{"as_of":"2026-08-11T07:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1aeec4b1c81b7988c4b31af6a78971d25fea05a7d99f7fccc5a2a7a39a19836c","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:19:57.666122Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.13331/citation-record","integrity":"/paper/2501.13331/integrity","json":"/paper/2501.13331/citation-record.json","paper":"/paper/2501.13331"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:19:57.341524Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.341524Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:64e0da3b8c0e5094d295e1afd4fe691f1188c6e74a9cd00202d61844891fec3e","observation_id":"1a0da431-a571-4bcd-aa68-ef05db174c31","resolution":{"observed_at":"2026-08-10T16:19:57.341524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09259","last_updated":"2023-11-02T14:26:57Z","snapshot_observed_at":"2026-08-10T08:03:47.753476Z","submitted_at":"2023-10-13T17:15:05Z","title":"QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09259","snapshot_observed_at":"2026-08-10T16:19:57.351802Z","title":"Quik: Towards end-to-end 4-bit inference on generative large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.351802Z"},"links":{"cited_paper":"/paper/2310.09259","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:30caca3ea12f1e75028f2433a16ba13dd51d2ee6c0d419b700735140bef44437","observation_id":"a9b3aaf8-f63b-4e7e-af60-9c394c870d5f","resolution":{"observed_at":"2026-08-10T16:19:57.351802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00456","last_updated":"2024-10-29T11:09:12Z","snapshot_observed_at":"2026-08-09T17:23:32.399243Z","submitted_at":"2024-03-30T19:20:06Z","title":"QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00456","snapshot_observed_at":"2026-08-10T16:19:57.357499Z","title":"L., Li, B., Jaggi, M., Alistarh, D., Hoefler, T., and Hensman, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.357499Z"},"links":{"cited_paper":"/paper/2404.00456","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:d1bd16d16040a54bd389a069f798c53598a346c1d6dc367e1b5e7a18162b2fd3","observation_id":"bbe66914-2085-4210-84c3-93654493d010","resolution":{"observed_at":"2026-08-10T16:19:57.357499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05723","last_updated":"2019-05-29T08:45:02Z","snapshot_observed_at":"2026-08-10T13:05:54.969982Z","submitted_at":"2018-10-02T15:10:44Z","title":"Post-training 4-bit quantization of convolution networks for rapid-deployment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05723","snapshot_observed_at":"2026-08-10T16:19:57.363052Z","title":"Post-training 4-bit quantization of convolution networks for rapid-deployment, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.363052Z"},"links":{"cited_paper":"/paper/1810.05723","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:e8d885abec6355434858612f83ba1701eb556a9fd9f7ed7a9a20b6e6b742f365","observation_id":"73a1e5c7-80b4-41e2-a752-52af15fef528","resolution":{"observed_at":"2026-08-10T16:19:57.363052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01885","last_updated":"2023-12-01T07:04:05Z","snapshot_observed_at":"2026-07-06T16:14:17.621050Z","submitted_at":"2023-09-05T01:39:09Z","title":"QuantEase: Optimization-based Quantization for Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01885","snapshot_observed_at":"2026-08-10T16:19:57.368395Z","title":"Quantease: Optimization-based quantization for language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.368395Z"},"links":{"cited_paper":"/paper/2309.01885","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:a2c930a91727f2971b43e61727b8724786f04176e6a05f9c71bc9e7faf46575f","observation_id":"3be87f72-e4e0-4525-9676-670a62fce13b","resolution":{"observed_at":"2026-08-10T16:19:57.368395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13304","last_updated":"2024-01-15T21:54:28Z","snapshot_observed_at":"2026-08-07T08:10:21.413901Z","submitted_at":"2023-07-25T07:44:06Z","title":"QuIP: 2-Bit Quantization of Large Language Models With Guarantees","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.13304","snapshot_observed_at":"2026-08-10T16:19:57.373615Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.373615Z"},"links":{"cited_paper":"/paper/2307.13304","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:9a3571e5512e50cba3bd3d8d475a43c8250664ae9e0b1d174ea85470a36522f9","observation_id":"217bbb1e-7fb9-4b11-b07f-21b6a6780d93","resolution":{"observed_at":"2026-08-10T16:19:57.373615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-07-06T16:16:56.609388Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-08-10T16:19:57.378916Z","title":"Optimize weight rounding via signed gradient descent for the quantization of llms, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.378916Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:67b902a389f0a65bd89472d72dee60aa3c7bc53a778875eec971b923a8042570","observation_id":"1c748489-ba7c-4797-a677-e32498911cd4","resolution":{"observed_at":"2026-08-10T16:19:57.378916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04503","last_updated":"2021-02-08T19:56:04Z","snapshot_observed_at":"2026-08-07T06:30:07.035461Z","submitted_at":"2021-02-08T19:56:04Z","title":"VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04503","snapshot_observed_at":"2026-08-10T16:19:57.384515Z","title":"J., and Khailany, B","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.384515Z"},"links":{"cited_paper":"/paper/2102.04503","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:9eee763d96d9b2cd540f59f9855c994c1038d2459badf4b6b06d3ee1519d6f25","observation_id":"1c7c046d-0833-4a90-9719-dba81a639f63","resolution":{"observed_at":"2026-08-10T16:19:57.384515Z","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":"2026-08-10T16:19:57.389378Z","title":"B., Cavalcanti, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.389378Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:cc9ba13eefd92e9600061a3d215a0b459b8096082883228729bbd4744069ab39","observation_id":"85482f43-8a08-4b14-aa65-47de8cd4a9ec","resolution":{"observed_at":"2026-08-10T16:19:57.389378Z","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":"2026-08-10T16:19:59.625592Z","title":"Gpt3.int8(): 8-bit matrix multiplication for transformers at scale","venue":null,"work_id":"b7408a5a-7b80-4e2b-a65d-d270fab4c7ad","year":2022},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.395202Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:a0af756ccc0b6342c8dd9d1166419a51de41c6b289784f0261a5649746712538","observation_id":"6f56452b-9b8b-41c8-bfdc-83fd73947b48","resolution":{"observed_at":"2026-08-10T16:19:59.634976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03078","last_updated":"2023-06-05T17:53:28Z","snapshot_observed_at":"2026-08-08T23:08:43.190961Z","submitted_at":"2023-06-05T17:53:28Z","title":"SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03078","snapshot_observed_at":"2026-08-10T16:19:57.400172Z","title":"Spqr: A sparse-quantized representation for near-lossless llm weight compression, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.400172Z"},"links":{"cited_paper":"/paper/2306.03078","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:ed09968f91da79c2a171e8ea1033da9eb8aaa3c7e772b8ee7beb362944fb1168","observation_id":"fbe635a7-a84f-48e8-84be-6b34799c41b7","resolution":{"observed_at":"2026-08-10T16:19:57.400172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17323","last_updated":"2023-03-22T13:10:47Z","snapshot_observed_at":"2026-08-07T08:38:54.025062Z","submitted_at":"2022-10-31T13:42:40Z","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.17323","snapshot_observed_at":"2026-08-10T16:19:57.406827Z","title":"Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.406827Z"},"links":{"cited_paper":"/paper/2210.17323","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:cb37ee3b370f862b93654ba2a8694efff7b5cf3d89d1b6c6d084fa0f64e1ee59","observation_id":"37c1cd01-7281-4402-a958-9d9a882bdb21","resolution":{"observed_at":"2026-08-10T16:19:57.406827Z","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":"2026-08-10T16:19:57.412279Z","title":"A framework for few-shot language model evaluation, 12 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.412279Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:ddd1ca896723fb9c713fbac6c0e4de7cc6b64208346685c10f7750879f65d5c5","observation_id":"3941d96d-5140-4ff2-9006-1c7e17db5b34","resolution":{"observed_at":"2026-08-10T16:19:57.412279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-10T16:19:57.419383Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.419383Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:0d636237723e9f552b786a6089085dcfa9c806b05eecb9e9e1ac363cf28e6e14","observation_id":"addc7dc0-a3c9-4b37-959d-48b721fe14e5","resolution":{"observed_at":"2026-08-10T16:19:57.419383Z","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":"2026-08-10T16:19:57.425059Z","title":"Olive: Accelerating large language models via hardware-friendly outlier-victim pair quantization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.425059Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:f8671d42fbb0ac70e05dfc7ba369382cd626727326c7399eb745bd9664a90a03","observation_id":"fc9b218d-df91-4c00-a951-970479a27e28","resolution":{"observed_at":"2026-08-10T16:19:57.425059Z","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":"2026-08-10T16:19:57.433226Z","title":"O-2a: Low overhead dnn compression with outlier-aware approximation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.433226Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:b00d580f0e65c7627b7aec475ad85fc4c78c3ce70277c557f90c47052b56131d","observation_id":"c31d1aee-0fb5-42e4-8863-ca596ed73df0","resolution":{"observed_at":"2026-08-10T16:19:57.433226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.18079","last_updated":"2025-05-28T18:58:29Z","snapshot_observed_at":"2026-08-09T08:05:16.511956Z","submitted_at":"2024-01-31T18:58:14Z","title":"KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.18079","snapshot_observed_at":"2026-08-10T16:19:57.440237Z","title":"W., Shao, Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.440237Z"},"links":{"cited_paper":"/paper/2401.18079","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:ef642228e4ac2457629ee9f57e77ccd47a9e59ce2dd07cbb4f01bea60a338d74","observation_id":"bc330e45-6dd7-4f4d-b307-5b56cc54e84d","resolution":{"observed_at":"2026-08-10T16:19:57.440237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07629","last_updated":"2024-06-05T03:57:41Z","snapshot_observed_at":"2026-08-10T23:43:21.615256Z","submitted_at":"2023-06-13T08:57:54Z","title":"SqueezeLLM: Dense-and-Sparse Quantization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07629","snapshot_observed_at":"2026-08-10T16:19:57.446387Z","title":"W., and Keutzer, K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.446387Z"},"links":{"cited_paper":"/paper/2306.07629","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:4d617f9a3789f7e1f679f9fccc7eddb39d0a15c63676caa95fb20c7d881dbd4c","observation_id":"e4589b22-bab1-4780-bff3-fd0e67da1f41","resolution":{"observed_at":"2026-08-10T16:19:57.446387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07906","last_updated":"2022-10-14T15:43:57Z","snapshot_observed_at":"2026-08-06T10:37:06.807350Z","submitted_at":"2022-10-14T15:43:57Z","title":"Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks","version":1},"cited_work":{"arxiv_id":"2210.07906","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.07906","snapshot_observed_at":"2026-08-10T16:19:58.574518Z","title":"Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks","venue":"cs.LG","work_id":"a495a4ee-6a69-4f0c-9f46-941d0499ff44","year":2022},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.453281Z"},"links":{"cited_paper":"/paper/2210.07906","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:f58057532515e9b34e11cbe04acd446dd41acac96482b1aab05646cba1eef160","observation_id":"444e9ef1-dca1-434f-8968-9a4fc989c94c","resolution":{"observed_at":"2026-08-10T16:19:58.580568Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02272","last_updated":"2024-01-24T02:53:27Z","snapshot_observed_at":"2026-08-10T01:01:42.551800Z","submitted_at":"2023-06-04T06:33:13Z","title":"OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02272","snapshot_observed_at":"2026-08-10T16:19:57.466052Z","title":"Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.466052Z"},"links":{"cited_paper":"/paper/2306.02272","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:bf58d98007ad066f1bdb821610f69f1480db7218e6a2b35f35287c44b391bb08","observation_id":"d97e2d60-ed5e-44c0-bff7-bd37a3ea2441","resolution":{"observed_at":"2026-08-10T16:19:57.466052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02784","last_updated":"2023-12-13T13:29:29Z","snapshot_observed_at":"2026-08-07T14:05:57.857217Z","submitted_at":"2023-09-06T06:51:15Z","title":"Norm Tweaking: High-performance Low-bit Quantization of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.02784","snapshot_observed_at":"2026-08-10T16:19:57.478372Z","title":"Norm tweaking: High-performance low-bit quantization of large language models, 2023 a","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.478372Z"},"links":{"cited_paper":"/paper/2309.02784","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:0b3592783135536dbbfa019451bb61220a332669e8e8a7e0137298ff7d37f172","observation_id":"91dfbf79-da4b-491f-a9e8-db66a71a3b3d","resolution":{"observed_at":"2026-08-10T16:19:57.478372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.15987","last_updated":"2023-08-30T12:18:18Z","snapshot_observed_at":"2026-08-11T00:36:17.296585Z","submitted_at":"2023-08-30T12:18:18Z","title":"FPTQ: Fine-grained Post-Training Quantization for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.15987","snapshot_observed_at":"2026-08-10T16:19:57.485389Z","title":"Fptq: Fine-grained post-training quantization for large language models, 2023 b","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.485389Z"},"links":{"cited_paper":"/paper/2308.15987","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:64771cd13e04dc449266ea0c382e0e9df47bb8c6f607d96ab2808ac5a775d356","observation_id":"1bddd51e-f1fd-41ce-8db9-77f7de62512a","resolution":{"observed_at":"2026-08-10T16:19:57.485389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00978","last_updated":"2026-04-25T06:58:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-01T17:59:10Z","title":"AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00978","snapshot_observed_at":"2026-08-10T16:19:57.495259Z","title":"Awq: Activation-aware weight quantization for llm compression and acceleration, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.495259Z"},"links":{"cited_paper":"/paper/2306.00978","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:9fd7f8a19136a9d65fb7ef6a026f40d20ef26df81bf9263a49364b08515019d4","observation_id":"1cd62d78-affc-48ff-9238-15c4f0aa04e0","resolution":{"observed_at":"2026-08-10T16:19:57.495259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04532","last_updated":"2025-05-01T02:14:05Z","snapshot_observed_at":"2026-08-11T03:12:32.404394Z","submitted_at":"2024-05-07T17:59:30Z","title":"QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04532","snapshot_observed_at":"2026-08-10T16:19:57.517227Z","title":"Qserve: W4a8kv4 quantization and system co-design for efficient llm serving, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.517227Z"},"links":{"cited_paper":"/paper/2405.04532","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:f9c0a6996c3b746af28684bba00425b1f7279636d2c7acb84e38ca063b750245","observation_id":"3dbfebed-0c40-4281-a48d-0ce9816587a7","resolution":{"observed_at":"2026-08-10T16:19:57.517227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08041","last_updated":"2024-04-06T10:22:57Z","snapshot_observed_at":"2026-08-10T20:21:54.887618Z","submitted_at":"2023-10-12T05:25:49Z","title":"QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08041","snapshot_observed_at":"2026-08-10T16:19:57.526967Z","title":"Qllm: Accurate and efficient low-bitwidth quantization for large language models, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.526967Z"},"links":{"cited_paper":"/paper/2310.08041","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:1c6101a224270e43aea158cd6a52af1e30e7cb4ab0971fcfcd711d1bcfb00ffe","observation_id":"d6de235c-32ef-411a-90e9-4841949c427e","resolution":{"observed_at":"2026-08-10T16:19:57.526967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17888","last_updated":"2023-05-29T05:22:11Z","snapshot_observed_at":"2026-08-07T18:58:35.737110Z","submitted_at":"2023-05-29T05:22:11Z","title":"LLM-QAT: Data-Free Quantization Aware Training for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17888","snapshot_observed_at":"2026-08-10T16:19:57.535780Z","title":"Llm-qat: Data-free quantization aware training for large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.535780Z"},"links":{"cited_paper":"/paper/2305.17888","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:cfa4b3528d071d579092cc643a4297c93033542500e954c5c054a5948352493f","observation_id":"91f0dd9f-91b1-4b16-9f83-74bc34240102","resolution":{"observed_at":"2026-08-10T16:19:57.535780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16406","last_updated":"2025-02-20T06:07:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-26T02:15:49Z","title":"SpinQuant: LLM quantization with learned rotations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16406","snapshot_observed_at":"2026-08-10T16:19:57.542793Z","title":"Spinquant: Llm quantization with learned rotations, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.542793Z"},"links":{"cited_paper":"/paper/2405.16406","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:4c617c28cd0bc811f5aade6a3839e4b362f90e604b51227130e28c6f478c1dcd","observation_id":"e0b1d81b-c1f2-4989-8b31-7d8de2dc073f","resolution":{"observed_at":"2026-08-10T16:19:57.542793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06031","last_updated":"2016-06-20T09:37:17Z","snapshot_observed_at":"2026-08-10T23:14:34.195361Z","submitted_at":"2016-06-20T09:37:17Z","title":"The LAMBADA dataset: Word prediction requiring a broad discourse context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.06031","snapshot_observed_at":"2026-08-10T16:19:57.553255Z","title":"N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fernández, R","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.553255Z"},"links":{"cited_paper":"/paper/1606.06031","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:9b6bc520b0d4936f8570d58b892d8d90075b85142918532de684ccd78c0ae330","observation_id":"75c6eddf-c62e-4786-96bb-5f8f6271328d","resolution":{"observed_at":"2026-08-10T16:19:57.553255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.06462","last_updated":"2015-03-19T06:54:53Z","snapshot_observed_at":"2026-07-06T04:12:44.976576Z","submitted_at":"2015-03-19T06:54:53Z","title":"Normalization: A Preprocessing Stage","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.06462","snapshot_observed_at":"2026-08-10T16:19:57.561765Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.561765Z"},"links":{"cited_paper":"/paper/1503.06462","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:aea13ccecf4fc51fc5117ee73b1fd1c30bcdc09055f99de4e8e7f95865e6e6b3","observation_id":"ca2b1707-834d-4858-a0b4-6e2ba8cc4725","resolution":{"observed_at":"2026-08-10T16:19:57.561765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13137","last_updated":"2024-03-18T05:33:22Z","snapshot_observed_at":"2026-07-06T16:10:15.694898Z","submitted_at":"2023-08-25T02:28:35Z","title":"OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13137","snapshot_observed_at":"2026-08-10T16:19:57.569008Z","title":"Omniquant: Omnidirectionally calibrated quantization for large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.569008Z"},"links":{"cited_paper":"/paper/2308.13137","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:1ca09c0852e678da1e58495d9084aec93c0cae0adae203ea792c597dc577b68f","observation_id":"7ecf95b8-9fdb-40ac-85b9-26af37e26a42","resolution":{"observed_at":"2026-08-10T16:19:57.569008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.06865","last_updated":"2023-06-12T07:48:53Z","snapshot_observed_at":"2026-08-06T08:43:40.051791Z","submitted_at":"2023-03-13T05:19:28Z","title":"FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.06865","snapshot_observed_at":"2026-08-10T16:19:57.579518Z","title":"Y., Xie, Z., Chen, B., Barrett, C., Gonzalez, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.579518Z"},"links":{"cited_paper":"/paper/2303.06865","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:1805833e3ac96308b8bc946b68dfaf25fe87b63c436d8410cd612aec8964d25d","observation_id":"3ec15a6a-896d-463f-84ea-f11f8285a99e","resolution":{"observed_at":"2026-08-10T16:19:57.579518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13202","last_updated":"2024-02-20T18:10:58Z","snapshot_observed_at":"2026-08-07T12:02:08.840309Z","submitted_at":"2024-02-20T18:10:58Z","title":"A Note on Approximate Hadamard Matrices","version":1},"cited_work":{"arxiv_id":"2402.13202","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.13202","snapshot_observed_at":"2026-08-10T16:19:58.037443Z","title":"A Note on Approximate Hadamard Matrices","venue":"math.CO","work_id":"29a882b6-9060-4711-b96a-2d2ff84b8813","year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.587102Z"},"links":{"cited_paper":"/paper/2402.13202","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:a673fad6e685a849a544285f1d153bc8a51545ce51090e0725f88c1a9243c12c","observation_id":"15665b4e-b0a9-4c05-a20d-bd42a9650351","resolution":{"observed_at":"2026-08-10T16:19:58.049991Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-10T16:19:57.596732Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.596732Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:98831724bfcbe032574f3c2d86cd968e1418e52bf5e3cf3d88d58baa7bfae0f4","observation_id":"fc062707-1c09-4d20-adfc-d3b084f31f66","resolution":{"observed_at":"2026-08-10T16:19:57.596732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18832","last_updated":"2024-06-27T02:02:26Z","snapshot_observed_at":"2026-07-06T18:37:43.309281Z","submitted_at":"2024-06-27T02:02:26Z","title":"OutlierTune: Efficient Channel-Wise Quantization for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18832","snapshot_observed_at":"2026-08-10T16:19:57.604260Z","title":"Outliertune: Efficient channel-wise quantization for large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.604260Z"},"links":{"cited_paper":"/paper/2406.18832","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:1afb2fa3c2a96e0fd125bef95c9d56f39a2b318880cc1107b32adcfed3520516","observation_id":"f399bc1f-34b9-4f94-ac58-5018e2b690f2","resolution":{"observed_at":"2026-08-10T16:19:57.604260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09145","last_updated":"2023-10-23T08:48:31Z","snapshot_observed_at":"2026-07-06T15:17:05.623407Z","submitted_at":"2023-04-18T17:34:23Z","title":"Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.09145","snapshot_observed_at":"2026-08-10T16:19:57.610432Z","title":"Outlier suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.610432Z"},"links":{"cited_paper":"/paper/2304.09145","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:a233d0c8c1de382db20fccc10aab4f3053542a737d4f9b65eae33676c3bad830","observation_id":"88b2d013-29a9-4bea-b67a-e56b73f4af4a","resolution":{"observed_at":"2026-08-10T16:19:57.610432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10438","last_updated":"2024-03-29T19:21:58Z","snapshot_observed_at":"2026-08-07T09:04:32.994880Z","submitted_at":"2022-11-18T18:59:33Z","title":"SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10438","snapshot_observed_at":"2026-08-10T16:19:57.616343Z","title":"Smoothquant: Accurate and efficient post-training quantization for large language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.616343Z"},"links":{"cited_paper":"/paper/2211.10438","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:3c8428f0faa52a32865e100eae71de3e65ae3f22f72abe6c712e0ebc1a02b9ff","observation_id":"432d6a19-8924-4b5c-9d51-7d36ab5c2700","resolution":{"observed_at":"2026-08-10T16:19:57.616343Z","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":"2026-08-10T16:19:59.595516Z","title":"Zeroquant: Efficient and affordable post-training quantization for large-scale transformers","venue":null,"work_id":"ae4a5c3f-d7f9-4f9e-87cc-3b41f77f5ac5","year":2022},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.625433Z"},"links":{"citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:5fcf64d913551fba9dba781237b5eccfe394d3686dcd8200e192c252217e5491","observation_id":"32fa36e5-38e3-421d-982d-b10ac952ade6","resolution":{"observed_at":"2026-08-10T16:19:59.602069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.01089","last_updated":"2023-05-17T10:07:33Z","snapshot_observed_at":"2026-07-06T15:11:32.429514Z","submitted_at":"2023-04-03T15:46:15Z","title":"RPTQ: Reorder-based Post-training Quantization for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.01089","snapshot_observed_at":"2026-08-10T16:19:57.635110Z","title":"Rptq: Reorder-based post-training quantization for large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.635110Z"},"links":{"cited_paper":"/paper/2304.01089","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:52be5db011dd8292379a9eae569ba9cdff3884d76e5307cbaffd3b00ec66bdab","observation_id":"6a3d74eb-d8f7-49c0-910b-a9e9f61f0d36","resolution":{"observed_at":"2026-08-10T16:19:57.635110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12356","last_updated":"2023-05-21T05:28:37Z","snapshot_observed_at":"2026-07-06T15:30:09.490978Z","submitted_at":"2023-05-21T05:28:37Z","title":"Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12356","snapshot_observed_at":"2026-08-10T16:19:57.646521Z","title":"Integer or floating point? new outlooks for low-bit quantization on large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.646521Z"},"links":{"cited_paper":"/paper/2305.12356","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:2e2b0e83e4b24238dae7fb8c63c962e58c79b8ca9c057b77249413972aa73b34","observation_id":"05e522e5-e560-4887-8ca6-bf09e899d755","resolution":{"observed_at":"2026-08-10T16:19:57.646521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19102","last_updated":"2024-04-16T06:08:05Z","snapshot_observed_at":"2026-08-10T12:47:07.125672Z","submitted_at":"2023-10-29T18:33:05Z","title":"Atom: Low-bit Quantization for Efficient and Accurate LLM Serving","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19102","snapshot_observed_at":"2026-08-10T16:19:57.666122Z","title":"Atom: Low-bit quantization for efficient and accurate llm serving, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.666122Z"},"links":{"cited_paper":"/paper/2310.19102","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:4c2a25ed289d56b8065c09dd6a529d1eff4d0b65b4b7578c30894b9ec56300dd","observation_id":"701fc2dd-82e0-4125-8523-63f72ed3fca3","resolution":{"observed_at":"2026-08-10T16:19:57.666122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":2,"verified_fuzzy":2},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.13331."}