{"as_of":"2026-08-10T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9218105f7cf8ba05821cbc757a1f98db7e674d0a9dcc4cb1572e8a41187794e2","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":"2026-08-07T15:35:39.661310Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.14533/citation-record","integrity":"/paper/2505.14533/integrity","json":"/paper/2505.14533/citation-record.json","paper":"/paper/2505.14533"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neucom.2021.08.023","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Jiang, Z","venue":"Neurocomputing","work_id":"837af5cd-7e30-4212-8337-09ae2f395c7e","year":2021},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:38.627340Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:035cb5603b7c02f08085dd685fb84a5c249d7bb4541e9f4228491a79936674ab","observation_id":"7c4ed891-9451-465b-8a77-57d92838bc43","resolution":{"observed_at":"2026-08-07T15:35:40.106243Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neucom.2019.01.087","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"Neurocomputing","work_id":"c6802a36-48fc-49ff-b042-3036f6af7544","year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:38.702280Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:7a830348f08db373bf733bace25e9696616bdb42d1064f0b953729df23fe687f","observation_id":"3ec54add-24cb-4da6-a5ec-0932aeadee36","resolution":{"observed_at":"2026-08-07T15:35:39.915738Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.12866","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.541167Z","title":null,"venue":null,"work_id":"7c2af89b-dae7-4243-9c08-3dae3b631f62","year":2025},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:38.789450Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:3ebc6c12cb81c6d74af0e48b4acb654e89672f410ad64c6694b959b8ade6b051","observation_id":"7465b7ba-d850-4b1d-902f-2e06583ad4d9","resolution":{"observed_at":"2026-08-07T15:35:41.556549Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.665609Z","title":null,"venue":null,"work_id":"54f5f225-019c-4743-aa27-3841c0701e35","year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:38.853444Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:a30426c323b4111c5144cf04dd67d8277345d61be96d474856bb8a127c9e779e","observation_id":"a95eaedc-9989-48f0-8593-5fcb872705d9","resolution":{"observed_at":"2026-08-07T15:35:41.671165Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:38.944440Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:38.944440Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:0245bc5b2bb9d033b05a0a1665bb4e3066b11547a1fe8fed617181386b1da194","observation_id":"8b824616-3deb-484e-a105-635d653d36c4","resolution":{"observed_at":"2026-08-07T15:35:38.944440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.007374Z","title":"Hochreiter, J","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.007374Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:31c6d7e8b6f8080b5936174df731886e86e8b19e2e9d1d4c79f979e430c125e8","observation_id":"1bf08ef3-d135-4963-a18f-813b48918c50","resolution":{"observed_at":"2026-08-07T15:35:39.007374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.099802Z","title":null,"venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.099802Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:504c18f83586754f87a91f31379b63fa924c16663b38fc70b1ecd83c8ee0f5de","observation_id":"f282bd33-a0e7-4b5a-8050-d7081daca693","resolution":{"observed_at":"2026-08-07T15:35:39.099802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.644628Z","title":"Vaswani, N","venue":null,"work_id":"bbb3c950-df9c-4016-90b1-d4a5888c8c39","year":2017},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.173601Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:2e8bcdf4365ee2c1ef623f1211168ec7be73145ecafac1e526bcc5977c699073","observation_id":"df3cd4f7-bd6d-469c-9c66-0f403104ad32","resolution":{"observed_at":"2026-08-07T15:35:41.650745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.212956Z","title":"Devlin, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.212956Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:86427441a8adef1ef84fff1d8900c9387e908ef195e7053676317d36ff0775cb","observation_id":"dc1f37be-9015-4188-abc5-6edf82ca1150","resolution":{"observed_at":"2026-08-07T15:35:39.212956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-07T15:35:39.274700Z","title":"Dosovitskiy, L","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.274700Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:bb272986f2d166ad0c2eb5fded41d2a9ccc4163b4e3346a6fcad836be7854959","observation_id":"3e607670-f82c-4ea4-92bc-637b62d8c8f4","resolution":{"observed_at":"2026-08-07T15:35:39.274700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01345","last_updated":"2021-06-24T17:09:59Z","snapshot_observed_at":"2026-08-07T09:11:20.723647Z","submitted_at":"2021-06-02T17:53:39Z","title":"Decision Transformer: Reinforcement Learning via Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01345","snapshot_observed_at":"2026-08-07T15:35:39.339411Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.339411Z"},"links":{"cited_paper":"/paper/2106.01345","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:644e4c628ef1943e32a1fb8eed377d25386e367852e16454515d480e0d212a6d","observation_id":"d749d0c9-106f-4a2d-9fe6-a5a7edd64b87","resolution":{"observed_at":"2026-08-07T15:35:39.339411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.09948","last_updated":"2019-05-03T16:24:45Z","snapshot_observed_at":"2026-07-06T07:29:33.628905Z","submitted_at":"2019-01-28T19:13:55Z","title":"Surrogate Gradient Learning in Spiking Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.09948","snapshot_observed_at":"2026-08-07T15:35:39.373438Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.373438Z"},"links":{"cited_paper":"/paper/1901.09948","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:9c1d8e060ff158cf24f7fa26401a2fda177a17e66251a5b15bd9b8fd1321abd5","observation_id":"74dd7fcd-9458-423d-9518-e471485b89bc","resolution":{"observed_at":"2026-08-07T15:35:39.373438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.401667Z","title":"Davies, N","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.401667Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:995530d19c5add9ac0f68199f292590dca6f591dca9000e55e7a1f395353917d","observation_id":"47d3c626-1857-4a78-9736-a291758f6a56","resolution":{"observed_at":"2026-08-07T15:35:39.401667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.467778Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.467778Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:b847282e739c7336fde91a362f864908945901ee531a6ec31095069f6a3c226c","observation_id":"18c0b7e8-677f-4ac3-a10f-7e74b5ca4c68","resolution":{"observed_at":"2026-08-07T15:35:39.467778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.502412Z","title":"Blouw, X","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.502412Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:1b3c0223a4df8ec7f59aa3bf8601c091fd8fbbae507417138ebf5731dcf215aa","observation_id":"9d62dfbd-976a-484f-aed7-15516efa02d3","resolution":{"observed_at":"2026-08-07T15:35:39.502412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.510374Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.510374Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:1b83dc6c73a509b5287cd3ef0dbfd59e159e245f388c32d91bb9e975a2bc0454","observation_id":"0180f0f9-7ae8-4b7c-9f3a-47e9f176385c","resolution":{"observed_at":"2026-08-07T15:35:39.510374Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.515928Z","title":"Rostami, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.515928Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:f91359f655ac066becb93572085d7be3b3d1661425ea51dd22309ab38e393a99","observation_id":"1a32cdc7-9945-44a3-95c6-46f2a9e0ae1b","resolution":{"observed_at":"2026-08-07T15:35:39.515928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.87770","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:40.976867Z","title":null,"venue":null,"work_id":"5108e73e-78c3-4ca3-9a04-4f75d231c218","year":2022},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.522100Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:7b24b16930505080c541f06c6db671bacc022d8db02337872c89234c0dd6c2e8","observation_id":"d9ede18b-a081-494c-8fd3-db6a3defed1a","resolution":{"observed_at":"2026-08-07T15:35:40.987241Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.01157","last_updated":"2020-07-31T22:25:13Z","snapshot_observed_at":"2026-08-08T12:53:35.395469Z","submitted_at":"2020-03-02T19:39:16Z","title":"Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware","version":2},"cited_work":{"arxiv_id":"2003.01157","doi":null,"metadata_source":"pith","pith_arxiv_id":"2003.01157","snapshot_observed_at":"2026-08-07T15:35:40.872619Z","title":"Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware","venue":"cs.NE","work_id":"70c6b966-6ac4-4c9c-909f-ab83dffd4c9e","year":2020},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.527384Z"},"links":{"cited_paper":"/paper/2003.01157","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:06a8e9d9d10f12a73fd5058e8059c47fa735c3ad49c85cb1cc0db5defc5cbcfb","observation_id":"e2bdb013-2675-4c5a-9235-cee447ccf3fc","resolution":{"observed_at":"2026-08-07T15:35:40.880839Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08646","last_updated":"2018-09-05T10:10:03Z","snapshot_observed_at":"2026-07-06T07:09:26.843025Z","submitted_at":"2018-09-05T10:10:03Z","title":"SLAYER: Spike Layer Error Reassignment in Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08646","snapshot_observed_at":"2026-08-07T15:35:39.533183Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.533183Z"},"links":{"cited_paper":"/paper/1810.08646","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:43d51d6dd638fe5c11cd90a7509600fbb4760a14bdcdac2ef2ada3ae148f7cdc","observation_id":"707b9591-37bb-4292-b55e-abdc3de4a45d","resolution":{"observed_at":"2026-08-07T15:35:39.533183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.626875Z","title":"Bellec, F","venue":null,"work_id":"7739a2a7-19ff-4871-96b2-ce91866de95e","year":2020},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.540370Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:f5720e6d1ebf8d24ca1897896bd84e34ffe4994f9922fe58cdd1518560096c06","observation_id":"08a12df0-0164-499f-b2c5-225728c6e883","resolution":{"observed_at":"2026-08-07T15:35:41.632165Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.545270Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.545270Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:4bacdda78baad82c56174b2687a30d11b377cb040e3fc50b0be571caabb3dcc5","observation_id":"874e89c4-5ab2-472e-b1d4-879b6b0185cf","resolution":{"observed_at":"2026-08-07T15:35:39.545270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.05785","last_updated":"2021-08-17T03:04:45Z","snapshot_observed_at":"2026-08-10T10:12:06.027558Z","submitted_at":"2020-07-11T14:35:42Z","title":"Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.05785","snapshot_observed_at":"2026-08-07T15:35:39.551208Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.551208Z"},"links":{"cited_paper":"/paper/2007.05785","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:02955992962a4504b66e32d90d4ac1abc0cf8f834530877555026c20eb09376c","observation_id":"c201bee8-a265-489a-a099-8fc450cd2706","resolution":{"observed_at":"2026-08-07T15:35:39.551208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.12826","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:40.685999Z","title":null,"venue":null,"work_id":"55d2385d-054c-47f6-b9b3-1ba9a16a377e","year":2024},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.557444Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:c272ea1dc740f99a48094d5550170b2df2097150214c845543b6ccbc563397b6","observation_id":"40aaab5a-0442-49d3-8dd7-dcdfefcfabf6","resolution":{"observed_at":"2026-08-07T15:35:40.702395Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.563827Z","title":"Gerstner, W","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.563827Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:00c9f57d6d71a4e40f8f7f441892904c677d98f48a9c8efe5dffe28d71a7d52b","observation_id":"b0008a02-c835-4623-a3bb-956461513b41","resolution":{"observed_at":"2026-08-07T15:35:39.563827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.02039","last_updated":"2021-11-29T00:56:52Z","snapshot_observed_at":"2026-07-06T11:15:43.397820Z","submitted_at":"2021-06-03T17:58:51Z","title":"Offline Reinforcement Learning as One Big Sequence Modeling Problem","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.02039","snapshot_observed_at":"2026-08-07T15:35:39.570011Z","title":"Janner, Q","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.570011Z"},"links":{"cited_paper":"/paper/2106.02039","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:7d43bf714ae0af429c544d6b3c8a90899778a0c033dfeedae0e7523c948e54e3","observation_id":"202d1f6c-1125-4b25-9ad8-dd40eb00abe9","resolution":{"observed_at":"2026-08-07T15:35:39.570011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.574968Z","title":"Ghanem, P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.574968Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:a6958a3b3b5ece9ff14da698e993507e6fef5e0f8e6163f385f29c54491798b7","observation_id":"7903d22c-28f5-4639-aa2c-428387e6e968","resolution":{"observed_at":"2026-08-07T15:35:39.574968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.586042Z","title":"Correia, L","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.586042Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:fa9bc6b06e272a1b76274b9c0043e7e332dce9b20ffb54d35a4bdc219679a699","observation_id":"522bae27-9570-4e49-a101-50d033b5db97","resolution":{"observed_at":"2026-08-07T15:35:39.586042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.608006Z","title":null,"venue":null,"work_id":"4a685c0a-d1a7-4d9f-ab19-7428661c20f4","year":2023},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.594727Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:6410f07c0e4934996557012b2ff6d485f3695c70d607a91d6cca2eb495366945","observation_id":"1c724046-3c53-4b5f-b5ff-71b36d514476","resolution":{"observed_at":"2026-08-07T15:35:41.614007Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16397","last_updated":"2024-10-07T12:05:12Z","snapshot_observed_at":"2026-08-10T10:17:19.641829Z","submitted_at":"2023-09-28T12:44:51Z","title":"Uncertainty-Aware Decision Transformer for Stochastic Driving Environments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16397","snapshot_observed_at":"2026-08-07T15:35:39.601060Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.601060Z"},"links":{"cited_paper":"/paper/2309.16397","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:67a3b532cbeddb30c192d09f0119e6779d942220376f4b52ca4004a4f7d2571c","observation_id":"902e3689-aee8-408a-9fdc-f204ff6749e9","resolution":{"observed_at":"2026-08-07T15:35:39.601060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06817","last_updated":"2023-08-11T17:45:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-12-13T18:55:15Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06817","snapshot_observed_at":"2026-08-07T15:35:39.607060Z","title":"Brohan, N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.607060Z"},"links":{"cited_paper":"/paper/2212.06817","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:42212e0915d9123a4ae0dc280428f18ba9476bfc9215b56368c60e2a9f04609e","observation_id":"536647cf-695a-43fd-a7c6-9fa6084519e1","resolution":{"observed_at":"2026-08-07T15:35:39.607060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.588554Z","title":null,"venue":null,"work_id":"ca2f1907-43b8-446f-8bb6-5a22b6a7cfde","year":2015},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.613268Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:6f5b581caefd9672b9fe58476470a73feee328aa6b1d847703654ade089ab298","observation_id":"54377c0c-b410-4b0f-9abf-10369cbbaab0","resolution":{"observed_at":"2026-08-07T15:35:41.594106Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.619168Z","title":"Sutton, A","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.619168Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:49c31dbc252cf1be5af6fb07268ce91a2961e6f4992a706eb8996fe79d86879f","observation_id":"8509d5bc-1c15-4d4c-91c3-4a67d89d9c8a","resolution":{"observed_at":"2026-08-07T15:35:39.619168Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:41.568544Z","title":"URLhttps://api.semanticscholar.org/CorpusID:208329736","venue":null,"work_id":"7656ff98-e094-4b66-8aee-6cb306679f5c","year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.625859Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:0fa03afd0fdc6efad7feba49fe577bf24bfe244278b869c279383d99cb1724e0","observation_id":"e50a8566-a6f9-4be5-8411-7cf1f7c82052","resolution":{"observed_at":"2026-08-07T15:35:41.574107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10686","last_updated":"2022-11-19T12:49:22Z","snapshot_observed_at":"2026-07-06T14:20:34.768988Z","submitted_at":"2022-11-19T12:49:22Z","title":"Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10686","snapshot_observed_at":"2026-08-07T15:35:39.631733Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.631733Z"},"links":{"cited_paper":"/paper/2211.10686","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:beca85ee2a471aa0b3d8d8cb5fdcf6043f416512d54f6af8fea203a2723ce9cc","observation_id":"a3b52750-fb4e-44be-8df8-6b0984522d92","resolution":{"observed_at":"2026-08-07T15:35:39.631733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15425","last_updated":"2022-11-22T12:45:05Z","snapshot_observed_at":"2026-08-09T23:18:31.999424Z","submitted_at":"2022-09-29T14:16:49Z","title":"Spikformer: When Spiking Neural Network Meets Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15425","snapshot_observed_at":"2026-08-07T15:35:39.637825Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.637825Z"},"links":{"cited_paper":"/paper/2209.15425","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:0c71b1837fb9a3c4f7f55807e1001d0dd62e5cc4ba2b0919a764676ea734d715","observation_id":"57d714de-8d9a-472e-82b4-fdd878938e14","resolution":{"observed_at":"2026-08-07T15:35:39.637825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07219","last_updated":"2021-02-06T01:57:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-04-15T17:18:19Z","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.07219","snapshot_observed_at":"2026-08-07T15:35:39.644320Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.644320Z"},"links":{"cited_paper":"/paper/2004.07219","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:55b03d789d4366ab84c384cb45ae128838c8b31695828b0166c8738655e4a3ab","observation_id":"cf10ac40-a7ec-40fe-8858-a6f218473a38","resolution":{"observed_at":"2026-08-07T15:35:39.644320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:35:39.649428Z","title":null,"venue":null,"work_id":null,"year":1968},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.649428Z"},"links":{"citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:1b6ffcf987cb948a0df9c3404a6a831f496547ecf203d8dcd2b6eb195445d7aa","observation_id":"77457ffd-248b-492b-8034-5cef194dbd52","resolution":{"observed_at":"2026-08-07T15:35:39.649428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-07T15:35:39.654619Z","title":"Loshchilov, F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.654619Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:9d5ce252637d73b06628f3b5f5f4e36b5be3617396549a9b2e2f3a6fd557145e","observation_id":"2778d2d6-b871-4d0c-b72c-468f96278b33","resolution":{"observed_at":"2026-08-07T15:35:39.654619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-07T15:35:39.661310Z","title":"Loshchilov, F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:35:39.661310Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2505.14533"},"observation_digest":"sha256:a681c0d83b768cbb58398a9c28209b437eeb9e02483ee0cad4ac6b370cfb9d43","observation_id":"6481bb74-74b2-4710-a477-17c2bf05d4d1","resolution":{"observed_at":"2026-08-07T15:35:39.661310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14533","last_updated":"2025-05-20T15:52:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T10:12:18.586479Z","submitted_at":"2025-05-20T15:52:43Z","title":"Energy-Efficient Deep Reinforcement Learning with Spiking Transformers"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":4,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":28,"verified_exact":3,"verified_fuzzy":2},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.14533."}