{"as_of":"2026-08-21T10:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:99169d9604da12e828b7896fd427a7e28e47b2f85c1fd7f6c9380f524d2b4b42","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:52:24.558444Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T12:12:00.253732Z","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":"2026-07-04T08:09:41.092661Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"cited_work":{"arxiv_id":"2505.00259","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.00259","snapshot_observed_at":"2026-07-04T08:09:41.092661Z","title":"arXiv:2505.00259 (2025)","venue":null,"work_id":"503ec894-361b-4052-82c5-1e8d55fe2262","year":2025},"citing_paper":{"arxiv_id":"2606.21947","last_updated":"2026-06-20T08:33:55Z","snapshot_observed_at":"2026-08-19T23:43:38.990078Z","submitted_at":"2026-06-20T08:33:55Z","title":"ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T12:12:00.253732Z"},"links":{"cited_paper":"/paper/2505.00259","citing_paper":"/paper/2606.21947"},"observation_digest":"sha256:0c8e4ebfe094f6979e52b19e6437a8d6c17cb3203522e350aafed08225d16942","observation_id":"41e7f247-dc28-44a6-81f5-960ed6dae169","resolution":{"observed_at":"2026-07-04T08:09:41.094518Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.00259/citation-record","integrity":"/paper/2505.00259/integrity","json":"/paper/2505.00259/citation-record.json","paper":"/paper/2505.00259"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","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":"2026-08-16T04:52:24.381103Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.381103Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:80e1a3ba4be7876182b1bb5d24d7ecea2e198c44b9d2236154a955404149c437","observation_id":"2e27ca68-c87d-47cf-bd92-9c8445012dec","resolution":{"observed_at":"2026-08-16T04:52:24.381103Z","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":"2026-08-16T04:52:25.332676Z","title":"Training data-efficient image transformers & distillation through attention","venue":null,"work_id":"ed126609-e510-490a-83c8-120def78c60c","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.386257Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:7b83ac883b2a391399485e2c0f770302459a9ff5290dfc6d68da04f9dad3b158","observation_id":"78baf8f2-07be-404f-bd99-8b819b3c0d62","resolution":{"observed_at":"2026-08-16T04:52:25.337387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.317729Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"2704c57a-1647-4913-9fe9-15cca86a92bc","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.390412Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:9f2f1dbb33de05abd6d1e669898b6c0ddc75f4dba26744e3e8c359b6f506d177","observation_id":"5ec679aa-fde4-4c40-92be-ce3cf01e6529","resolution":{"observed_at":"2026-08-16T04:52:25.323229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.297681Z","title":"Mask R-CNN","venue":null,"work_id":"d16b4d88-4605-423e-b89b-cec3453c7b91","year":2017},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.394270Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:15d561390a1f485b5999d2c04c1a010107bec6d75de71838738fcbf54b6bcec1","observation_id":"95f32d39-6996-4ab7-aa0f-7df8bdd2972f","resolution":{"observed_at":"2026-08-16T04:52:25.303797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.281111Z","title":"Dual-mode learning for multi-dataset x-ray security image detection","venue":null,"work_id":"0a9af0bc-7d67-45bb-b32f-0e0600fef751","year":2024},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.398038Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:8c65b672e632b9089a25186098b101d8fbd5f5994cad40a6cf559638399d38de","observation_id":"5a0aa6ba-61ed-40b9-82f3-7d17f7bae701","resolution":{"observed_at":"2026-08-16T04:52:25.286867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.268072Z","title":"Segmenter: Transformer for semantic segmentation","venue":null,"work_id":"0fb79d86-5378-4f1a-ace6-0d6b3cac1c02","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.402237Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:d38e3786b575db7aa14cdd8c4b2abdd3f31520faf6712ea4abc81500e22b9909","observation_id":"2023ae31-545f-42b1-ad93-978ca08f018e","resolution":{"observed_at":"2026-08-16T04:52:25.272537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.254238Z","title":"Segformer: Simple and efficient design for semantic segmentation with transformers","venue":null,"work_id":"d396216c-2130-453d-a1ad-af5522eb4118","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.406327Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:6e678b6c78e7f14c0b65e46dfeb6db3d23a6a5a01e918ef5794e57c9cfc4c27b","observation_id":"d1c2539f-4f0e-42de-9eb2-9040cc83921b","resolution":{"observed_at":"2026-08-16T04:52:25.259458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.240013Z","title":"MobileNetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"f3ed346e-0d81-4a28-93b9-ec25a95291e4","year":2018},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.409984Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:86e6ddb18373d3b2d465c4168073b3e8abc7dbc41d8ba516cfff7a3212eb513c","observation_id":"f0bdfc52-f667-4b8c-9a19-550a717078be","resolution":{"observed_at":"2026-08-16T04:52:25.245017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.224242Z","title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","venue":null,"work_id":"3f74d59e-d97f-4995-a7dd-ef2944633584","year":2024},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.414033Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b11be646e330154a78b63ff293bf00c2392897228ccc36d121182c922dda2bd4","observation_id":"01d438e2-0d9b-48f3-b1f9-67bce38a7d85","resolution":{"observed_at":"2026-08-16T04:52:25.230118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.206728Z","title":"When sparse neural network meets label noise learning: A multistage learning framework","venue":null,"work_id":"eea03e41-8f23-4fc9-9c6f-9d40301df531","year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.417846Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:ea680009c91d552852671015c7e08a5a3af8df8d9bc5e0916d7c283bee89227f","observation_id":"7c492bcf-69ce-4807-add2-52596fdcfbd2","resolution":{"observed_at":"2026-08-16T04:52:25.212354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.192582Z","title":"Knowledge distillation: A survey","venue":null,"work_id":"abc257ff-d524-493c-a5b7-e4f07b774890","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.421444Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:5619cbdf8885e5359136c78ec2e7a08796e9b61e98f1f865aa4d9cc918982a3c","observation_id":"f5f0b569-744c-4a48-b366-804be3bb63fc","resolution":{"observed_at":"2026-08-16T04:52:25.197312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.178245Z","title":"Knowledge distillation meets label noise learning: Ambiguity-guided mutual label refinery","venue":null,"work_id":"d37ea190-a615-4896-9348-c8712b485150","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.425303Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:24dba2e90bf6d7cd84764349b45c90133058c65317611b13462b2921c286f1e2","observation_id":"3967669d-f679-42c6-8d0d-b268c42bd16e","resolution":{"observed_at":"2026-08-16T04:52:25.182754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08295","last_updated":"2021-06-15T17:12:42Z","snapshot_observed_at":"2026-08-19T05:42:08.537901Z","submitted_at":"2021-06-15T17:12:42Z","title":"A White Paper on Neural Network Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08295","snapshot_observed_at":"2026-08-16T04:52:24.429679Z","title":"A white paper on neural network quantization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.429679Z"},"links":{"cited_paper":"/paper/2106.08295","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:8b6b30775878ba712d6b713b938bfc43df3a76e07998c73ea9915c8ba6e8982f","observation_id":"cacc3f73-8ff6-46f8-9284-2e2257b4ce8a","resolution":{"observed_at":"2026-08-16T04:52:24.429679Z","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":"2026-08-16T04:52:25.164374Z","title":"BRECQ: Pushing the limit of post-training quantization by block reconstruction","venue":null,"work_id":"03d27407-eca9-453f-ae48-0cb96cc63e31","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.434111Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:fc6ce5b18e64efb016c924364dc71c2854649e505b2b6ee258b4b7d511c2f6b7","observation_id":"8ac7d36f-1295-486e-b5e3-5101ca4c4162","resolution":{"observed_at":"2026-08-16T04:52:25.169476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.148919Z","title":"Quantization and training of neural networks for efficient integer-arithmetic-only inference","venue":null,"work_id":"1c28ff3e-c8ac-4d24-99cc-cd4c7fb5b554","year":2018},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.438256Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:161f5eb76f54bdd534c739ad9d474e160ae5d0cff85ed6fd9d6a778de7ea284b","observation_id":"b68372bd-7cbe-4968-b040-21dc0a335b2f","resolution":{"observed_at":"2026-08-16T04:52:25.153329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.06085","last_updated":"2018-07-17T07:33:19Z","snapshot_observed_at":"2026-08-17T06:12:39.940205Z","submitted_at":"2018-05-16T01:19:43Z","title":"PACT: Parameterized Clipping Activation for Quantized Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.06085","snapshot_observed_at":"2026-08-16T04:52:24.441984Z","title":"PACT: Parameterized clipping activation for quantized neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.441984Z"},"links":{"cited_paper":"/paper/1805.06085","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:712937a0edfca7b544be7182692dd360228e17995cc4a8f20588568c4ab304f1","observation_id":"d78326d2-1716-4d9c-a847-65c416b30930","resolution":{"observed_at":"2026-08-16T04:52:24.441984Z","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":"2026-08-16T04:52:25.132918Z","title":"PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization","venue":null,"work_id":"19d45a76-2eee-4905-82a8-e24d3f4acbb4","year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.446540Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:eb3d40883d425f73f2adf87ce49fcd8bb2dc61d86c21096100bf976249546e3e","observation_id":"017a9546-9b03-4e75-9d43-757b688f3a22","resolution":{"observed_at":"2026-08-16T04:52:25.139196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.115716Z","title":"Up or down? adaptive rounding for post-training quantization","venue":null,"work_id":"0dd76fda-71ab-430e-b493-0eed1c5f8584","year":2020},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.450566Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:898822e959cd27d11a8f11ebe1047bef652c750a40eb4d8e00e8c6b8f520a52f","observation_id":"456b77d8-bda3-4bca-9e95-78200a0e2124","resolution":{"observed_at":"2026-08-16T04:52:25.121640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-08-19T23:43:09.365575Z","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":"2026-08-16T04:52:24.454792Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.454792Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:457012ef17447a7ee3a9d3f38864b2c4664f9bdad69f3a71e095d301e44b6313","observation_id":"031a4f9e-9828-4009-9c5f-fd2e315b64fc","resolution":{"observed_at":"2026-08-16T04:52:24.454792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08153","last_updated":"2020-05-07T03:30:49Z","snapshot_observed_at":"2026-08-18T09:17:55.370547Z","submitted_at":"2019-02-21T17:31:32Z","title":"Learned Step Size Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08153","snapshot_observed_at":"2026-08-16T04:52:24.459254Z","title":"Learned step size quantization","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.459254Z"},"links":{"cited_paper":"/paper/1902.08153","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:4821b661829a9f8fbdb80ce58b9a9f525da62e9f00b06830cb1a830b0a31efa5","observation_id":"b42b5c23-55db-449e-8eb9-4bd126922165","resolution":{"observed_at":"2026-08-16T04:52:24.459254Z","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":"2026-08-16T04:52:25.099707Z","title":"Overcoming oscillations in quantization-aware training","venue":null,"work_id":"0e82b5b4-0776-4ea2-bdb0-c0bc9d27eb78","year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.466075Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:811bf7c83499e4db94bd1f7d06697350614b9eb75433ceea4ffd58bff0792caa","observation_id":"98fcd5e1-36f3-4187-b4e4-446d0afc7518","resolution":{"observed_at":"2026-08-16T04:52:25.104729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.084162Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization, 2023","venue":null,"work_id":"0b236005-f562-41b8-b947-713006a84a0e","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.469948Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:c1fe1f39d3f2f8e65b5ba31c273d086851ebd1eb5659c88f49c97e32337e7135","observation_id":"9552335a-7979-47a3-8b9b-ce371cc7eefe","resolution":{"observed_at":"2026-08-16T04:52:25.088848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.068958Z","title":"NoisyQuant: Noisy bias- enhanced post-training activation quantization for vision transformers","venue":null,"work_id":"a343a0f7-8ca7-4361-9c53-7b9b207f6dd8","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.473717Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:1447c96fd10591750b6a54d14462cfb2690386754da3183b968ecd121e358462","observation_id":"4723f731-3410-4257-8e7b-b76e08ba125d","resolution":{"observed_at":"2026-08-16T04:52:25.074493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.052795Z","title":"Lightweight maize disease detection through post-training quantization with similarity preservation","venue":null,"work_id":"c36dc672-7817-4f73-b162-803540561aec","year":2024},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.477592Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:3115ffec618b002cbf481bd3d2aeb169e48529f643a8e90272280246d0d5c04b","observation_id":"a3b08cdd-6eb3-4593-a567-24dd2e711765","resolution":{"observed_at":"2026-08-16T04:52:25.058752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.037209Z","title":"PD-Quant: Post-training quantization based on prediction difference metric","venue":null,"work_id":"9e2f1dc9-7628-4963-995c-c14cd3a0fe9a","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.481079Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:ea64bf3fd33b75535cdbf8599ca667504d95ca92a61ee6b192750f0640af8581","observation_id":"4832a80d-f8c1-49a8-8d1d-a0297281d195","resolution":{"observed_at":"2026-08-16T04:52:25.041765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.023096Z","title":"Data-free quantization through weight equalization and bias correction","venue":null,"work_id":"29d44161-2439-4929-85c6-94ef7127727b","year":2019},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.485581Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:f7873432c0abb02d8a6cb6f03aa64ee0b7dcde02bbfcc1ff3c2a04d8e1417543","observation_id":"eade6f03-36d2-4598-9a57-37c3bf07b20e","resolution":{"observed_at":"2026-08-16T04:52:25.028128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:25.006203Z","title":"Towards mixed-precision quantization of neural networks via constrained optimization","venue":null,"work_id":"eefb8110-a203-459b-ba8b-5887d83c1bee","year":2021},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.489726Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:c67e3c336f14560cf36e261378c345257a7c7a1fb91752e2a01aa62ffa962bff","observation_id":"19f59496-8e3d-45ff-b351-93714df16c3b","resolution":{"observed_at":"2026-08-16T04:52:25.011965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.990355Z","title":"HAWQ: Hessian aware quantization of neural networks with mixed-precision","venue":null,"work_id":"eaffea8d-5b70-4c0f-9046-3263af76e5c4","year":2019},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.493544Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:fe7ac4ea1097fba89ba6f3ca51138c942929c79654d5725d838156050af38da5","observation_id":"24241c0e-01c3-4fa5-9082-22cf71ada95f","resolution":{"observed_at":"2026-08-16T04:52:24.995281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.974624Z","title":"HAWQ-v2: Hessian aware trace-weighted quantization of neural networks","venue":null,"work_id":"20b0a201-a64a-49c8-aa01-10088212e9bb","year":2020},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.497434Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b0d3d909c6b8fb590028dd023182aec7b1c638ea4c82a42555b60ea884ba84a4","observation_id":"c5735f17-46c3-42b4-8e45-03e7c7090420","resolution":{"observed_at":"2026-08-16T04:52:24.980022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.951941Z","title":"APTQ: Attention-aware post- training mixed-precision quantization for large language models","venue":null,"work_id":"215ac3c2-f846-41ba-abba-e18d67eb86a5","year":2024},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.501338Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:bdf8c7640c3d82405dba9e5254711228881b82aacfed933546148297fda616ce","observation_id":"b7fd4dcb-e0ed-4193-98f1-f5dbf1ff7a8e","resolution":{"observed_at":"2026-08-16T04:52:24.960240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.938145Z","title":"ImageNet classification with deep convolutional neural networks","venue":null,"work_id":"963f825d-5ba3-4152-8270-dbf93d9db62a","year":2012},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.505330Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:77332e51838dd4bac2313d707b274f393d4097b6f6ce10e22ef00049db5c0918","observation_id":"e6ba4fa2-1c8d-47e3-870e-119287a11811","resolution":{"observed_at":"2026-08-16T04:52:24.942524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.12322","last_updated":"2022-09-25T15:37:50Z","snapshot_observed_at":"2026-08-16T17:04:29.743456Z","submitted_at":"2022-04-26T14:02:04Z","title":"RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.12322","snapshot_observed_at":"2026-08-16T04:52:24.509294Z","title":"RapQ: Rescuing accuracy for power-of-two low-bit post-training quantization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.509294Z"},"links":{"cited_paper":"/paper/2204.12322","citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:5f189e48b12756f9563956195549be469856e6da0e6a30054973b5d41b3f2781","observation_id":"66748328-28c7-4e29-8f0c-bbb6cfeba6b2","resolution":{"observed_at":"2026-08-16T04:52:24.509294Z","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":"2026-08-16T04:52:24.923499Z","title":"Solving oscillation problem in post-training quantization through a theoretical perspective","venue":null,"work_id":"8f9d63d1-cd5e-40f7-91fd-ae9e78a7cc3b","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.513699Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:417f7c9d2503088c1098246dc44a40bac3371dce335be3339f54eb350cd01f9a","observation_id":"9b515ee5-a6a0-4b4c-9394-1b6c27460767","resolution":{"observed_at":"2026-08-16T04:52:24.928680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.907363Z","title":"Genie: Show me the data for quantization","venue":null,"work_id":"ac78f87e-613a-4afd-8e1e-6443b37b6869","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.517678Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b6a76e8570cc7cd6bf19d9004c44eafc68b8757d3d9ff0d188d67c6ea284c151","observation_id":"12b0460c-691b-44ec-bfb2-d8f1a457b329","resolution":{"observed_at":"2026-08-16T04:52:24.912375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.892479Z","title":"Repq-ViT: Scale reparameterization for post-training quantization of vision transformers","venue":null,"work_id":"79417ddf-6c6a-4c0c-a8b2-00eb4a8939fe","year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.521387Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:9e0743b39f7e66a89eb4bd8b2840228727898f1cc110c5dcbf0e432a79cb4991","observation_id":"cda6a03b-a0c4-47a1-9788-b08ded5936e5","resolution":{"observed_at":"2026-08-16T04:52:24.897058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.878329Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":"c0708679-76c0-4139-91b8-81530ae215d3","year":1912},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.525115Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:261f08abe483d2616c457b6ec80bc3723c8e336e4f899961c16471fc1717eb02","observation_id":"679e7265-738c-4155-a63e-faf6edc743e0","resolution":{"observed_at":"2026-08-16T04:52:24.882651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.529257Z","title":"I&S-ViT: An inclusive & stable method for pushing the limit of post-training vits quantization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.529257Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:1d657cc4063f56b4b43a55e242c4f97751a9fdb8617e532cd3515c2a9c23c879","observation_id":"c06f5081-b8b7-45a8-bbf5-ec76451d831c","resolution":{"observed_at":"2026-08-16T04:52:24.529257Z","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":"2026-08-16T04:52:24.863251Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"aade3f27-cea4-4cdd-a07e-187d82fd9c8a","year":2016},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.533150Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:3c86d5d0f7867eacecd672c1a3a2d455ced87874259c2dc385db3c2b5efc782c","observation_id":"f73ea687-cb72-46ba-b9f6-90b51203c1f2","resolution":{"observed_at":"2026-08-16T04:52:24.868462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.846627Z","title":"Designing network design spaces","venue":null,"work_id":"fc546a5a-1c56-4081-841e-bb640f2769dc","year":2020},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.536861Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b7b508ef1f5f9a54c25b26f652f60e6ec626d70c06a1390120185290e8abbe8a","observation_id":"ab305a89-2801-4eb2-bd7e-6704eb5b8fcf","resolution":{"observed_at":"2026-08-16T04:52:24.853117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.827691Z","title":"Mnasnet: Platform-aware neural architecture search for mobile","venue":null,"work_id":"ff3d5d27-97a8-44bb-bd3f-bfd539533bef","year":2019},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.540916Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:40852d53b477c32c09dc4e9e8f6da715fbadb8a853198a5bdc7164cccbd9e04e","observation_id":"44f4f8ed-5569-427d-886a-b39e7283c6a1","resolution":{"observed_at":"2026-08-16T04:52:24.833043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.814473Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":"d773c9f8-7645-4fe1-9561-b67c2bdba2b3","year":2017},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.544817Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b9c5afd2d1287879dab36e41c3d10dd25e3add58a243b0ad1a29781337bb8341","observation_id":"e070bd5d-32ed-42df-9e65-d0811400ec2c","resolution":{"observed_at":"2026-08-16T04:52:24.818854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.799799Z","title":"Texq: Zero-shot network quantization with texture feature distribution calibration","venue":null,"work_id":"4622c002-69aa-4c0c-89c1-e54c7af61f5a","year":2024},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.549923Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:04a2d1330e4e487d6bb4da96cc10b058d264d074e1db18da9aab784ffaf8314a","observation_id":"db5144f7-717d-4e14-a38d-6ee2a2b0798f","resolution":{"observed_at":"2026-08-16T04:52:24.804667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.784014Z","title":"AdaLog: Post-training quantization for vision transformers with adaptive logarithm quantizer","venue":null,"work_id":"4027e745-0a81-452f-be36-a13803c91b16","year":2025},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.554133Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:b684d58f6da8e2f76102c9144307b8bc93622689e39a5a06e4255aa47198cc28","observation_id":"c87dc7de-877f-406c-b0e0-bd647b123578","resolution":{"observed_at":"2026-08-16T04:52:24.788921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T04:52:24.767650Z","title":"Towards accu- rate post-training quantization for vision transformer","venue":null,"work_id":"a915a866-ed52-4687-a2e9-3af635ad8a39","year":2022},"citing_paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T04:52:24.558444Z"},"links":{"citing_paper":"/paper/2505.00259"},"observation_digest":"sha256:a5d8dd58fd8bf97a1d941d9ea737e829a851fd754524679992f75022d82c0894","observation_id":"e9c21708-c98b-4887-975a-c6cd30d21bb6","resolution":{"observed_at":"2026-08-16T04:52:24.774297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.00259","last_updated":"2025-05-01T02:53:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T17:09:35.257612Z","submitted_at":"2025-05-01T02:53:46Z","title":"Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":37},"total_outbound_references":44},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2505.00259."}