8-d38dca280e47","resolution":{"observed_at":"2026-08-07T01:07:27.367161Z","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":"2026-08-07T01:07:27.773226Z","title":"Computing functions over-the-air using digital mod- ulations","venue":null,"work_id":"f2363e95-0136-4b9d-af74-53e8722eef37","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.371471Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:c3d53cfa04dcebcbf9cf3796e0ebe2dd8b1e28d4fb87df7342a682bf103ae3c0","observation_id":"ec85f7bf-ba7b-4773-bd59-f7bde312fb8e","resolution":{"observed_at":"2026-08-07T01:07:27.776752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.762404Z","title":"A reconfigurable linear rf analog processor for realizing mi- crowave artificial neural network","venue":null,"work_id":"27495680-3db5-4823-aafa-66aa15a6fbc9","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.374928Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:4d3c9149138177dbcc8cb980defb27d140c83cf799adf23a5eb118491dce9112","observation_id":"8820fbe4-2ad5-47c8-a184-f771131fffcc","resolution":{"observed_at":"2026-08-07T01:07:27.766112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.751982Z","title":"An integrated microwave neu- ral network for broadband computation and communication","venue":null,"work_id":"d6914e04-4535-4320-a1d1-454f442553a2","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.377537Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:2680c8598ed722dad53394e8a6c603d63b5b69c4400877bcd9241e4166d437fd","observation_id":"d673082d-ec16-41d3-a71b-3d018a3db7a3","resolution":{"observed_at":"2026-08-07T01:07:27.755214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.741934Z","title":"Mi- crowave signal processing using an analog quantum reservoir computer","venue":null,"work_id":"ae2df111-3cb2-450f-a89d-34b0959ef750","year":2024},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.380669Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:a326f69ec2951a24c571565bdaacd7f59ab69c94003d8be2db17ff5587350649","observation_id":"dbd121fb-ee39-48c9-92e3-cad4d3f2b94b","resolution":{"observed_at":"2026-08-07T01:07:27.745073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.731420Z","title":"A precise four-quadrant multiplier with sub- nanosecond response","venue":null,"work_id":"a51fd2d9-046a-4ced-8f09-266ed300dd40","year":1968},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.384075Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:6b66b05c44a6d879b6eaf0f4e85a32ce830b869cab83c7e5500dad6bd84e3f57","observation_id":"b0e801f1-facb-42e9-800e-4edf65cc1b56","resolution":{"observed_at":"2026-08-07T01:07:27.734736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.720172Z","title":"Low voltage per- formance of a microwave CMOS Gilbert cell mixer","venue":null,"work_id":"0d47028a-d940-4bc8-b28c-d5ac36ae4d0d","year":1997},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.387512Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:fc2a915cb183d227ebfc4816f72b875cf9f7716bee93d744dfb70ea0a29acc94","observation_id":"2d980232-6cab-442f-b6d3-9d02ee17ef58","resolution":{"observed_at":"2026-08-07T01:07:27.723139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.709122Z","title":"Coaxial frequency mixer, 300–4300 MHz","venue":null,"work_id":"086ddff4-c3ae-45de-b266-274508ec5db1","year":null},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.390470Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:47a74a318fe2781ae2a562ea083636614e644195095af0d69079448141e6a484","observation_id":"32e81438-02c0-4c10-8102-6ee80cf5fd2b","resolution":{"observed_at":"2026-08-07T01:07:27.712211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.698771Z","title":"Reconsidering os memory optimizations in the presence of disaggregated memory","venue":null,"work_id":"207aa6b5-3f4a-46cf-8c00-5b9fd115c5e6","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.393734Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:cdabd06fd09fa03eb7bd991c7b0de8bff1cfceddbcb3b9f4d9f888f5fa337d72","observation_id":"b6601a47-6bb3-4119-b1c6-eb6daf8cef76","resolution":{"observed_at":"2026-08-07T01:07:27.702246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.689107Z","title":"Thermal agitation of electric charge in con- ductors","venue":null,"work_id":"8b0c7b0d-1b28-454d-a669-26eb24f4896f","year":1928},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.397266Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:ba12e776d1541d958ec89fabcc17e3b4adcf9ad5e11dde45955176bbb8f86cd5","observation_id":"8c4cc3de-58d6-43e4-94ca-ddbc0d5d9d9b","resolution":{"observed_at":"2026-08-07T01:07:27.692302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.677191Z","title":"Irreversibility and generalized noise","venue":null,"work_id":"397af347-298d-434e-9b7e-995a8c807f79","year":1951},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.400554Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:1de68a27fd6c7f04b3838a07afd21114843be7f9d81b29df30eb1f31c238cb7e","observation_id":"bc80d65a-cbde-4d9e-afca-3066e187ea33","resolution":{"observed_at":"2026-08-07T01:07:27.681345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.17752","last_updated":"2025-04-24T17:10:18Z","snapshot_observed_at":"2026-08-16T10:29:45.607070Z","submitted_at":"2025-04-24T17:10:18Z","title":"Disaggregated Deep Learning via In-Physics Computing at Radio Frequency","version":1},"cited_work":{"arxiv_id":"2504.17752","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.17752","snapshot_observed_at":"2026-08-07T01:07:27.484525Z","title":"Disaggregated Deep Learning via In-Physics Computing at Radio Frequency","venue":"cs.ET","work_id":"02b44d92-1cc5-43f4-aca5-f6c20fcdad62","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.403999Z"},"links":{"cited_paper":"/paper/2504.17752","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:abe316c3a74f993a4a0059fc3312a383b95664813ba290b9c9a8032f0cceb7b4","observation_id":"de4b6638-6a0f-4a82-b1a7-0496811e0409","resolution":{"observed_at":"2026-08-07T01:07:27.487817Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.666258Z","title":"A 0.75-million-point fourier-transform chip for frequency-sparse signals","venue":null,"work_id":"bf3e19a5-6e66-4dc0-bf77-1a253c72e39c","year":2014},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.407437Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:f187a684c0cbfff7331f286e65dfcac479b058f937571511dedc95a0133e2572","observation_id":"061b4afb-20eb-4460-b626-2f9293a959fe","resolution":{"observed_at":"2026-08-07T01:07:27.669407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.654958Z","title":"An IEEE 802.11a/g/p OFDM receiver for GNU Radio","venue":null,"work_id":"cce6fa40-790a-4062-81fc-10fd897f887d","year":2013},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.410418Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:f92f5f82f65854824255b2690e9dcb2379620b1ff9b86557831384754435253a","observation_id":"529cd0b9-eefa-40bb-b910-4011f3dff1d9","resolution":{"observed_at":"2026-08-07T01:07:27.658138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.643460Z","title":"Ex- perimentally realized in situ backpropagation for deep learn- ing in photonic neural networks","venue":null,"work_id":"c85bc3a1-c571-4231-9411-665be443b8f9","year":2023},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.413629Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:a0e4eb99c76d7f6f638f80bd64bb7e11a9837af5ea7b0ff5a6c59739538693a7","observation_id":"3e0637bb-7c38-4e23-b144-d12d1389755c","resolution":{"observed_at":"2026-08-07T01:07:27.647455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T01:07:27.631488Z","title":"Large-scale optical neural net- works based on photoelectric multiplication","venue":null,"work_id":"217b086f-5d95-452b-9c43-fbad5b8aacd8","year":2019},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.416725Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:53393a7ec5e1ea51578bcc1702271008d8efcefb43e11dadec49bcefa90b8d60","observation_id":"138fc9bf-5fa2-4480-ba80-7274a09e46f9","resolution":{"observed_at":"2026-08-07T01:07:27.634979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.06883","last_updated":"2024-06-06T21:32:35Z","snapshot_observed_at":"2026-08-16T16:47:20.009347Z","submitted_at":"2022-07-08T16:37:13Z","title":"RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement","version":2},"cited_work":{"arxiv_id":"2207.06883","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.06883","snapshot_observed_at":"2026-08-07T01:07:27.467115Z","title":"RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement","venue":"cs.ET","work_id":"d1683013-7f8c-4c82-8ab5-5739c0e94a9d","year":2022},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.420040Z"},"links":{"cited_paper":"/paper/2207.06883","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:af1257a7b7f4ccd29870066e770213027fea42ef73c98520dadfbb5820eebc7a","observation_id":"35944593-9b34-46cc-9269-b4703ae01d5c","resolution":{"observed_at":"2026-08-07T01:07:27.473373Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-07T01:07:27.423313Z","title":"Layer normalization","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.423313Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:880a7ff77a589ad1baf80d53d95e6447f7567d1e627b7e0914e828d44b4a12e1","observation_id":"7c930411-928f-4de0-92da-46a9e07a8e15","resolution":{"observed_at":"2026-08-07T01:07:27.423313Z","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":"2026-08-07T01:07:27.621049Z","title":"Transformers without normalization","venue":null,"work_id":"9bca030a-73ea-4d91-866b-b3b393b11ada","year":2025},"citing_paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:27.426260Z"},"links":{"citing_paper":"/paper/2506.12210"},"observation_digest":"sha256:3411dceeaeba3dec86b97b2f53bf933f40a5e8a1fe3dafc1a9648f721fa7fbb1","observation_id":"f9885473-4266-424c-a9b6-e95a5b43e305","resolution":{"observed_at":"2026-08-07T01:07:27.624341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.12210","last_updated":"2025-09-04T05:14:04Z","latest_version":2,"primary_category":"cs.ET","snapshot_observed_at":"2026-08-10T11:23:21.604590Z","submitted_at":"2025-06-13T20:26:30Z","title":"Machine Intelligence on Wireless Edge Networks"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":3,"verified_fuzzy":63},"total_outbound_references":73},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2506.12210."}