{"as_of":"2026-08-14T17:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:abb472d0a27b06e799726fa0d4c4883f593c7f71b3411354b834f9262ba130da","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-16T12:01:57.764416Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T06:23:49.990308Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-21T06:24:00.461907Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"cited_work":{"arxiv_id":"2601.16118","doi":null,"metadata_source":"pith","pith_arxiv_id":"2601.16118","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","venue":"cs.AR","work_id":"70807938-fb81-4258-a416-66e4931bc203","year":2026},"citing_paper":{"arxiv_id":"2604.14411","last_updated":"2026-04-15T20:50:38Z","snapshot_observed_at":"2026-07-06T23:02:13.885476Z","submitted_at":"2026-04-15T20:50:38Z","title":"Incidence Constraints in Hypergraph Partitioning on GPU","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T11:43:50.529754Z"},"links":{"cited_paper":"/paper/2601.16118","citing_paper":"/paper/2604.14411"},"observation_digest":"sha256:2bfdda0a2458f84ae706dbefa7c11fd41bbbfce3be8c0831225ee4297c046ed1","observation_id":"0edc6e4e-dce9-4f29-b3f8-7d27f29e6e92","resolution":{"observed_at":"2026-05-10T11:45:21.363681Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"cited_work":{"arxiv_id":"2601.16118","doi":null,"metadata_source":"pith","pith_arxiv_id":"2601.16118","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","venue":"cs.AR","work_id":"70807938-fb81-4258-a416-66e4931bc203","year":2026},"citing_paper":{"arxiv_id":"2605.20497","last_updated":"2026-05-19T21:04:20Z","snapshot_observed_at":"2026-08-13T02:40:13.598258Z","submitted_at":"2026-05-19T21:04:20Z","title":"Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T06:23:49.990308Z"},"links":{"cited_paper":"/paper/2601.16118","citing_paper":"/paper/2605.20497"},"observation_digest":"sha256:2da42a9113b93193a9b5586de73536399e1bfaccf1fded1d3e74bbfc3362d114","observation_id":"d21ebbbe-33c1-49d7-9628-16c04e852763","resolution":{"observed_at":"2026-05-21T06:24:00.463565Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2601.16118/citation-record","integrity":"/paper/2601.16118/integrity","json":"/paper/2601.16118/citation-record.json","paper":"/paper/2601.16118"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip","venue":null,"work_id":"6123d36b-2606-4998-ac4d-1a9b806d7b0c","year":2015},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:f3a85cc4a04fdbefe59cd638c5f00ac1ff978a395bdcf216839efdae4583f159","observation_id":"3b37b7d2-6c12-4120-a787-bfef4289b640","resolution":{"observed_at":"2026-05-16T12:02:51.434944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3304103","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Spiking neural networks hardware implementations and challenges: A survey","venue":"ACM Journal on Emerging Technologies in Computing Systems","work_id":"835725d5-93ff-4f66-a99a-058190b961ee","year":2019},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:43a8c76cc45a7365c9dd1ef0bdcbf0f6a49cf4d3629f45e7dace24148ddc8e0b","observation_id":"6168ea93-6a37-41f3-8609-1d0a3b274fde","resolution":{"observed_at":"2026-05-16T12:02:50.542957Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spiking neural networks: A survey","venue":null,"work_id":"4fb9a3e7-f770-405a-9abe-c5db9c553259","year":2022},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:60e5a24b3e18edf2011a81a2570205edce48fcf555d24e1fcb2d0054d9f03176","observation_id":"eb44c998-0e81-4887-9bb0-c3efecaabe94","resolution":{"observed_at":"2026-05-16T12:02:51.438009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Loihi: A neuromorphic manycore processor with on- chip learning","venue":null,"work_id":"cae10ffb-7fd6-4cd7-9d65-de0e85bd2aed","year":2018},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:17909ab1ee7289d8759561ade172d5bfd150fa930868e049cb1e49707b25297a","observation_id":"2755f7f4-d6f5-4fa6-8f75-728197766c40","resolution":{"observed_at":"2026-05-16T12:02:51.447404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The spinnaker project","venue":null,"work_id":"6a185251-3af4-453b-9ae7-2308cad0e671","year":2014},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:98ab73562f00c4377421fd9a7962714d7c70a67bd5844d36f0d2d12cd96ea7e2","observation_id":"18120c85-7cc2-476f-aa38-b53ba4dfdc92","resolution":{"observed_at":"2026-05-16T12:02:51.415199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations","venue":null,"work_id":"fdfb37e8-a7b6-448b-9851-b96afc782531","year":2014},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:0b4b935d830801fd4e4387b9705d9ddfb02f69d7879688962b02f686d811403f","observation_id":"4e4ca685-418e-4073-b130-42a73cfc4f79","resolution":{"observed_at":"2026-05-16T12:02:51.412468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mapping very large scale spiking neuron network to neuromorphic hardware","venue":null,"work_id":"83ec1839-49e9-48c6-92b4-2f3d2f266acf","year":null},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:87524b78e8e3a7aa27302eca390b222fc2ca97cf49a4eccbdc389e5720c5b946","observation_id":"0f32bc26-108d-4f37-be64-4a06fb8eda2d","resolution":{"observed_at":"2026-05-16T12:02:51.444328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.358203","doi":"10.1145/3582016.3582031","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mapping very large scale spiking neuron network to neuromorphic hardware","venue":null,"work_id":"481d345d-8c51-421e-83a2-7c7483646de3","year":2023},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:c52d6b969d2f91e938cd0fba06dc595b54b335ac495690aafe5521c65af39262","observation_id":"0126bfcb-e005-4d40-a2ee-46be7ff5c3a0","resolution":{"observed_at":"2026-05-16T12:02:50.464289Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A linear-time heuristic for improving network partitions","venue":null,"work_id":"04bf506d-d105-47bc-ab01-4c0ed412227f","year":1982},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:3eab7301c9ca1aaa145db6edfe89e781015b9418942ff3a6c16b0ea852c302a7","observation_id":"04720c8f-f3a9-4f45-8d19-b4c1e7cb674f","resolution":{"observed_at":"2026-05-16T12:02:51.418025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hypergraph partitioning and clustering","venue":null,"work_id":"07157e55-83dd-4058-b44f-65948e584e24","year":2007},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:e9e04fca47eb8bcc616362c135346192ed84cbb0ae25db3b59f548f3e718d7ef","observation_id":"33c954c6-9fac-4cf7-8cf7-12c032111215","resolution":{"observed_at":"2026-05-16T12:02:51.420730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multilevel hyper- graph partitioning: Applications in vlsi domain","venue":null,"work_id":"52c92343-ef81-4616-a444-e044d5edebec","year":1999},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:b905c2a39aa161324e59f576193c7dde90e12993ded4eb101282385b79c512a9","observation_id":"7e8c19bd-42ea-4a46-a9e5-2703fc5dbf9d","resolution":{"observed_at":"2026-05-16T12:02:51.428551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications","venue":null,"work_id":"805c604a-a6f0-48bc-b63a-a532e257bb97","year":2016},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:ed96cc1cbacbfa20ff09f99b1c5130518de21dfdb17033050f407a3f061e7849","observation_id":"01cbe8df-ec3a-436c-addc-8f35a83cfc01","resolution":{"observed_at":"2026-05-16T12:02:51.406738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2366.319237","doi":"10.1145/3192366.3192371","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mapping spiking neural networks onto a manycore neuromorphic architecture","venue":null,"work_id":"15c8aa47-23c7-4a5c-bc22-2741a1c6f2b8","year":2018},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:46c744c9309820a53cb5e42dea10d4bd4dd6c11e06e705c7eced7d4e1e3277ed","observation_id":"e6338744-6123-4aec-80b6-4b9a3954a73e","resolution":{"observed_at":"2026-05-16T12:02:50.469838Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mapping spiking neural networks to neuromorphic hardware","venue":null,"work_id":"68aae246-7860-49e4-b641-e350ddbd6876","year":2020},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:276d46ff35a7b80194bcd45a985a00dbea633f4e7ad3408807489f095b5e1a1f","observation_id":"0f25e451-c65a-4f76-8fca-db0843ac814d","resolution":{"observed_at":"2026-05-16T12:02:51.403612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3479156","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dfsynthesizer: Dataflow-based synthesis of spiking neural networks to neuromorphic hardware","venue":"ACM Transactions on Embedded Computing Systems","work_id":"255646be-08b6-4147-a51f-9a85806ae334","year":2022},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:6c84f7a1412708cdbefc72d74304f4b091ddce271e00cfe80dad3e00b9bdd43f","observation_id":"a2ed3326-aa1e-46b9-8c5f-3dafce59eef4","resolution":{"observed_at":"2026-05-16T12:02:50.530773Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Edgemap: An optimized mapping toolchain for spiking neural network in edge computing","venue":null,"work_id":"65bb2c56-98d0-414c-9511-98935170240d","year":2023},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:627521b086d945ebb1d86db26652b339e9bb3bda62284b3f9c1fb4b23b99924a","observation_id":"78fb6dbe-d7fc-4aef-a6f7-edcaaf11baae","resolution":{"observed_at":"2026-05-16T12:02:51.409565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hierarchical mapping of large-scale spiking convolutional neural networks onto resource-constrained neuromorphic processor","venue":null,"work_id":"1212639b-bc8c-4df7-af05-4c85063f59ba","year":2024},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:1a46917b1d9340a382f36e5ad8ef3f05ab55ba00042984b5a74f4bb8a6c364e5","observation_id":"a26aacb9-7b16-4506-b6d0-ffe8340da763","resolution":{"observed_at":"2026-05-16T12:02:51.431684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6368.373529","doi":"10.1145/3716368.3735294","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking spiking network partitioning methods on loihi 2","venue":null,"work_id":"ebaa02e3-f863-4553-bf42-b2628b208fe3","year":2025},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:66484159b80494afbeb418b91daf51db0dbcbef863ed8134383f94d49db7c1a3","observation_id":"ae943f54-f9bf-4e03-a6a6-ff0a05720d87","resolution":{"observed_at":"2026-05-16T12:02:50.513494Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Liquid computing","venue":null,"work_id":"29619dec-0224-47e7-8db4-14f4abede7d2","year":2007},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:3471171ade7fae953fe23ff7442c4cff049689412eab360aa1303536d6dea2e1","observation_id":"a146f6a9-abe6-4307-a0dd-710dabf3d0ce","resolution":{"observed_at":"2026-05-16T12:02:51.440708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3529090","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"High-quality hypergraph partitioning","venue":"ACM Journal of Experimental Algorithmics","work_id":"b0b06838-43b1-45d9-add6-fa4917c3d29e","year":2023},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:d351788fc8773fd8a28e10787973366a24381f0aa9169d83c76b30dfa5f5315d","observation_id":"b7c6052f-f691-4ba8-8243-f0a4ca99390a","resolution":{"observed_at":"2026-05-16T12:02:50.517924Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-05-21T17:52:22.923951+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T17:52:22.923951+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b0a207ec-d74d-4b78-bab6-16ef81fb3ab4","year":2020},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:88649cc0e3cfb6eb7a00e9a2da8550bc76d102138c03748e3aa506a65870a3fa","observation_id":"d316ed49-6808-4b51-b32e-ba843f1488b8","resolution":{"observed_at":"2026-05-16T12:02:51.460258Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning with hypergraphs: Clustering, classification, and embedding","venue":null,"work_id":"d1aaff78-0607-4cab-8f2f-a84e7cd9370c","year":2006},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:96303074bee25677d27328bff71101f5c72ff95492ae48790f86f4f41d5ff899","observation_id":"cfb5d926-1db7-4ee0-b661-d57f5bb1837b","resolution":{"observed_at":"2026-05-16T12:02:51.453908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification","venue":null,"work_id":"5015a000-9677-4946-b008-05bfb7a4cedc","year":2017},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:4a8581889ee8f92e2a8f620c1fef5f53b6e7922f26dd3fc06250ff2822b8fd68","observation_id":"bcad7d35-ad71-40d6-ae10-2fa411158108","resolution":{"observed_at":"2026-05-16T12:02:51.457181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1126/sciadv.adi1480","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence","venue":"Science Advances","work_id":"17b42858-8d5f-41ad-a4ec-1828d982a8cd","year":2023},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:6a0089082b6a355853ce4f4e93d0579458236e0b0640bb0d5dd2d75a46d02b79","observation_id":"d3ed7f52-bfb7-4ead-9ab7-2f514d7397b5","resolution":{"observed_at":"2026-05-16T12:02:50.549619Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rethinking the performance comparison between snns and anns","venue":null,"work_id":"ad3a70fa-65ad-41be-86df-8ab86d725a24","year":2020},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:755529fc1ff1aab05df1dd9f52a8d826cca0201276731ff9026fbc9b067d16a6","observation_id":"1e7141a7-18f9-4a15-8b61-3d802806c85f","resolution":{"observed_at":"2026-05-16T12:02:51.463404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Training feedback spiking neural networks by implicit differentiation on the equilibrium state","venue":null,"work_id":"85369c3f-0393-4022-8a53-4ff9f40d9f01","year":2021},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:179560222c922bf8c46a85498d7c34c4e960ddea361aa8837ca335a81d0e080d","observation_id":"831e0e6f-818f-43dc-b288-ac934816967d","resolution":{"observed_at":"2026-05-16T12:02:51.425333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3145479","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Online adaptation and energy minimization for hardware recurrent spiking neural networks","venue":"ACM Journal on Emerging Technologies in Computing Systems","work_id":"8e17a68f-c1d8-4f43-b44d-12b53519bf27","year":2018},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:3168150d5dfdecea0ca4253a1ad2c131d897957a268bb5f8aac9fd2610ac5baf","observation_id":"756fa737-4c95-44aa-94c2-4e934286400a","resolution":{"observed_at":"2026-05-16T12:02:50.546110Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6277.274109","doi":"10.1145/2736277.2741093","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Line: Large-scale information network embedding","venue":null,"work_id":"838a98fa-101f-403b-8bd3-3e68a639f4f8","year":2015},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:a467fe76024a6abdf94d232b8f0c442557a3ce3ffd0e04ed4356f57c0e66e93c","observation_id":"fbc12e75-a57c-4fc4-ac12-ac5488b827b1","resolution":{"observed_at":"2026-05-16T12:02:50.535979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3e568ded-7f97-478b-a037-b3ac5234db25","year":1990},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:6fe0d821d8067926f6f9512ad05e7571ba2ce782fd61f84570d1eceb62a16c6f","observation_id":"c4afd9ea-5edd-48b6-8062-70363ec6846f","resolution":{"observed_at":"2026-05-16T12:02:51.479114Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.03137","last_updated":"2015-11-10T15:29:19Z","snapshot_observed_at":"2026-08-04T15:16:11.178811Z","submitted_at":"2015-11-10T15:29:19Z","title":"k-way Hypergraph Partitioning via n-Level Recursive Bisection","version":1},"cited_work":{"arxiv_id":"1511.03137","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.03137","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"k-way Hypergraph Partitioning via n-Level Recursive Bisection","venue":"cs.DS","work_id":"2014ec6d-5e78-4563-8d64-5464624bfae0","year":2015},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"cited_paper":"/paper/1511.03137","citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:c3526fd474967edbf26042c0f0e441884d2f69fb40355884bcf5b066e7859b28","observation_id":"d53b3d99-a69e-46c1-8bff-749b5ef8abfe","resolution":{"observed_at":"2026-05-16T12:02:50.775445Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9847.309954","doi":"10.1145/309847.309954","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Multilevel k-way hypergraph partitioning","venue":null,"work_id":"597f5553-5d93-4d02-bd47-ac2a93d1bfe1","year":1999},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:2a84875e0f6f6b267afac1612b6638dfbdfa27c7c5d9318a44c21171e00984f8","observation_id":"18d447b6-560b-4a2c-b06b-248c1b52c315","resolution":{"observed_at":"2026-05-16T12:02:50.527189Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-05-21T17:52:22.39122+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T17:52:22.39122+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8996.369025","doi":"10.1145/368996.369025","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"title =","venue":"Communications of the ACM","work_id":"b5079c47-8e78-4dab-b7eb-da5a034f534b","year":1962},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:6b5db894c97dda8fce392c7fed9b4d3978e5e357ebef25ee693b329f092547ff","observation_id":"912b18b1-e1e7-4f06-8908-c22201aacffc","resolution":{"observed_at":"2026-05-16T12:02:50.474873Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-07-11T02:49:24.476915+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T02:49:24.476915+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Drawing graphs by eigenvectors: theory and practice","venue":null,"work_id":"edfca288-32b3-46a3-86c7-566243b79004","year":2005},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:6bcc93f5be4ea1d0e1d095495cb1f0ecc62cbca26a37b89e897a9c7d01bda518","observation_id":"6c57ee46-150d-47e8-8b59-cf2c6514e182","resolution":{"observed_at":"2026-05-16T12:02:51.482941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Laplacian eigenmaps for dimensionality reduction and data representation","venue":null,"work_id":"9e9a7cfd-e5a9-4f03-9225-f03fc26d6eec","year":2003},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:f6ef9c1805dd842048f7f43538805643b5d6488811e5fdd0f9ac056bc6003660","observation_id":"a09a5ec3-9409-4324-ae01-8e35e082bb26","resolution":{"observed_at":"2026-05-16T12:02:51.473796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/1.9780898719628","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lehoucq, D.C","venue":"Society for Industrial and Applied Mathematics eBooks","work_id":"64f94a32-e5a3-4080-8510-200784e21415","year":1998},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:af751af6cf17a32575cd3344edd8356ee8ebc9d21e735d114dfc20ef8012d1ff","observation_id":"18ac1949-7d99-43f2-85d4-57509314d11b","resolution":{"observed_at":"2026-05-16T12:02:50.539337Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T04:13:35.056640Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":"1409.1556","doi":"10.48550/arxiv.1409.1556","metadata_source":"pith","pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","venue":"cs.CV","work_id":"1c4b4409-c14b-488b-a086-c57a5aab8a29","year":2014},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:1e8b7438c5093d2a58d13ba386fba1b5d60edb1be82ee5c280cd0590a169e746","observation_id":"3db0b39f-ff91-4c54-95d4-e857c0cfa6d6","resolution":{"observed_at":"2026-05-16T12:02:50.779981Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-07-12T23:51:07.915518+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T23:51:07.915518+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cholletet al., “Keras, ” https://keras.io","venue":null,"work_id":"cf6b9975-ad9e-4fbf-8495-ca85ec832e79","year":2015},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:a4abb94033c6f29d8bfc165c1a71887ea1db8412f04f1fd4d6caa99e0d82d7af","observation_id":"c205398d-578e-442b-a233-898ad173a49d","resolution":{"observed_at":"2026-05-16T12:02:51.466540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":"b9eb9546-49a7-43d9-a91b-e6504be4558b","year":2019},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:e29670a9b9daa3c6eb371af8ae320406bd3d62ccfc29e4d1c567a1397b6512b9","observation_id":"12eff54d-ee8d-4f60-aeaf-e455fddb708a","resolution":{"observed_at":"2026-05-16T12:02:51.470053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neuron.2020.01.040","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex","venue":"Neuron","work_id":"fe974a5b-69f4-40c3-8b0c-c7ddedb0c86e","year":2020},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:24f1ca913cb8d69cf523c73307427456c2cc9151032e90614cbe43d1d4275f0e","observation_id":"f40c3967-3313-44c7-850d-5bb65ee6de68","resolution":{"observed_at":"2026-05-16T12:02:50.521869Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-05-21T17:52:25.587279+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T17:52:25.587279+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the distribution of firing rates in networks of cortical neurons","venue":null,"work_id":"c1abcaec-2f0c-45ac-b3bd-4b5c05d40585","year":2011},"citing_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-16T12:01:57.764416Z"},"links":{"citing_paper":"/paper/2601.16118"},"observation_digest":"sha256:74f09eabbe6212b0cddf0539ee4977c71e224013ff8d20f9e884b0c1419f4643","observation_id":"0f19f248-3152-4009-9d32-c35841e288dd","resolution":{"observed_at":"2026-05-16T12:02:51.450729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","latest_version":2,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-12T14:19:37.371598Z","submitted_at":"2026-01-22T17:13:57Z","title":"A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":2,"verified_exact":13,"verified_fuzzy":23},"total_outbound_references":40},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2601.16118."}