{"as_of":"2026-08-05T07:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:89b5f99dd029075c9cdb2da3a526f8c2c02a9e9bb66738d475b350ff12e513be","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":"2026-08-05T06:32:48.257954+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":"2026-07-06T22:42:43.581560Z","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:d710aa580e81d71b9c94798cb080d5dfe2046a822bf9d514266c62ddfa7aff1e","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16118","last_updated":"2026-04-21T16:32:45Z","snapshot_observed_at":"2026-07-06T22:42:43.581560Z","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":"2026-07-06T23:31:03.878152Z","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":"sha256:4bb80181201dffde514812e72a9aee657ebb9b5102c9b1da0345112f7669b9b8","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:cffc5a3a7aa935da4e681360b04c856ceb5351533961efb0c1ed7afe90d7fc45","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:192d792ee75304e8fc2da22cc53bccebc044b270955501ebc9fac5e87a8197f6","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:ebb9abdc28182e42a1de6c8808eebb7d9d848a7f9a5e3519eea0cddf0a61a74a","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:75f8d4f50c7b54c347264b3836623388032440c16a8142ff85e6bdd752d7110f","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:8388f70124800cc3cef8bd50b63e8c0ff5602f9d4d9d10b9ed137e397ee35867","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:34de0e9b8e0b529a9542d07962627dc4a4638e7bfd51026ca089abf6fe5753db","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:c188b749551b404fe5378e0fec81822ff752eb2b07f0960aa2522a0c08f10d54","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:09da0ceb6e25bb1eca4d556c4750300ad4907c10c6553dc10efd42407de19789","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:24f1113f972bbad4129eb3657e7f68b72d2982d83ab5c93512080c5dba4a2db1","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:203555ae1dbf0b6febc3dfbcdde5f2af5823bf0f75c11c5d2f36403e08c96906","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:13c849405fe90d7827584447e6784ce64b92190ded040d6066c731a8f58c2207","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:2864df87c3e674dec6f85acb97c4b90a3adb7f03534beca69807f23d76e11944","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:78a30ec16f9e3c75c36f51bc2042fb8f7518270e609745293a8af86f17bb2e49","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:d05aee1c664ac212feec66a7094a8ba8be4c39c84be2a52de44bad2c4270922d","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:15dce602475de0a93d7499669c8a87b39e605a111e6a5a59c4b37110f29a760a","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:2f3905d64e3a826e8cc5433d68f814f006981e5bc54471d89944cb8470961644","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:cdc9f9fa090cd7c381ce7e8b5f0dc247113d338c9db60bb61cf0ef460cc8748b","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:a3594af3c268443396b89b4ccff7c21fef4404ce5da3cb6daf3509a541dc1b91","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:5a79692e98d22442b3b9a301193ec977fd120c51cf7b783a103ba97ef8440291","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:362d3721bcfd4d7481667052b23d8d5f626fc2c6f31eee8bf3679e224e47645f","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:cf44053c2c18811b91f96ebbf889ce97b968fbd20d980a0cf0f7464b382e4e91","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:9c73d3facfaf17e55a2fe943e8516a0509516b1e1d98e89fd99ecae6f8334e53","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:c5c19926659d83fc5c593bc14be8d4541cecda3b41ca707c9b0a7efeb19a8e23","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:3980dae2bdd1c5d40b22c957be255228901914b255ec45d50bd3e14a57cf21ef","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:f3d72cc04cffa1729e2c94768d331584ff927d7b3c25db09eaa93fe0944027b1","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:f3c1a2276193fb57e3f9763a7a527ee619c7a39fc86aab3032051fbca4590509","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:ac62d9d5e3a482d620ca487cb74914ce3ad39257b9d9453ab64f40a57a5bd76b","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:08e709208f1c86036b7838885a69de7869b49aaeb429c3f205e723605a7ec120","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:1182b20b30e3d4c38c13d84eab60e855baa3d06284131829690135fb4d87c81a","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:8ee1c8a6607aeeb2972e704011b4cba065c18573f5cc3c8f4001b4971b3819b0","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:efec89363895ccf1411115e37afb3a65629422c5bcb21e73024befe788034e57","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:dbc9a2896c2610b720bff7d57ada63b9463e2e2fbf87eb1a2e8d9c48391c289d","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:6dff18e4d24cae054a110683e8d7fecc8d48458f988e7be146d3264c93b9cd52","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:19afb10e9795b3333bac97ea49d868aad3d4c4ae515958d2e5f0e6a89df01f65","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:c91f09dd6d05379cb94bd2b816aac26ca20239265459aa8c03782841cf4fec4f","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-07-06T03:53:32.549552Z","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":"2026-07-06T22:42:43.581560Z","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":"sha256:0b61dbeb211746c5a64596aabce7051ba9814c081c111a11ba7ce5f99d78e875","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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:ff8187024e9f197d822513d4e868008ed595cb4ecf2b0e9f6190072bbb1c57c1","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:631b6104803242375c25160d64ccdaa0dfe4e6cacf9aca6b9c2d2297891902d0","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:1d9a3c2340c5adfa3cd7515b6af6284d98c3a90a3ce736514781ee7fa9a1f5e9","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"sha256:2f678c073b4f1f7c8bcd827ce1e2d5be976453338ad028053d1265d93bfafb3f","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 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T22:42:43.581560Z","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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2601.16118."}