{"as_of":"2026-08-08T01:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:84949aeadcc733e25e5a93cced4a762e9c41d96ae9cf6150e7d2ed2030479afb","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:56:59.870638Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.22349/citation-record","integrity":"/paper/2507.22349/integrity","json":"/paper/2507.22349/citation-record.json","paper":"/paper/2507.22349"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:57:00.197941Z","title":"Post train- ing 4-bit quantization of convolutional networks for rapid- deployment","venue":null,"work_id":"f76695b8-97e9-4ca4-bda5-588ddda4b94c","year":2019},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.761043Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:a03c5f95bd829791baabf7c6fd2feca98beceb901bcf8db8c9349b551a9633e6","observation_id":"2790afa1-04eb-4bb5-9f1f-2ebf7c315c2e","resolution":{"observed_at":"2026-08-06T11:57:00.201098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.189254Z","title":"Dory: Automatic end-to-end deployment of real-world dnns on low-cost iot mcus","venue":null,"work_id":"d3be271e-7454-4696-bc10-1c3c4ccaa9b5","year":2021},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.764720Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:42da2020b8e8cd9c36e357926c01a650839096be50076769729a6f7b8a2d2c1b","observation_id":"47c6628a-a786-4314-b52a-779c2e6a15db","resolution":{"observed_at":"2026-08-06T11:57:00.192400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.06085","last_updated":"2018-07-17T07:33:19Z","snapshot_observed_at":"2026-07-06T06:39:21.688392Z","submitted_at":"2018-05-16T01:19:43Z","title":"PACT: Parameterized Clipping Activation for Quantized Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.06085","snapshot_observed_at":"2026-08-06T11:56:59.767955Z","title":"Pact: Parameterized clipping activa- tion for quantized neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.767955Z"},"links":{"cited_paper":"/paper/1805.06085","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:2656d70284870f8ebf6b24e5112c629891c8a5bfc48f7797f9562e472b71881b","observation_id":"ce360dc3-cc38-406c-be8f-d7b66c5567a3","resolution":{"observed_at":"2026-08-06T11:56:59.767955Z","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":"2026-08-06T11:56:59.771547Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.771547Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:e95b2fb0380692268037d8ead1de92e2c0739fce8a7a30d87aa06176180554d7","observation_id":"5c3f5361-fa23-4a51-9d21-8bd570c9e763","resolution":{"observed_at":"2026-08-06T11:56:59.771547Z","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":"2026-08-06T11:57:00.175263Z","title":"Hawq: Hessian aware quantization of neural networks with mixed-precision","venue":null,"work_id":"65cd21b8-ad5f-4326-8546-437f8d470950","year":2019},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.774577Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:cce9f881aeb99e66299134c523199039136d5bf0fe5e0c0a7ddc36a670064df8","observation_id":"76c8ebd0-f440-4a8e-b7ff-7c33b6d33f35","resolution":{"observed_at":"2026-08-06T11:57:00.178338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.167260Z","title":"Hawq-v2: Hessian aware trace-weighted quantization of neural networks","venue":null,"work_id":"24c54506-1799-42b3-b39d-e6d56ae31c61","year":2020},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.777588Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:1b6c06b3345ae7598d6d473202cbf3fc4cef022c2816fb3c248450114e6e6914","observation_id":"9f9bf3e4-9e2f-4072-a778-d767162d9ece","resolution":{"observed_at":"2026-08-06T11:57:00.170191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08153","last_updated":"2020-05-07T03:30:49Z","snapshot_observed_at":"2026-08-07T23:40:04.424513Z","submitted_at":"2019-02-21T17:31:32Z","title":"Learned Step Size Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08153","snapshot_observed_at":"2026-08-06T11:56:59.780872Z","title":"Esser, John L","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.780872Z"},"links":{"cited_paper":"/paper/1902.08153","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:cb4e610bdb4743451782d45be4fbb8fb281dbe65ed553fb9fb57b7ef49a5ddaf","observation_id":"4a647b1d-9898-4422-b763-fdba6849a14c","resolution":{"observed_at":"2026-08-06T11:56:59.780872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07471","last_updated":"2022-02-14T01:57:33Z","snapshot_observed_at":"2026-07-06T12:38:02.234243Z","submitted_at":"2022-02-14T01:57:33Z","title":"SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07471","snapshot_observed_at":"2026-08-06T11:56:59.784156Z","title":"Squant: On-the-ﬂy data-free quantization via diagonal hes- sian approximation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.784156Z"},"links":{"cited_paper":"/paper/2202.07471","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:66e9e4765f22e5dbe6c34dcabf6b001aa261a26457db3f02e25dc643382af921","observation_id":"47810d93-d8ca-47eb-9113-4834c570388d","resolution":{"observed_at":"2026-08-06T11:56:59.784156Z","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":"2026-08-06T11:57:00.159003Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"065ffecd-a1a7-43aa-816f-8bb7baf1766c","year":2016},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.787354Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:b3313fca42e9fafece3bde437c793b8900d05a42a99a0d79199b1d8cbcecdb47","observation_id":"4e3ecd63-5772-4b95-9893-ae1b4f507cb3","resolution":{"observed_at":"2026-08-06T11:57:00.161977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.151141Z","title":"1.1 computing’s energy problem (and what we can do about it)","venue":null,"work_id":"45f25d80-55c5-4f92-bc60-dfe4c9f5af25","year":2014},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.790062Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:3e41e2aa1ec219e8e9667b6412d90da7f96253c7d654c3c4c451dddeff1de89a","observation_id":"808161fd-9c0a-42ba-a729-8985206dcd97","resolution":{"observed_at":"2026-08-06T11:57:00.154019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02244","last_updated":"2019-11-20T17:26:40Z","snapshot_observed_at":"2026-07-06T07:50:45.045024Z","submitted_at":"2019-05-06T19:38:31Z","title":"Searching for MobileNetV3","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02244","snapshot_observed_at":"2026-08-06T11:56:59.792872Z","title":"Le, and Hartwig Adam","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.792872Z"},"links":{"cited_paper":"/paper/1905.02244","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:6c0f5581fb6bd448683e775341da8804c63764ff183a3ae98d02d6cd866191b8","observation_id":"6e01ed75-1705-477f-8b6b-a0a776a1311f","resolution":{"observed_at":"2026-08-06T11:56:59.792872Z","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":"2026-08-06T11:57:00.142992Z","title":"Squeeze-and-excitation net- works","venue":null,"work_id":"036d52f8-b055-4f91-9e08-ac885ced7bb6","year":2018},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.796280Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:b8320a2015618223a19499a7c46e4ddcdf8751457414084769ed4455bee4146a","observation_id":"fd9daa6e-2588-42c5-81bb-d13199387411","resolution":{"observed_at":"2026-08-06T11:57:00.146062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.134664Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"30d3a189-4fae-405e-849a-3a8105fd8b42","year":2009},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.798968Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:5440def5684521e60205bddde59c191bf6617fa2f39e14e684e989c7f1396db2","observation_id":"b3218a98-e628-41b8-ba1c-abb2d8740412","resolution":{"observed_at":"2026-08-06T11:57:00.137749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.126046Z","title":"Q-vit: Accurate and fully quantized low-bit vision transformer","venue":null,"work_id":"71a766fc-0a96-4225-ad9e-7bb9b3a29c2a","year":2022},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.801761Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:25daeed024c2a9b1355e2f4d331e6895823dc4a3ddd78679b9e0fd53050781c0","observation_id":"29549d4d-8e09-4167-8a9d-d2789916b927","resolution":{"observed_at":"2026-08-06T11:57:00.129144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.117834Z","title":"Oscillation-free quantization for low-bit vision transformers","venue":null,"work_id":"3b9a3377-0501-4561-a3bb-d60875220c2c","year":2023},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.804436Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:10d19fd314a1c7c2e35701a09eda30474117115c5a42b028e2e5f28877f8cd55","observation_id":"bbcc7037-aa21-4ae5-a958-affc13d0059a","resolution":{"observed_at":"2026-08-06T11:57:00.120945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.109362Z","title":"Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers","venue":null,"work_id":"a2bd5261-4171-4037-a7b7-de0509861e9b","year":2023},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.807281Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:d99544540072abf790e44209f105e7709ff4f6124c4edada886c284334c651ab","observation_id":"ba5a3269-f7db-4ff3-bfb0-56a0a33679b0","resolution":{"observed_at":"2026-08-06T11:57:00.112388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.101026Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"cc164ef9-717e-4460-8523-3ba7913b6df8","year":2021},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.810033Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:a849cebd3a1b26a387b4725516ce6205ba90dffad0b36e2aed59584f4c4ad1e9","observation_id":"8384dec6-3c63-46be-b8fc-a00b5bff3a9e","resolution":{"observed_at":"2026-08-06T11:57:00.104062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.092679Z","title":"Up or down? adap- tive rounding for post-training quantization","venue":null,"work_id":"fa13d713-678f-4681-82af-bb332795a3d6","year":2020},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.812665Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:6542b2630e359f75b78a8028a6da79ab4857af4d0c470acfd2c5748e850a7960","observation_id":"94bae949-d196-43cb-8cb2-a264d3c52ca9","resolution":{"observed_at":"2026-08-06T11:57:00.095757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06357","last_updated":"2025-01-10T21:36:20Z","snapshot_observed_at":"2026-08-03T03:47:38.102545Z","submitted_at":"2025-01-10T21:36:20Z","title":"Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06357","snapshot_observed_at":"2026-08-06T11:56:59.815422Z","title":"Mix-qvit: Mixed- precision vision transformer quantization driven by layer importance and quantization sensitivity","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.815422Z"},"links":{"cited_paper":"/paper/2501.06357","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:b4eb88607cb3b1ce27051a825e559e167a3622269fd6236dc663c9e0af1b962e","observation_id":"081af1ac-1832-47bb-9540-3aabfd51c68d","resolution":{"observed_at":"2026-08-06T11:56:59.815422Z","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":"2026-08-06T11:57:00.084664Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"fff0f55a-0307-4799-b935-2fef968086fb","year":2018},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.818397Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:14f7059a27aad443cf9940948113a2dff6cf2c8d9133822b9be959700d0204a3","observation_id":"650c7e64-a4b1-4618-ae85-ac729df13b16","resolution":{"observed_at":"2026-08-06T11:57:00.087726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.076291Z","title":"Winning the lottery with continuous sparsiﬁcation","venue":null,"work_id":"af7d6480-269c-4e3b-8797-46398c14ce0d","year":2020},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.821087Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:ab2686f3e614a5bfa5604dfe324a8f802008987c0f31e32ccb18843956ea210b","observation_id":"7b7da998-d387-48a2-b7ab-19195b0ccecb","resolution":{"observed_at":"2026-08-06T11:57:00.079483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.067894Z","title":"Training data-efﬁcient image transformers & distillation through at- tention","venue":null,"work_id":"084efdcd-c6f1-4f0a-8cd1-f8fb5cbfeeb4","year":2021},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.823905Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:4b86c0357dad4059532495c9d420e053903ef98ed6eb94465a1b21269a9db4a0","observation_id":"5b9bb181-16b9-4cec-9616-e0edcbfb5bf2","resolution":{"observed_at":"2026-08-06T11:57:00.070997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.059595Z","title":"Haq: Hardware-aware automated quantization with mixed precision","venue":null,"work_id":"ff0ddff3-bbf4-4c84-9e49-4b77480d2d75","year":2019},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.827793Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:8accc8d557c5b9eeaeab9a2ed1cfa0a2e73ad146a1392a1ec1c41e4138d52617","observation_id":"86430e71-926d-4670-b7e1-bbe6755ebf46","resolution":{"observed_at":"2026-08-06T11:57:00.062740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.050912Z","title":"Fbnet: Hardware-aware efﬁcient con- vnet design via differentiable neural architecture search","venue":null,"work_id":"5f793998-51e3-4ea1-a97e-043d4191729d","year":2019},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.831380Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:2d84b4634bc6d5190786e41208c7f01c3d66d5c4bd8648104f14c2d3e1f33ff3","observation_id":"2d49307d-170e-496d-a9f6-71c882adda41","resolution":{"observed_at":"2026-08-06T11:57:00.054459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.042498Z","title":"Smoothquant: Accurate and efﬁ- cient post-training quantization for large language models","venue":null,"work_id":"69fea213-2780-4305-8202-168096a0f658","year":2023},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.834664Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:3b0839fb7c05c3b7ebf6df20b42b73858803398cb167dbe0e3f636a4f418c398","observation_id":"bf27856f-74e4-4df1-9fe6-0311da771d86","resolution":{"observed_at":"2026-08-06T11:57:00.045774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.033483Z","title":"Csq: Growing mixed-precision quantization scheme with bi-level continuous sparsiﬁcation","venue":null,"work_id":"ac99e591-9e89-43df-b8c1-159b348b5393","year":2023},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.837609Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:88a7a3f066ea9459230a52af1e1103a6f62197568a4ea9322bab950eb48acd35","observation_id":"10ee9007-f191-4566-8039-e6a7e7e8bd6e","resolution":{"observed_at":"2026-08-06T11:57:00.036961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.10462","last_updated":"2021-02-20T22:37:41Z","snapshot_observed_at":"2026-07-06T10:43:11.902082Z","submitted_at":"2021-02-20T22:37:41Z","title":"BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.10462","snapshot_observed_at":"2026-08-06T11:56:59.840476Z","title":"Bsq: Ex- ploring bit-level sparsity for mixed-precision neural network quantization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.840476Z"},"links":{"cited_paper":"/paper/2102.10462","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:a658a3bd35f7227d6ce8b9b8128cf7a30f7d70b47880e15c518720559596634f","observation_id":"744569d2-cc5e-4c48-813c-de21f8ce02aa","resolution":{"observed_at":"2026-08-06T11:56:59.840476Z","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":"2026-08-06T11:57:00.024370Z","title":"Quan- tization networks","venue":null,"work_id":"3b79a408-2caa-4871-9756-032fc4919977","year":2019},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.843927Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:8bbd34fbd0112c82dc3c1007e9ac2e55bdd46b5e90347f01a91caa545cd14679","observation_id":"c0ec9f73-919c-4e8d-ab08-afb9eb29b2c6","resolution":{"observed_at":"2026-08-06T11:57:00.027592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.015468Z","title":"Hawq-v3: Dyadic neural net- work quantization","venue":null,"work_id":"052b98e2-6b65-4a3f-91ca-0108c68533f9","year":2021},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.846577Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:60be7a8770bab33e8bc332621a8768bf271138de5c79a6ce23af93efd40099a3","observation_id":"aa2ec4c9-1acd-4593-8fef-e5d9b1071b3a","resolution":{"observed_at":"2026-08-06T11:57:00.019049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:57:00.006609Z","title":"Lq-nets: Learned quantization for highly accurate and compact deep neural networks","venue":null,"work_id":"c2b7d633-e0ca-4f49-955f-e49820f5bfb6","year":2018},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.849488Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:5563f4c1fb5e385fd86038e409e2baa97a0bac2510ef3ef53dca390e7ec1f1da","observation_id":"021655fa-a7af-457a-9ffc-ab57d1bf43c4","resolution":{"observed_at":"2026-08-06T11:57:00.010005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06160","last_updated":"2018-02-02T01:43:54Z","snapshot_observed_at":"2026-07-06T05:00:35.763958Z","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":"2026-08-06T11:56:59.852287Z","title":"Dorefa-net: Training low bitwidth convo- lutional neural networks with low bitwidth gradients","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.852287Z"},"links":{"cited_paper":"/paper/1606.06160","citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:3e6fd684288c4ef387856d8c2da42b900c7535f1919510d41fae23734b2bf089","observation_id":"db63a2ab-4df5-4caf-9f49-528d89f05413","resolution":{"observed_at":"2026-08-06T11:56:59.852287Z","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":"2026-08-06T11:56:59.997241Z","title":"1 illustrates how Omega values and bit precision change across layers during the training process of ResNet-","venue":null,"work_id":"66fea8b9-d7e9-4fef-80d9-59b0d7690a2b","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.855464Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:2c5efa5f74c93d6e6e2681021ee80deded091990695223f2d220ab64ec1da820","observation_id":"d1378d23-072f-4e88-9548-6c426eb50988","resolution":{"observed_at":"2026-08-06T11:57:00.000613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:56:59.988239Z","title":"In the ﬁrst pruning step Fig","venue":null,"work_id":"93d1d8d6-d6b0-4463-a1ee-89465807aa4f","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.858481Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:b16778fde5777069b4f9429bfad0eb56d0076e4447ee753e325a140ad9ee31dd","observation_id":"76675883-fafb-45bc-8dca-67f004316dde","resolution":{"observed_at":"2026-08-06T11:56:59.991254Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:56:59.979468Z","title":null,"venue":null,"work_id":"b4662da3-7d5c-49f1-879d-9d305e1a7354","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.861608Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:60d843600e21eb4cc10d2656f12db40229bd848f95fcdaa6cf01087f04308da0","observation_id":"ed913675-e685-4138-9457-e782ea88ec61","resolution":{"observed_at":"2026-08-06T11:56:59.982555Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:56:59.970548Z","title":"While the main pa- per presents results on compact vision transformers such as Table 1","venue":null,"work_id":"7d360c49-c31c-43d7-bb40-c62e68c475bd","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.864506Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:c3d35d0d7d3cc6cce48b454a229889f14ecccd4d1273df9644ed6e93b63b64a8","observation_id":"43b4ec91-7efd-48d0-bc91-747bea1acd12","resolution":{"observed_at":"2026-08-06T11:56:59.973706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:56:59.961105Z","title":"The pruning interval I is crucial for guiding LSB sparsiﬁcation and facil- itating accuracy recovery after pruning","venue":null,"work_id":"9bbe1f61-cb17-4ccb-9c60-59aae49c43c0","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.867664Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:f681b6bb1b50731c5b4ea6a9280de6bc80e7a894c02c1ebf05a2e644731529fe","observation_id":"d0a534d8-57b5-4f28-90d6-60bc46722eed","resolution":{"observed_at":"2026-08-06T11:56:59.964697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T11:56:59.949619Z","title":"Thus, it is essential to carefully tune λ and the pruning threshold α to balance sparsity and accuracy effec- tively","venue":null,"work_id":"19f1ff47-0f12-41a5-b4a2-148520aa7044","year":null},"citing_paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:59.870638Z"},"links":{"citing_paper":"/paper/2507.22349"},"observation_digest":"sha256:711edd68d1ee18d884277a5ba3112dbf32e890e011e11028fb828d0e3524d4a2","observation_id":"fef3cd38-4246-4668-bb30-678ca5247377","resolution":{"observed_at":"2026-08-06T11:56:59.954250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.22349","last_updated":"2025-07-30T03:21:29Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T23:42:10.255138Z","submitted_at":"2025-07-30T03:21:29Z","title":"MSQ: Memory-Efficient Bit Sparsification Quantization"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":37},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.22349."}