{"as_of":"2026-08-18T08:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:98433e7d1cdf195f1c8c3f9ec592346da4c803a5f5f60f1820f1acd199acc23f","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:26:47.127024Z","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":"2026-05-20T19:33:55.748295Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.06126","snapshot_observed_at":"2026-08-08T04:52:43.752151Z","title":"Neural network quantization for efficient inference: A sur- vey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.08692","last_updated":"2025-02-12T15:51:39Z","snapshot_observed_at":"2026-08-17T03:26:48.184241Z","submitted_at":"2025-02-12T15:51:39Z","title":"Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T04:52:43.752151Z"},"links":{"cited_paper":"/paper/2112.06126","citing_paper":"/paper/2502.08692"},"observation_digest":"sha256:2f37d129b0eac8ebb9e57ea256d76b346f0a44e920ce5ff885500466752c380e","observation_id":"2d118b4c-5729-4657-876c-cab429e244d6","resolution":{"observed_at":"2026-08-08T04:52:43.752151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.06126","snapshot_observed_at":"2026-08-15T20:26:47.127024Z","title":"org/abs/2112.06126","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.13060","last_updated":"2025-05-19T12:51:02Z","snapshot_observed_at":"2026-08-18T02:42:11.323945Z","submitted_at":"2025-05-19T12:51:02Z","title":"Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:26:47.127024Z"},"links":{"cited_paper":"/paper/2112.06126","citing_paper":"/paper/2505.13060"},"observation_digest":"sha256:ea59a2a2a96fa0ae1d731d021b0e91253705dfd2461aabe45bfd0eec0eeae2ec","observation_id":"21e09a4c-352b-430e-8a12-4d6480de9e8a","resolution":{"observed_at":"2026-08-15T20:26:47.127024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey","version":2},"cited_work":{"arxiv_id":"2112.06126","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.06126","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2112.06126 , year=","venue":null,"work_id":"27e7ffff-275b-4490-9349-f0c9d15bc933","year":null},"citing_paper":{"arxiv_id":"2510.09696","last_updated":"2026-05-01T09:17:59Z","snapshot_observed_at":"2026-08-06T02:09:22.715677Z","submitted_at":"2025-10-09T15:17:13Z","title":"Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T08:38:52.367887Z"},"links":{"cited_paper":"/paper/2112.06126","citing_paper":"/paper/2510.09696"},"observation_digest":"sha256:15dce2cd80df90dc16d0e3e3c6264f5258fc7ad9481a2011503e020e33ef962d","observation_id":"9b7b93d1-975e-4ebc-86af-4ed9e49e2b90","resolution":{"observed_at":"2026-05-18T08:41:08.413337Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey","version":2},"cited_work":{"arxiv_id":"2112.06126","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.06126","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2112.06126 , year=","venue":null,"work_id":"27e7ffff-275b-4490-9349-f0c9d15bc933","year":null},"citing_paper":{"arxiv_id":"2605.16138","last_updated":"2026-07-29T21:46:56Z","snapshot_observed_at":"2026-08-16T13:20:23.026516Z","submitted_at":"2026-05-15T16:18:43Z","title":"SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-20T19:33:41.365384Z"},"links":{"cited_paper":"/paper/2112.06126","citing_paper":"/paper/2605.16138"},"observation_digest":"sha256:4702bdb6abb39dd92a28bce2a26fb80d09b4da2731c39bf9fd70cf6415265fcb","observation_id":"784cdaf8-6ab5-4037-82e6-9e1e44c2de02","resolution":{"observed_at":"2026-05-20T19:33:55.751909Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.06126","snapshot_observed_at":"2026-07-11T17:52:29.131365Z","title":"Neural Network Quantization for Ef- ficient Inference: A Survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.04531","last_updated":"2026-07-05T22:25:52Z","snapshot_observed_at":"2026-08-16T03:26:44.618521Z","submitted_at":"2026-07-05T22:25:52Z","title":"Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-11T17:52:29.131365Z"},"links":{"cited_paper":"/paper/2112.06126","citing_paper":"/paper/2607.04531"},"observation_digest":"sha256:7573033179e35dabf259a8a68df725531575fe8e479babfedf7f030842550490","observation_id":"c60f0486-b71f-4218-9cf2-cf0a4cc3135f","resolution":{"observed_at":"2026-07-11T17:52:29.131365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2112.06126/citation-record","integrity":"/paper/2112.06126/integrity","json":"/paper/2112.06126/citation-record.json","paper":"/paper/2112.06126"},"outbound":[],"paper":{"arxiv_id":"2112.06126","last_updated":"2023-01-18T00:43:34Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:36:03.107660Z","submitted_at":"2021-12-08T22:49:39Z","title":"Neural Network Quantization for Efficient Inference: A Survey"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2112.06126."}