{"as_of":"2026-08-16T07:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f39ef2bc8751900da050ec37b2b3c291dd5082ff2b7f7845a719dccb3d4d3128","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:45:37.855925Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.17768/citation-record","integrity":"/paper/2507.17768/integrity","json":"/paper/2507.17768/citation-record.json","paper":"/paper/2507.17768"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:34.486960Z","title":"Remote sensing image scene classifi- cation: Benchmark and state of the art,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.486960Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:7e093240e7980d0d03f1b4353c277ef7e80d9e6a262fcdf8080f7b832f4a8bea","observation_id":"8086d831-ac6e-44b6-977d-015f56e2df54","resolution":{"observed_at":"2026-08-06T16:45:34.486960Z","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":"2026-08-06T16:45:38.144628Z","title":"Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,","venue":null,"work_id":"c75746b0-52ab-443b-bb76-929ddaad78a5","year":2017},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.588289Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:26cefb04f4ff7f8de4187785aad40709971a12bbee62706e181bdf910d4f0eb7","observation_id":"87b1d903-4ba2-47ba-b839-21664101efe6","resolution":{"observed_at":"2026-08-06T16:45:38.147138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.138981Z","title":"Efficient acceleration of deep learning inference on resource-constrained edge devices: A review,","venue":null,"work_id":"d7c98bcf-0a64-4cc0-833e-027db3151c46","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.701413Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:c5b790f0bdf92f73ef7ee8baeed9218b58045bb2baee60b3c9c480ba3c5310eb","observation_id":"13d0180d-34fc-4071-9851-05dc8061027b","resolution":{"observed_at":"2026-08-06T16:45:38.141053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.132687Z","title":"A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities,","venue":null,"work_id":"50173be1-ad61-4a45-8f1e-7f7fac9f771a","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.786281Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:eec50faea045ca342c9d298cfbe29fafee04ea4904ea8b259a03f02048ec6e96","observation_id":"903a33c9-763a-49a4-b8d0-ea2775ac8e37","resolution":{"observed_at":"2026-08-06T16:45:38.135107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-06T16:45:34.859287Z","title":"Mobilenets: Efficient convo- lutional neural networks for mobile vision applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.859287Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:f75318f32eab447af7b8d4356a023c88a50491cba96616c3ad15ff48c9689e14","observation_id":"d74ce28b-3d3e-4ed9-b952-af5a883b0964","resolution":{"observed_at":"2026-08-06T16:45:34.859287Z","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":"2026-08-06T16:45:38.125833Z","title":"Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,","venue":null,"work_id":"0ddb5509-c1ea-4e63-a899-cafc69b13a7f","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:34.959375Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:13cad1065a6478e662fd3c529148a01f61fc5a06760ce79e7e23d26958f6d6ba","observation_id":"0cc9fe7d-9b3f-41b2-8c0c-3c88509b17d8","resolution":{"observed_at":"2026-08-06T16:45:38.128491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.119963Z","title":"Bi-deepvit: Binarized transformer for efficient sensor-based human activity recognition,","venue":null,"work_id":"b40bc89a-d37d-4cf0-a8e1-2341d72ca927","year":2025},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.018385Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:24ad1098d8516d36f6387d3c868cec3b885973da25001490ccdb661d4d111ae9","observation_id":"8336e0e7-7a4f-46ec-879a-9483402b370b","resolution":{"observed_at":"2026-08-06T16:45:38.122154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.113754Z","title":"Post-training piecewise linear quantization for deep neural networks,","venue":null,"work_id":"23c2ffb1-57df-4a77-a2df-8adbca309547","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.077318Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:5e850df849dfaa343099e8bf3dd4fc8eb5a6b83c1a9ed2700a2e8cda16683a03","observation_id":"82103bcd-dcf0-4d38-9940-78f591203bec","resolution":{"observed_at":"2026-08-06T16:45:38.115971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.107850Z","title":"Towards accurate post- training network quantization via bit-split and stitching,","venue":null,"work_id":"db476756-4fdb-4437-a168-faa2e5ff98f6","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.130126Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:bbc9916783b4f24f1ed624adb2cf0b72ea082b56561ebadb38d2b0299c68d2f8","observation_id":"8370ad33-bd7a-465b-9371-f3ba4970a741","resolution":{"observed_at":"2026-08-06T16:45:38.110019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06160","last_updated":"2018-02-02T01:43:54Z","snapshot_observed_at":"2026-08-15T08:38:24.917341Z","submitted_at":"2016-06-20T15:02:31Z","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.06160","snapshot_observed_at":"2026-08-06T16:45:35.200433Z","title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.200433Z"},"links":{"cited_paper":"/paper/1606.06160","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:de3676a0c9b6957548a4d78d716dab5346009c127dd917a40eb764e3c2089b42","observation_id":"af801f44-1f7e-466c-9971-5d45aeffeb0a","resolution":{"observed_at":"2026-08-06T16:45:35.200433Z","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":"2026-08-06T16:45:38.100386Z","title":"Learned step size quantization,","venue":null,"work_id":"8c27d387-fd78-4699-b814-068ede669f94","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.271279Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:0d5d99f9fba405e415f0a290dd6c002fc9b7a09bec1da9182c487234173be3e3","observation_id":"345c6ffc-3249-4def-af03-50abc50f5eee","resolution":{"observed_at":"2026-08-06T16:45:38.103526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.093172Z","title":"Herding dynamical weights to learn,","venue":null,"work_id":"43583b37-89a3-474e-af6a-a3c0e7d118da","year":2009},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.351990Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:242303b0d07b3689caf3afff188d1f45b588f1da2fdcbbc48ed776ad5722d905","observation_id":"fd18cfdb-48ae-4bb9-a59c-0eb42b8229dd","resolution":{"observed_at":"2026-08-06T16:45:38.096173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.085823Z","title":"Deep learning on a data diet: Finding important examples early in training,","venue":null,"work_id":"058ea57d-6acf-4bd2-aa6b-c2af7e7ad5c8","year":2021},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.424312Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:e891dfb3792db7faa907916a1c59598456461f1f2a37a1bcfb7a079e1f2a5f82","observation_id":"a3178b05-0995-4022-af0b-a37dca1d92c4","resolution":{"observed_at":"2026-08-06T16:45:38.089034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.078925Z","title":"Grad- match: Gradient matching based data subset selection for efficient deep model training,","venue":null,"work_id":"dfcdbb83-ab18-4ee3-98c2-420885242c6c","year":2021},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.485476Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:c97c3e2ed2daca3b92c6ceed16caae58aedc534a6c8ad78a9dcf149c0a2972eb","observation_id":"04f5549b-ecb9-455f-84b6-4b95e00a1d88","resolution":{"observed_at":"2026-08-06T16:45:38.081404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.072181Z","title":"Beyond neural scaling laws: beating power law scaling via data pruning,","venue":null,"work_id":"8fb53470-3c83-42f3-991f-d8d2fc32e78c","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.748409Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:1ce60107c1cd7ece043151d87f930c1911382b5eca7791755037d52e6c4c4415","observation_id":"d48ba3b6-9140-4f73-82d7-c03975b7222c","resolution":{"observed_at":"2026-08-06T16:45:38.074774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.064971Z","title":"Robust and efficient quantization-aware training via coreset selection,","venue":null,"work_id":"9f09fbc4-652f-4c03-8942-62fefad6ad96","year":2024},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.804790Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:0a90154562bab421bf7c8768f67b1f441e3851777db37dba7ba87450a0c23c44","observation_id":"30fe4e67-a3a8-481b-bc84-82fbfc8dff26","resolution":{"observed_at":"2026-08-06T16:45:38.067573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.058664Z","title":"Q-vit: Accurate and fully quantized low-bit vision transformer,","venue":null,"work_id":"a1fa3047-6b4d-46ed-9052-4f7a56a5cec6","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.881540Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:6ecfcc3d16e9578884931c754cfdc1e364186a8847edec958f445aba2d08969f","observation_id":"6f697fe0-b9a3-4cf2-ad21-56a260202c21","resolution":{"observed_at":"2026-08-06T16:45:38.060920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.052507Z","title":"Over- coming oscillations in quantization-aware training,","venue":null,"work_id":"d5288cfb-b32a-4dc2-b78e-6c0947653a15","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:35.958371Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:9703d26ff4010867c96952a69e04a59215bacc4d5b31f0139b981963d5be12d6","observation_id":"a2d9ac30-763e-4168-9519-cf2f4d3c28da","resolution":{"observed_at":"2026-08-06T16:45:38.054649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-06T16:45:36.028662Z","title":"Estimating or propagating gradients through stochastic neurons for conditional computation,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.028662Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:0467fedd87964c0836fe3032c09ae47e25b3f57b35895521ee4f825bd9052a7e","observation_id":"4aa9b46a-2e19-4c84-ae16-7f7210c6edda","resolution":{"observed_at":"2026-08-06T16:45:36.028662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:36.106639Z","title":"Crossvit: Cross-attention multi- scale vision transformer for image classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.106639Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:c07a70315bc6b8b8740741d893d251a9b8a99743b028070f5779d20a1d998b88","observation_id":"16a00319-b3e8-402d-87b0-d9bbdf29e480","resolution":{"observed_at":"2026-08-06T16:45:36.106639Z","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":"2026-08-06T16:45:38.042677Z","title":"Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,","venue":null,"work_id":"013874ff-0d85-47bf-8f11-e2cfd80b0474","year":2018},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.165531Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:5370b74bc79e22cf35e930f1f69c59f76301e499fe69b5c555a434241831bd35","observation_id":"582359c5-6a32-4ce2-ac1d-c9d6ecdc7ef3","resolution":{"observed_at":"2026-08-06T16:45:38.044867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.036515Z","title":"Sdq: Stochastic differentiable quantization with mixed precision,","venue":null,"work_id":"8a78fd76-6c4a-44d3-8eb1-75484c26a9ab","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.275467Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:a3db3c31b3e170e2e0dcec0f5c9c0a3495a6f5f66a2db51dc9ecb7eaedd31e7f","observation_id":"59f18a7e-eb34-4e98-a8ba-4c7f4a2f865f","resolution":{"observed_at":"2026-08-06T16:45:38.038768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05852","last_updated":"2017-11-15T23:45:59Z","snapshot_observed_at":"2026-08-14T20:13:12.511047Z","submitted_at":"2017-11-15T23:45:59Z","title":"Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05852","snapshot_observed_at":"2026-08-06T16:45:36.437127Z","title":"Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.437127Z"},"links":{"cited_paper":"/paper/1711.05852","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:52f9f36071e16cc8fbaa8170c5f7f6d649230e03ee572b648ad59c85728fd7ab","observation_id":"0f3bac0c-a7f4-4825-9922-41101bea328c","resolution":{"observed_at":"2026-08-06T16:45:36.437127Z","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":"2026-08-06T16:45:38.029737Z","title":"Oscillation-free quantization for low-bit vision transformers,","venue":null,"work_id":"c1366c05-6f0a-4ba3-8d55-9ffb597d8060","year":2023},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.541397Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:e1c652701c6362e5fc1bdf90d4c19d0bbcf78ab0eee79dd859a6eeabb5fae21e","observation_id":"ab0dff3e-70b5-45a7-a2ba-0fdcf85ef489","resolution":{"observed_at":"2026-08-06T16:45:38.032630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.022830Z","title":"Moderate coreset: A universal method of data selection for real-world data- efficient deep learning,","venue":null,"work_id":"afcbffae-fac5-4ff0-9e40-794cf909ef84","year":2023},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.657576Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:091761d1bfd0a71545ebd53e226ca2ebfccc7539ba950bc8f1fec0697530021f","observation_id":"d6508c4e-64d8-424e-844b-e52af66b1eaa","resolution":{"observed_at":"2026-08-06T16:45:38.025404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.016216Z","title":"Contextual diversity for active learning,","venue":null,"work_id":"a9811700-65a5-4faa-8532-e6f00b02a2f4","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.812512Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:fae4620efa32f37ba26f80365af36b585046506a50249314083e3bdcfd569485","observation_id":"677ea887-7714-4007-b722-d8e2ae3c1c50","resolution":{"observed_at":"2026-08-06T16:45:38.018383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.009704Z","title":null,"venue":null,"work_id":"8293ad60-93f9-461a-8373-15051049b148","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:36.975602Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:b572f25d431342d3f2f6cdd4f767f641a4a8916fb392dad8e2443cebfdd7e460","observation_id":"05aebef5-2a62-466f-b670-b4d552e191f2","resolution":{"observed_at":"2026-08-06T16:45:38.011894Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:38.002998Z","title":"An empirical study of example forgetting during deep neural network learning,","venue":null,"work_id":"b5ed511c-9597-4e0a-891d-1d4bb630d433","year":2019},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.120471Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:1d6eb9aca146989454ea162075ba00a108881c8053d4ffd17eb89f33696601d0","observation_id":"f0e9558c-56a2-4bc7-841c-690c84b0f658","resolution":{"observed_at":"2026-08-06T16:45:38.005371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.996615Z","title":"Hard sample matters a lot in zero-shot quantization,","venue":null,"work_id":"ff87cc3b-cfcc-4138-8432-e5a06b836364","year":2023},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.231575Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:381b3533375646e0c9918b1038dd58a0c92fe822ef611e6210c05f035f0ed410","observation_id":"64ee4093-b5a2-4940-9c3a-2ef71bdb3901","resolution":{"observed_at":"2026-08-06T16:45:37.999037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.15704","last_updated":"2020-06-28T20:39:45Z","snapshot_observed_at":"2026-07-06T09:33:26.082100Z","submitted_at":"2020-06-28T20:39:45Z","title":"PyTorch Distributed: Experiences on Accelerating Data Parallel Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.15704","snapshot_observed_at":"2026-08-06T16:45:37.393460Z","title":"Pytorch dis- tributed: Experiences on accelerating data parallel training,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.393460Z"},"links":{"cited_paper":"/paper/2006.15704","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:936d8cc6bacebc877d4b5b062cd63ed02d25605c51a7a9af515329e163321e68","observation_id":"bf28da72-d2f3-42e4-a134-0004ac472cbc","resolution":{"observed_at":"2026-08-06T16:45:37.393460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:37.589311Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.589311Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:4b43d505efc4e7d7e8263c170c11b0a92aba3a2d32f82820cf7e6ea01c18e40d","observation_id":"45df4467-1017-4526-986a-10a22d0d9820","resolution":{"observed_at":"2026-08-06T16:45:37.589311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:37.751551Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.751551Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:620954652248f7fc720d24fb8a9f0b14e6e83ad2c9535c9a237f7f8429199eb0","observation_id":"289bff4e-fc3b-4629-9c5b-ee02e0fd4ffa","resolution":{"observed_at":"2026-08-06T16:45:37.751551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:37.827465Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.827465Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:b2d6745c02628532dc10532ecab4e9b853db2cb0cbef030f28ca4c3e32f5f39b","observation_id":"464ed32a-7877-43bb-af4b-80924b7f3777","resolution":{"observed_at":"2026-08-06T16:45:37.827465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:37.829620Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.829620Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:3120ecaa3a1f240f4064853bd8fcccc66b50c185bbe4d171fb06750de1dc4391","observation_id":"12f3af05-caf1-4820-8ff2-f73d6230d163","resolution":{"observed_at":"2026-08-06T16:45:37.829620Z","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":"2026-08-06T16:45:37.975259Z","title":"Lsq+: Improving low-bit quantization through learnable offsets and better initialization,","venue":null,"work_id":"23883eff-8195-479d-85cc-87b55a174ddc","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.831753Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:2a4aaad5fddedd7411813c9e14d5f5b75c0e6f30736771dee2d37a86c7566f1e","observation_id":"338992b7-8ca1-4020-be04-e88af95da673","resolution":{"observed_at":"2026-08-06T16:45:37.977530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.968355Z","title":"Communication-efficient satellite-ground federated learning through progressive weight quantization,","venue":null,"work_id":"eb2d148d-1ce5-4483-923f-15cfc033faf3","year":2024},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.833780Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:6fe94f064c1f55d93bb50dcd1060c60eb1a59fe54a84527df779a585f2b5a273","observation_id":"9b2a376e-452e-4d94-84eb-916e4b0a6004","resolution":{"observed_at":"2026-08-06T16:45:37.970824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.961666Z","title":"Binarized neural network for edge intelligence of sensor-based human activity recognition,","venue":null,"work_id":"736c987c-0c54-4fe7-8088-259541f597ec","year":2021},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.836047Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:4699913ce66309ba5532c208a7f4571a1e2ac08e8206d61dfcee7b67c194ea04","observation_id":"63fa41b9-6f6d-4455-944c-d407c74b6832","resolution":{"observed_at":"2026-08-06T16:45:37.963973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09841","last_updated":"2018-02-27T12:02:33Z","snapshot_observed_at":"2026-08-15T15:35:27.871363Z","submitted_at":"2018-02-27T12:02:33Z","title":"Adversarial Active Learning for Deep Networks: a Margin Based Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.09841","snapshot_observed_at":"2026-08-06T16:45:37.838266Z","title":"Adversarial active learning for deep networks: a margin based approach,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.838266Z"},"links":{"cited_paper":"/paper/1802.09841","citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:05f28124dcf6611d3c2ac2ab0291b609e31a1b8648d2960055fde34b563b75f4","observation_id":"10ed582b-234a-4aec-bd00-4be26fd77f18","resolution":{"observed_at":"2026-08-06T16:45:37.838266Z","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":"2026-08-06T16:45:37.955149Z","title":"Active learning by acquiring contrastive examples,","venue":null,"work_id":"d278761f-d0ad-48c6-aa8d-4825aa8a13e4","year":2021},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.840580Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:cbcb303dd3fc7bc38810496d3e680202384da4efaf1bd35177e1b1346a32cd79","observation_id":"c67a6168-21c6-4ae6-a75d-ba027ba7c609","resolution":{"observed_at":"2026-08-06T16:45:37.957552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.948747Z","title":"Coresets for data- efficient training of machine learning models,","venue":null,"work_id":"1ea66c92-17c4-4339-8d04-583d5dfa7a4f","year":2020},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.842745Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:6f9076340d0e756632d3fe745a32cd12963a500630de7610e3f85f79fcffbc64","observation_id":"50208e08-6115-45a7-be11-7c73b29df5c6","resolution":{"observed_at":"2026-08-06T16:45:37.951013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.942093Z","title":"Object detection with deep learning: A review,","venue":null,"work_id":"9cd290f1-6dda-42b3-aa4f-7adf275a3e3a","year":2019},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.844882Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:5871f0f649a03e574941b3341bc6ab1f40edafca71e18f887fc7e89af35a99ce","observation_id":"8250a34c-17f7-4d23-b965-15cadb065786","resolution":{"observed_at":"2026-08-06T16:45:37.944545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.847097Z","title":"Object detection in 20 years: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.847097Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:8724b8cdbb9c7897374a7ebd53bba7c4ba6d79ab544e4dc7ae47e2bf3cb3bc62","observation_id":"f7d792d0-e746-4ff0-9fd9-df1cf0672eda","resolution":{"observed_at":"2026-08-06T16:45:37.847097Z","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":"2026-08-06T16:45:37.930816Z","title":"Review the state-of- the-art technologies of semantic segmentation based on deep learning,","venue":null,"work_id":"2a0c4395-2fd4-4944-9870-fdf62cd95886","year":2022},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.849109Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:5920b479671737d137ada8489d20ba76c4de64611aea5e8ac27ed8470fd5696b","observation_id":"e396a012-5336-48bc-9767-67e823d18aec","resolution":{"observed_at":"2026-08-06T16:45:37.933742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.921395Z","title":"Semantic segmentation using vision transformers: A survey,","venue":null,"work_id":"d70a3ef3-9022-4a23-82d2-e04b07aec080","year":2023},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.851167Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:bdb2a29ad72c8423890b911570a429630e003e6b9b22743ebd88951465bac1a7","observation_id":"2e4d77eb-ccf4-4417-a1bd-14a13050951c","resolution":{"observed_at":"2026-08-06T16:45:37.926255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-06T16:45:37.853635Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.853635Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:464703e6a25faccd6acefd641a20b21271815806c5fac573d5bc23a248df7257","observation_id":"d478e023-f0b2-4d80-9317-96d120f20b3b","resolution":{"observed_at":"2026-08-06T16:45:37.853635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:45:37.855925Z","title":"Language mod- els are few-shot learners,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T16:45:37.855925Z"},"links":{"citing_paper":"/paper/2507.17768"},"observation_digest":"sha256:4d65ced6735b7a01a41b199dbeafda8f6db2c6bebbf424b4d451286a22ed6ebe","observation_id":"73123aaf-2300-4c4e-97f6-578280c9e152","resolution":{"observed_at":"2026-08-06T16:45:37.855925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.17768","last_updated":"2025-07-17T02:19:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T19:06:11.333333Z","submitted_at":"2025-07-17T02:19:33Z","title":"Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":46},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.17768."}