{"as_of":"2026-08-08T22:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91d24f896be1f295209d91743431148dcbdc2f6d764598efb72cc43170ed505a","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":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":19,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T05:32:34.858441Z","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":"2026-07-02T08:16:48.290504Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-08T05:32:34.858441Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.08355","last_updated":"2025-02-12T12:30:49Z","snapshot_observed_at":"2026-08-08T05:23:46.644746Z","submitted_at":"2025-02-12T12:30:49Z","title":"Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T05:32:34.858441Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2502.08355"},"observation_digest":"sha256:85cca724e63adc44d3ecd22f554521d84aeb449daf1c26eaca840b3c7731fbe8","observation_id":"01dbd76a-5b27-40b5-a481-ed0c621267fc","resolution":{"observed_at":"2026-08-08T05:32:34.858441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2503.03088","last_updated":"2026-04-08T13:11:29Z","snapshot_observed_at":"2026-07-31T21:51:41.767129Z","submitted_at":"2025-03-05T01:04:45Z","title":"AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-23T01:48:34.562325Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2503.03088"},"observation_digest":"sha256:1a2d50cfff1e6e1e5e6d3775144d713f35276e0fa6160b20a498bae71ffda79f","observation_id":"481baac7-9b24-4986-a7a1-9e1e883658e2","resolution":{"observed_at":"2026-05-23T01:52:23.058469Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-07T15:09:28.666178Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantiza- tion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16210","last_updated":"2025-05-22T04:23:19Z","snapshot_observed_at":"2026-08-08T09:52:35.968307Z","submitted_at":"2025-05-22T04:23:19Z","title":"NQKV: A KV Cache Quantization Scheme Based on Normal Distribution Characteristics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:28.666178Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2505.16210"},"observation_digest":"sha256:2b82faebfc0084b2a71dc9997a180c9c73582caa7a73bd5ceffdb4e8ca1513f5","observation_id":"03b3f273-7919-4734-be7f-655c8e8fd9ec","resolution":{"observed_at":"2026-08-07T15:09:28.666178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-07T11:52:58.400598Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01221","last_updated":"2025-06-02T00:12:50Z","snapshot_observed_at":"2026-08-08T11:28:40.441673Z","submitted_at":"2025-06-02T00:12:50Z","title":"Flexible Mixed Precision Quantization for Learned Image Compression","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:52:58.400598Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2506.01221"},"observation_digest":"sha256:83a63c360c5ae785e3416520e9d61d6f9a2bbef5d657916ca3e50595149d7c7f","observation_id":"01cb48ca-6b42-4ac9-979e-2ce1dd577268","resolution":{"observed_at":"2026-08-07T11:52:58.400598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-07T11:51:37.954450Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01229","last_updated":"2025-06-02T00:40:53Z","snapshot_observed_at":"2026-08-08T13:14:35.815700Z","submitted_at":"2025-06-02T00:40:53Z","title":"Structured Pruning and Quantization for Learned Image Compression","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:51:37.954450Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2506.01229"},"observation_digest":"sha256:77f4c1adfb37fe6e220ae4a33362203f3f13ff77da0ef9312db2a274b520920c","observation_id":"13ce9926-2cf7-4b77-922f-3ce20e3d14af","resolution":{"observed_at":"2026-08-07T11:51:37.954450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-07T04:22:43.645464Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10840","last_updated":"2025-06-12T15:57:14Z","snapshot_observed_at":"2026-08-07T23:42:57.255857Z","submitted_at":"2025-06-12T15:57:14Z","title":"Post-Training Quantization for Video Matting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T04:22:43.645464Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2506.10840"},"observation_digest":"sha256:28ae23df8efc3cb74796b8eb9dc69aa3e2a52b40aaea72560ca4dd0ae11c44e2","observation_id":"eb03d6f8-4d42-4610-8f0f-172e92cedf7e","resolution":{"observed_at":"2026-08-07T04:22:43.645464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T23:42:35.025715Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:35.025715Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:2ed9548f9489e1d330fee4721ac905004535db81a81f2096b6c3ce02b0d6ac5b","observation_id":"f1688ff2-f41e-42d8-bca7-1aa5554578cc","resolution":{"observed_at":"2026-08-06T23:42:35.025715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T19:57:20.219566Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04290","last_updated":"2025-07-06T08:16:50Z","snapshot_observed_at":"2026-08-08T08:13:06.170937Z","submitted_at":"2025-07-06T08:16:50Z","title":"MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:57:20.219566Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2507.04290"},"observation_digest":"sha256:e324aecbf50cd84f41235932b104768a2d0ccca0879494831d79d4856b53b68b","observation_id":"5795d9c2-4698-4cd4-abac-13eb26129ca2","resolution":{"observed_at":"2026-08-06T19:57:20.219566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T18:20:01.113405Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08765","last_updated":"2025-07-11T17:21:06Z","snapshot_observed_at":"2026-08-07T23:42:59.261042Z","submitted_at":"2025-07-11T17:21:06Z","title":"Compress Any Segment Anything Model (SAM)","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T18:20:01.113405Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2507.08765"},"observation_digest":"sha256:6ebeb9187a6b699e3076e3ded5749ef8646abd27110484a6a0a3fa5b4c3775a1","observation_id":"f9313f3f-3547-4483-9261-2d551fef9c30","resolution":{"observed_at":"2026-08-06T18:20:01.113405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T15:05:21.552478Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16782","last_updated":"2025-07-22T17:28:29Z","snapshot_observed_at":"2026-08-08T03:07:53.331031Z","submitted_at":"2025-07-22T17:28:29Z","title":"Task-Specific Zero-shot Quantization-Aware Training for Object Detection","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T15:05:21.552478Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2507.16782"},"observation_digest":"sha256:8acbe480f89e6ee772cad25dafb98ee7e42ff2a014a5b842fa440b9740002193","observation_id":"42340851-4780-4a8b-8f4f-4317bc677ecb","resolution":{"observed_at":"2026-08-06T15:05:21.552478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T12:18:19.977400Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.21947","last_updated":"2025-07-29T16:00:20Z","snapshot_observed_at":"2026-08-06T12:18:16.562236Z","submitted_at":"2025-07-29T16:00:20Z","title":"Enhancing Generalization in Data-free Quantization via Mixup-class Prompting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:18:19.977400Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2507.21947"},"observation_digest":"sha256:d4e5401d3e539153219bc06b595e4bf92c74d0d39fdc33f1430a81c515325638","observation_id":"d608fdd7-7355-40d9-8e94-cd3bf28f21e2","resolution":{"observed_at":"2026-08-06T12:18:19.977400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2604.16855","last_updated":"2026-07-15T11:03:07Z","snapshot_observed_at":"2026-08-02T16:10:53.002900Z","submitted_at":"2026-04-18T06:05:20Z","title":"When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T06:48:46.239804Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2604.16855"},"observation_digest":"sha256:cf982551a3bdc2c8096b05042af091725f848d8119a3a5ace150ab293299bb3c","observation_id":"fc23cf6f-f278-4dc4-89ac-1f56cd512da9","resolution":{"observed_at":"2026-05-10T06:51:46.061390Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-02T16:10:57.543897Z","title":"In: ICLR","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.16855","last_updated":"2026-07-15T11:03:07Z","snapshot_observed_at":"2026-08-02T16:10:53.002900Z","submitted_at":"2026-04-18T06:05:20Z","title":"When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T16:10:57.543897Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2604.16855"},"observation_digest":"sha256:2f45f9b489c1433667abd4601041c8b886d13d7ef5b4eb66d5e906d75f993aba","observation_id":"9647a2e2-8e7a-4499-bd62-7f6c08853bf1","resolution":{"observed_at":"2026-08-02T16:10:57.543897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2605.16423","last_updated":"2026-05-14T14:55:46Z","snapshot_observed_at":"2026-07-06T23:27:34.514541Z","submitted_at":"2026-05-14T14:55:46Z","title":"Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-20T20:55:10.360775Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2605.16423"},"observation_digest":"sha256:99bbdea41c4e9923ad378690e47806f1a843f04f3c9cd90108951fe1762f2b1d","observation_id":"bceef409-cd65-4933-9119-2c0e151c3633","resolution":{"observed_at":"2026-05-20T20:59:01.892057Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2605.16901","last_updated":"2026-05-16T09:25:23Z","snapshot_observed_at":"2026-07-06T23:27:57.805018Z","submitted_at":"2026-05-16T09:25:23Z","title":"CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-19T21:30:10.628872Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2605.16901"},"observation_digest":"sha256:326a1f3c6384e9660a83146dfab476067334bf5bf447cb2c0b0eb25249072d7d","observation_id":"dd8d07a8-aee8-4afa-87fd-d632407d86b8","resolution":{"observed_at":"2026-05-19T21:32:47.896291Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2605.17997","last_updated":"2026-05-18T07:51:49Z","snapshot_observed_at":"2026-07-06T23:28:55.079333Z","submitted_at":"2026-05-18T07:51:49Z","title":"MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-20T12:56:48.177386Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2605.17997"},"observation_digest":"sha256:36f12d4a143f014f50bec157090f14ec00bc9b2243da83028e9b44c842528b8b","observation_id":"391be1b4-f984-48d2-b99c-d145c673b518","resolution":{"observed_at":"2026-05-20T12:58:17.771436Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2605.26092","last_updated":"2026-05-28T05:26:15Z","snapshot_observed_at":"2026-08-06T19:30:28.330206Z","submitted_at":"2026-05-25T17:52:46Z","title":"GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T22:16:51.663800Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2605.26092"},"observation_digest":"sha256:c5987a92933a8e0970b88780c6b8d329d8fc30671db661283e47d327e2ccaf6c","observation_id":"b296fcaa-424e-4950-81bd-7e4cf0dd5164","resolution":{"observed_at":"2026-06-29T22:24:00.722123Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":"2203.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-02T08:16:48.290504Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":"7ec52667-7b82-4b3c-b5ef-7f45a81015f9","year":2022},"citing_paper":{"arxiv_id":"2606.05429","last_updated":"2026-06-03T20:51:52Z","snapshot_observed_at":"2026-08-06T01:16:12.101022Z","submitted_at":"2026-06-03T20:51:52Z","title":"Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T06:09:42.838355Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2606.05429"},"observation_digest":"sha256:b08748b05c4e24512a9f35bd961128fe1d1fa41a9a5441b0b98004a9a28a4f45","observation_id":"4f826c91-87c5-4aa7-bcbc-6da7e0d860a1","resolution":{"observed_at":"2026-07-02T08:16:48.292159Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-07-31T02:57:29.293074Z","title":"Qdrop: Randomly dropping quantiza- tion for extremely low-bit post-training quantization.ArXiv, abs/2203.05740, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28589","last_updated":"2026-07-30T17:43:36Z","snapshot_observed_at":"2026-08-03T00:12:32.913568Z","submitted_at":"2026-07-30T17:43:36Z","title":"MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-31T02:57:29.293074Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2607.28589"},"observation_digest":"sha256:bb68fad41682c5c27a532bd5f56a7493ba330a203d23e7245ddb8f41f37af255","observation_id":"876428d7-64c8-44d9-85fd-0ac3d57af8f6","resolution":{"observed_at":"2026-07-31T02:57:29.293074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2203.05740/citation-record","integrity":"/paper/2203.05740/integrity","json":"/paper/2203.05740/citation-record.json","paper":"/paper/2203.05740"},"outbound":[],"paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2203.05740."}