{"as_of":"2026-08-11T13:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:951f455443ebc5eafea92ff4bfe09c08ce3440fed02199e1b25bbdc8b87cf716","coverage":[{"denominator":4,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T22:53:39.926722Z","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":"2026-08-11T06:34:44.6726+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/2605.25469/citation-record","integrity":"/paper/2605.25469/integrity","json":"/paper/2605.25469/citation-record.json","paper":"/paper/2605.25469"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.06085","last_updated":"2018-07-17T07:33:19Z","snapshot_observed_at":"2026-07-06T06:39:21.688392Z","submitted_at":"2018-05-16T01:19:43Z","title":"PACT: Parameterized Clipping Activation for Quantized Neural Networks","version":2},"cited_work":{"arxiv_id":"1805.06085","doi":"10.48550/arxiv.1805.06085","metadata_source":"pith","pith_arxiv_id":"1805.06085","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PACT: Parameterized Clipping Activation for Quantized Neural Networks","venue":"cs.CV","work_id":"ab8da7fe-9ff3-44e6-9132-11d17ceef8c2","year":2018},"citing_paper":{"arxiv_id":"2605.25469","last_updated":"2026-05-25T06:19:49Z","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:19:49Z","title":"JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T22:53:39.926722Z"},"links":{"cited_paper":"/paper/1805.06085","citing_paper":"/paper/2605.25469"},"observation_digest":"sha256:3700c10457e8b08abfe63e130ae947c69899b970436701ce2bb681fdf1291105","observation_id":"293c60bf-7875-485a-95be-bf17d08d3a73","resolution":{"observed_at":"2026-06-29T22:54:00.677703Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06160","last_updated":"2018-02-02T01:43:54Z","snapshot_observed_at":"2026-07-06T05:00:35.763958Z","submitted_at":"2016-06-20T15:02:31Z","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","version":3},"cited_work":{"arxiv_id":"1606.06160","doi":"10.48550/arxiv.1606.06160","metadata_source":"pith","pith_arxiv_id":"1606.06160","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","venue":"cs.NE","work_id":"ff82bd1e-0b64-4426-81a0-91594dba0a20","year":2016},"citing_paper":{"arxiv_id":"2605.25469","last_updated":"2026-05-25T06:19:49Z","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:19:49Z","title":"JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:53:39.926722Z"},"links":{"cited_paper":"/paper/1606.06160","citing_paper":"/paper/2605.25469"},"observation_digest":"sha256:98a68590d438ac3b26806f93880ef630e22e44ef44ccbdc3fed3e3e667ecef3a","observation_id":"bb48a7fc-11c6-4098-9e74-65ce03e1795f","resolution":{"observed_at":"2026-06-29T22:54:00.681250Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-06-29T22:53:39.926722Z","title":"Despite empirical success, most QAT methods inheritSTE’s bias, which ignores discrete bin geometry and can induce optimization mismatch at 2–4 bits","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25469","last_updated":"2026-05-25T06:19:49Z","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:19:49Z","title":"JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T22:53:39.926722Z"},"links":{"citing_paper":"/paper/2605.25469"},"observation_digest":"sha256:555cd47be08e1c12680da5fc5643b51f51df6350f50bcf5b9c6152cd52477deb","observation_id":"4830be8e-d92c-45e4-a83d-590f6d992fe3","resolution":{"observed_at":"2026-06-29T22:53:39.926722Z","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":"2026-06-29T22:53:39.926722Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.25469","last_updated":"2026-05-25T06:19:49Z","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:19:49Z","title":"JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T22:53:39.926722Z"},"links":{"citing_paper":"/paper/2605.25469"},"observation_digest":"sha256:24247a0cf14f78a3bb15ecf14c29031731882bdc55428d711e141f3549f06bd8","observation_id":"a2ee221a-c316-4682-9d3a-7edfa48ebe94","resolution":{"observed_at":"2026-06-29T22:53:39.926722Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.25469","last_updated":"2026-05-25T06:19:49Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:35:26.687529Z","submitted_at":"2026-05-25T06:19:49Z","title":"JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates"},"reference_resolution":{"displayed":4,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":4},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 0 inbound Pith citation observations for arXiv:2605.25469."}