{"as_of":"2026-08-10T21:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a6df822916e9968c95d3decdac0125467001e7277d67fbe874066c50cd28ded5","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T19:12:58.837909Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T22:16:16.046751Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2208.07339","last_updated":"2022-11-10T18:14:31Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-08-15T17:08:50Z","title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","version":2},"reference_index":171,"source":"arxiv_source","source_observed_at":"2026-05-13T13:35:35.972596Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2208.07339"},"observation_digest":"sha256:eea2254a6ecd0309af00190db23398ab65be0496c3d625cd4b6cd9175b96e508","observation_id":"5d495910-c8b9-4bfa-b361-25c92364875a","resolution":{"observed_at":"2026-05-13T13:35:36.185243Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_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},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T17:18:35.153078Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2210.17323"},"observation_digest":"sha256:31285cbcbbb842522bb6fea10a7b93a855cbdeac231a8e7e73e7fb159bb22c3c","observation_id":"03b4f879-9892-4bcb-abf0-9017dbf482d2","resolution":{"observed_at":"2026-05-10T17:18:35.274980Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2302.01318","last_updated":"2023-02-02T18:44:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-02T18:44:11Z","title":"Accelerating Large Language Model Decoding with Speculative Sampling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-11T07:29:36.205026Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2302.01318"},"observation_digest":"sha256:f9716ef309d265d53da3092a93352fb427848d81cc0080cb8fbf12d4388da1c0","observation_id":"3b7bbd66-67a3-49f0-b4ba-f25606e2f5ac","resolution":{"observed_at":"2026-05-11T07:29:36.307343Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2305.14314","last_updated":"2023-05-23T17:50:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-23T17:50:33Z","title":"QLoRA: Efficient Finetuning of Quantized LLMs","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-11T13:29:53.345251Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2305.14314"},"observation_digest":"sha256:9bd272ced386fe3edda00f72ca833d973417fac53093cd69c6916306a2f703cb","observation_id":"f6589bb8-472a-4d55-b1fb-2a71b309c258","resolution":{"observed_at":"2026-05-11T13:29:53.790343Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_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},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-24T08:27:35.798991Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2306.00978"},"observation_digest":"sha256:a14cdbd2b8afce6faf2b34657565b9c3d9c95a301aedc4395a1f2e111c5ff177","observation_id":"419f3a01-d77c-4b52-9378-02df1fff1d21","resolution":{"observed_at":"2026-05-24T08:29:11.224966Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2306.14048","last_updated":"2023-12-18T19:10:00Z","snapshot_observed_at":"2026-08-06T19:19:02.165567Z","submitted_at":"2023-06-24T20:11:14Z","title":"H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-17T18:00:50.053377Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2306.14048"},"observation_digest":"sha256:49ffbef25093c297c5aacd72ae87cab3ef780f1b5cbb089508ea3faaa7b5d91c","observation_id":"29d83d7a-d3a3-4d48-b1df-85a29bb200fd","resolution":{"observed_at":"2026-05-17T18:00:50.460651Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-08-10T19:12:58.837909Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.10534","last_updated":"2025-01-17T20:15:11Z","snapshot_observed_at":"2026-08-10T19:06:44.681838Z","submitted_at":"2025-01-17T20:15:11Z","title":"4bit-Quantization in Vector-Embedding for RAG","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T19:12:58.837909Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2501.10534"},"observation_digest":"sha256:d99f7d3c8bd8ffd11a0c128edf26c84b91c066441221f1b10d9adf0c682cc400","observation_id":"4cc0df57-4fc6-4af5-9030-8a7ae42ce0c6","resolution":{"observed_at":"2026-08-10T19:12:58.837909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-08-10T16:13:19.988135Z","title":"Zeroquant: Efficient and affordable post-training quantization for large-scale transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13987","last_updated":"2025-01-23T08:24:25Z","snapshot_observed_at":"2026-08-10T15:53:21.557278Z","submitted_at":"2025-01-23T08:24:25Z","title":"OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T16:13:19.988135Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2501.13987"},"observation_digest":"sha256:22264d751c68398d85a7a696f5c4766f41317bfb15b1cf19b332de454d6fbb16","observation_id":"d1c187c5-2f9a-4c8e-b164-77642734c8de","resolution":{"observed_at":"2026-08-10T16:13:19.988135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-08-07T10:42:41.045802Z","title":"Zeroquant: Efficient and affordable post-training quantization for large-scale transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05432","last_updated":"2025-06-26T06:17:49Z","snapshot_observed_at":"2026-08-09T13:48:08.253333Z","submitted_at":"2025-06-05T08:58:58Z","title":"PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:42:41.045802Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2506.05432"},"observation_digest":"sha256:4f3a5c758cf0d07c492d86dde6adfe997c7dff402bcf683383396535b362fcc1","observation_id":"a138f024-ee03-4bc0-97c7-7b1d9e2e4d83","resolution":{"observed_at":"2026-08-07T10:42:41.045802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-08-06T13:22:47.848505Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.848505Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:a69f5318c0ed12db1a02c5ea80374cbf94d814a0c8d8f0266e912791f94474a7","observation_id":"934d023b-00d6-45e5-a64e-f1d9d38e76e3","resolution":{"observed_at":"2026-08-06T13:22:47.848505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2601.02455","last_updated":"2026-04-27T15:59:08Z","snapshot_observed_at":"2026-08-08T17:14:13.979315Z","submitted_at":"2026-01-05T18:47:16Z","title":"Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T17:30:43.808662Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2601.02455"},"observation_digest":"sha256:cc8e07609e5b9b1a12e41da860749f7897d26b980c00cac7388781333d3ad183","observation_id":"a3f54d90-0c07-462f-b783-f990656e6967","resolution":{"observed_at":"2026-05-16T17:31:08.335975Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2604.13440","last_updated":"2026-04-15T03:40:30Z","snapshot_observed_at":"2026-08-02T07:02:47.648329Z","submitted_at":"2026-04-15T03:40:30Z","title":"A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T14:24:53.745784Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2604.13440"},"observation_digest":"sha256:5a6c335f9dead55342222c15809e9c21706878606d9f2498087fc072d1b69d99","observation_id":"38c47fdd-5b79-4043-b192-f7df2b5a3817","resolution":{"observed_at":"2026-05-10T14:25:29.242563Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2605.24754","last_updated":"2026-05-23T22:09:34Z","snapshot_observed_at":"2026-08-10T16:49:44.604804Z","submitted_at":"2026-05-23T22:09:34Z","title":"Motion-Compensated Weight Compression","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-30T12:58:27.637822Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2605.24754"},"observation_digest":"sha256:0e70886d30032fb23ac722c7fb45c6820de8a191eb4cc15284cae8d31d9750eb","observation_id":"b3d0d9e1-e4d3-4c83-882e-1e7635f2e040","resolution":{"observed_at":"2026-06-30T13:04:40.440211Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":"2206.01861","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-07-01T22:16:16.046751Z","title":"Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L","venue":null,"work_id":"31ae02a0-1bff-4485-8eb1-92cf90e65c74","year":2022},"citing_paper":{"arxiv_id":"2606.09864","last_updated":"2026-06-01T02:02:20Z","snapshot_observed_at":"2026-08-03T01:43:21.917246Z","submitted_at":"2026-06-01T02:02:20Z","title":"Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-28T15:37:34.129339Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2606.09864"},"observation_digest":"sha256:29ce79b2af68c7e778f8350eda94a564b3d517258a01d74e91699ff0b51419e7","observation_id":"2687967a-64b3-4698-8ad1-f18a8546b249","resolution":{"observed_at":"2026-07-01T22:16:16.048491Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.01861/citation-record","integrity":"/paper/2206.01861/integrity","json":"/paper/2206.01861/citation-record.json","paper":"/paper/2206.01861"},"outbound":[],"paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T00:45:35.320071Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2206.01861."}