{"as_of":"2026-08-09T00:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:42c41366dd3d866885beec3ebb75f385010f017c8db52329a7cee83949cc198a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:36:09.092529Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T10:06:03.417152Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.06522","last_updated":"2024-09-17T14:30:04Z","snapshot_observed_at":"2026-07-06T16:30:25.358715Z","submitted_at":"2023-10-10T11:08:31Z","title":"Watt For What: Rethinking Deep Learning's Energy-Performance Relationship","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06522","snapshot_observed_at":"2026-08-06T23:36:09.092529Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17016","last_updated":"2025-06-20T14:13:52Z","snapshot_observed_at":"2026-08-08T16:17:24.703072Z","submitted_at":"2025-06-20T14:13:52Z","title":"The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:36:09.092529Z"},"links":{"cited_paper":"/paper/2310.06522","citing_paper":"/paper/2506.17016"},"observation_digest":"sha256:9bc6d08ab3b71125673de196213087c55ec600b25edc7cc36c902f583775c812","observation_id":"1c5b9a41-e532-4363-a575-dd76a969cad6","resolution":{"observed_at":"2026-08-06T23:36:09.092529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06522","last_updated":"2024-09-17T14:30:04Z","snapshot_observed_at":"2026-07-06T16:30:25.358715Z","submitted_at":"2023-10-10T11:08:31Z","title":"Watt For What: Rethinking Deep Learning's Energy-Performance Relationship","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06522","snapshot_observed_at":"2026-08-05T17:57:16.276535Z","title":"Gowda, Xinyue Hao, Gen Li, Laura Sevilla - Lara, and Shashank Narayana Gowda","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15478","last_updated":"2025-09-04T11:23:22Z","snapshot_observed_at":"2026-08-05T17:57:12.128089Z","submitted_at":"2025-08-21T11:56:05Z","title":"SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T17:57:16.276535Z"},"links":{"cited_paper":"/paper/2310.06522","citing_paper":"/paper/2508.15478"},"observation_digest":"sha256:e0e1405310e155508ce57da2516022783e5ce6e1f5db3edb5e5ac458feeead7d","observation_id":"53c0b531-320d-487b-8cce-80ff77a01104","resolution":{"observed_at":"2026-08-05T17:57:16.276535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06522","last_updated":"2024-09-17T14:30:04Z","snapshot_observed_at":"2026-07-06T16:30:25.358715Z","submitted_at":"2023-10-10T11:08:31Z","title":"Watt For What: Rethinking Deep Learning's Energy-Performance Relationship","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06522","snapshot_observed_at":"2026-08-05T17:03:26.077577Z","title":"arXiv preprint arXiv:2310.06522 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00045","last_updated":"2025-09-03T05:15:22Z","snapshot_observed_at":"2026-08-07T02:23:23.482133Z","submitted_at":"2025-08-24T09:53:33Z","title":"Performance is not All You Need: Sustainability Considerations for Algorithms","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T17:03:26.077577Z"},"links":{"cited_paper":"/paper/2310.06522","citing_paper":"/paper/2509.00045"},"observation_digest":"sha256:037b7ad92b49a2d2cf9bf7f8ba784c0792037a7551f36a502f53b55f90bd2dc9","observation_id":"98a75840-7fe3-427c-8631-33f7c6c1c3c8","resolution":{"observed_at":"2026-08-05T17:03:26.077577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06522","last_updated":"2024-09-17T14:30:04Z","snapshot_observed_at":"2026-07-06T16:30:25.358715Z","submitted_at":"2023-10-10T11:08:31Z","title":"Watt For What: Rethinking Deep Learning's Energy-Performance Relationship","version":2},"cited_work":{"arxiv_id":"2310.06522","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.06522","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Watt for what: Rethinking deep learn- ing’s energy-performance relationship","venue":null,"work_id":"b285cfa0-5fa2-4d31-8502-a645d00d12c1","year":2023},"citing_paper":{"arxiv_id":"2604.12945","last_updated":"2026-04-14T16:41:33Z","snapshot_observed_at":"2026-07-06T23:01:05.634599Z","submitted_at":"2026-04-14T16:41:33Z","title":"Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T15:38:50.333310Z"},"links":{"cited_paper":"/paper/2310.06522","citing_paper":"/paper/2604.12945"},"observation_digest":"sha256:6dd92bfb65033eb5d4bbf572af48f2f8e5a807f5042354c3a08d5575113c9ffa","observation_id":"11c98073-db02-45e5-82ef-908373daed3c","resolution":{"observed_at":"2026-05-11T10:06:03.420523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.06522/citation-record","integrity":"/paper/2310.06522/integrity","json":"/paper/2310.06522/citation-record.json","paper":"/paper/2310.06522"},"outbound":[],"paper":{"arxiv_id":"2310.06522","last_updated":"2024-09-17T14:30:04Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:30:25.358715Z","submitted_at":"2023-10-10T11:08:31Z","title":"Watt For What: Rethinking Deep Learning's Energy-Performance Relationship"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.06522."}