{"as_of":"2026-08-23T02:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67c8d2d17b69abb73bb0199518f95eb475a611c98b5c9fdbac9da3628959125a","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T11:08:55.344254Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2501.16738/citation-record","integrity":"/paper/2501.16738/integrity","json":"/paper/2501.16738/citation-record.json","paper":"/paper/2501.16738"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-10T11:08:55.293053Z","title":"Vision mamba: Efficient visual represen- tation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.293053Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:d6bbda125a4af2d3c38f8bb1618fca46e3098f5d1d5e47abc7acdef1edecd918","observation_id":"f954cd1b-44ca-421c-8704-b9bc502c1f5b","resolution":{"observed_at":"2026-08-10T11:08:55.293053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-10T11:08:55.296129Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.296129Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:f3dfeaff06a82fb3f2f286d43c40d077addc00e0f8347e1597b3443a22bafc60","observation_id":"29b834f0-11a9-42e7-a0d3-af29415eed82","resolution":{"observed_at":"2026-08-10T11:08:55.296129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.09602","last_updated":"2020-04-20T19:59:22Z","snapshot_observed_at":"2026-08-14T00:25:24.218417Z","submitted_at":"2020-04-20T19:59:22Z","title":"Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.09602","snapshot_observed_at":"2026-08-10T11:08:55.298739Z","title":"Integer quantization for deep learning inference: Principles and empirical evaluation,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.298739Z"},"links":{"cited_paper":"/paper/2004.09602","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:dd354101b446cc96e22bc765e77dff87f33019b824457b0a6f50ac7a135c3aa0","observation_id":"1e768386-00b8-4cd4-ac55-c8742de0c34d","resolution":{"observed_at":"2026-08-10T11:08:55.298739Z","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":"2026-08-10T11:08:55.490219Z","title":"Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization,","venue":null,"work_id":"85d8ded0-6243-443d-b35c-a1ad0c8f40be","year":2022},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.301333Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:4eb672adf50e91fbd9d5c87e1e9393dbe66135c7f217433c419505d454b7e1f3","observation_id":"eff074e1-51c4-45c1-a398-2ab0876a6f0a","resolution":{"observed_at":"2026-08-10T11:08:55.492983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13824","last_updated":"2023-02-17T13:17:52Z","snapshot_observed_at":"2026-08-16T22:48:47.551504Z","submitted_at":"2021-11-27T06:20:53Z","title":"FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.13824","snapshot_observed_at":"2026-08-10T11:08:55.303914Z","title":"Fq-vit: Post-training quantization for fully quantized vision trans- former,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.303914Z"},"links":{"cited_paper":"/paper/2111.13824","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:2d935f48ecfe56d357d213340f673ed49740f4162084c1316d281d128fb866be","observation_id":"ea4c122b-7808-4fde-a789-dda26a000e9b","resolution":{"observed_at":"2026-08-10T11:08:55.303914Z","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":"2026-08-10T11:08:55.482593Z","title":"Tsptq-vit: Two- scaled post-training quantization for vision transformer,","venue":null,"work_id":"5e7afa62-ca38-48c5-b107-8ce571174005","year":2023},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.306591Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:47d4f094c83252ed6a7c81598c99e48da4debb4fa80bc6c1e4e1ab6233bd17ee","observation_id":"1c959f25-90b7-44f6-a6f9-8d2439061b58","resolution":{"observed_at":"2026-08-10T11:08:55.485153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T11:08:55.475776Z","title":"Smoothquant: Accurate and efficient post-training quantization for large language models,","venue":null,"work_id":"a0fc1525-4b4d-418d-8a7f-3325af00cfeb","year":2023},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.309366Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:e5a7c7ff836ec02cff56d76559e640294aa13d86b33ca081a777acf6018db698","observation_id":"8132ea8e-a368-4c99-915d-63dafa5df80c","resolution":{"observed_at":"2026-08-10T11:08:55.477916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T11:08:55.468125Z","title":"Awq: Activation-aware weight quantization for on-device llm compression and acceleration,","venue":null,"work_id":"45edd0b0-9f51-4a26-91ce-8ce8aecc80be","year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.311218Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:76576e8184bad34ac3b9c82c4310a7564a23f9ff6121e9bfa2df55f60bc9cdc6","observation_id":"f8811ffd-d973-4868-92e0-77d53b173e79","resolution":{"observed_at":"2026-08-10T11:08:55.470265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T11:08:55.461785Z","title":"Repq-vit: Scale reparameterization for post-training quantization of vision transformers,","venue":null,"work_id":"85367b2a-1790-475d-928e-3f55091822e9","year":2023},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.313033Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:a3b42a82d36437e5bedec36df0b173fabd5eb4580c8e8695b8bc78bfcc4aad05","observation_id":"9e8e5cac-84a5-4946-96c2-aa5d2e499ad1","resolution":{"observed_at":"2026-08-10T11:08:55.464157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14895","last_updated":"2024-02-01T02:05:02Z","snapshot_observed_at":"2026-08-19T23:42:59.999788Z","submitted_at":"2024-01-26T14:25:15Z","title":"MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14895","snapshot_observed_at":"2026-08-10T11:08:55.314827Z","title":"Mptq-vit: Mixed-precision post-training quantiza- tion for vision transformer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.314827Z"},"links":{"cited_paper":"/paper/2401.14895","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:7280a4f01959cbeb535fbc199c5d02c0d406770b2cd6a4a43060f1244b5ca358","observation_id":"05ceff5d-ef32-4b7f-935c-2917afd5d745","resolution":{"observed_at":"2026-08-10T11:08:55.314827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-10T11:08:55.317084Z","title":"Vmamba: Visual state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.317084Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:4cff5b25ad392a58d32723626ff076deb3bd9977c9a320113691be7402dbbfc0","observation_id":"d461c9bf-29b8-48cf-8b63-927492f06251","resolution":{"observed_at":"2026-08-10T11:08:55.317084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08083","last_updated":"2025-03-25T17:54:37Z","snapshot_observed_at":"2026-08-19T13:18:49.777037Z","submitted_at":"2024-07-10T23:02:45Z","title":"MambaVision: A Hybrid Mamba-Transformer Vision Backbone","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08083","snapshot_observed_at":"2026-08-10T11:08:55.319867Z","title":"Mambavision: A hybrid mamba- transformer vision backbone,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.319867Z"},"links":{"cited_paper":"/paper/2407.08083","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:7119149cfabb2917c09783b1ac26c08c49b6e15036801f2bb2d93c76efd12840","observation_id":"8e6b766a-4ff9-4dd1-85d5-44718214c069","resolution":{"observed_at":"2026-08-10T11:08:55.319867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12397","last_updated":"2024-07-17T08:21:06Z","snapshot_observed_at":"2026-08-16T13:33:30.855021Z","submitted_at":"2024-07-17T08:21:06Z","title":"Mamba-PTQ: Outlier Channels in Recurrent Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12397","snapshot_observed_at":"2026-08-10T11:08:55.322993Z","title":"Mamba-ptq: Outlier channels in recurrent large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.322993Z"},"links":{"cited_paper":"/paper/2407.12397","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:433207355f7829f8d6f7556b211fcb1548f54ed6e1b26ac548a654b54f199995","observation_id":"c9089625-b139-464d-b308-1be759afd21e","resolution":{"observed_at":"2026-08-10T11:08:55.322993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13229","last_updated":"2024-12-07T07:27:00Z","snapshot_observed_at":"2026-08-16T13:08:33.950728Z","submitted_at":"2024-10-17T05:32:33Z","title":"Quamba: A Post-Training Quantization Recipe for Selective State Space Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13229","snapshot_observed_at":"2026-08-10T11:08:55.325477Z","title":"Quamba: A post-training quantization recipe for selective state space models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.325477Z"},"links":{"cited_paper":"/paper/2410.13229","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:f1879f20e5f4ebe3a85426463a3c14d4ab8e1bcc830de351e05011d3f01fd515","observation_id":"12f17093-1a99-49a4-b651-65583759e28e","resolution":{"observed_at":"2026-08-10T11:08:55.325477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.21060","last_updated":"2024-05-31T17:50:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-31T17:50:01Z","title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.21060","snapshot_observed_at":"2026-08-10T11:08:55.328040Z","title":"Transformers are ssms: Generalized models and efficient algorithms through structured state space duality,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.328040Z"},"links":{"cited_paper":"/paper/2405.21060","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:036e914a711805a58a792bbc7a832b643e70af8323681181025865d7f7d68465","observation_id":"4823ab22-5111-4b2c-8404-6da4b9d02695","resolution":{"observed_at":"2026-08-10T11:08:55.328040Z","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":"2026-08-10T11:08:55.330438Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.330438Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:7d9377500c99652ffd37f2dd5b41fc5a6925a013ebdb4be42f9997ff826ffd06","observation_id":"4df91f3c-0726-4636-aa6e-c10002cb68fe","resolution":{"observed_at":"2026-08-10T11:08:55.330438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-10T11:08:55.332699Z","title":"Efficiently model- ing long sequences with structured state spaces,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.332699Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:7a609c1ca822f3746ba5d9afd471cc9060fbcaf07a43ca23fa952f930e1a44d8","observation_id":"3b0bc884-5e8a-43f9-8ebe-46cce7429a27","resolution":{"observed_at":"2026-08-10T11:08:55.332699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-10T11:08:55.335090Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.335090Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:ae69c9d1be8353852458bc7a953961893564a098023ce8da2aa7f2b7f6245aed","observation_id":"8f48e605-5d05-4809-af33-8fae5d9efcc5","resolution":{"observed_at":"2026-08-10T11:08:55.335090Z","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":"2026-08-10T11:08:55.337072Z","title":"Quantization and training of neural networks for efficient integer- arithmetic-only inference,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.337072Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:601e5af95d2b8412e0722f441e9da535733c6994cb05103e48a185edfa8cabbd","observation_id":"168389dd-bb53-481e-845f-7513602c36cb","resolution":{"observed_at":"2026-08-10T11:08:55.337072Z","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":"2026-08-10T11:08:55.338828Z","title":"Up or down? adaptive rounding for post- training quantization,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.338828Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:bad7c98685d01c75ab0d29f584f5b86e029d6d4b16d0c66e08e3a0f470c8234b","observation_id":"c0219cca-6c96-48b9-92eb-f34e5cb2d879","resolution":{"observed_at":"2026-08-10T11:08:55.338828Z","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":"2026-08-10T11:08:55.442045Z","title":"Low- bit quantization of neural networks for efficient inference,","venue":null,"work_id":"c4726ed1-d68e-459c-bea9-3b95f637e0a6","year":2019},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.340703Z"},"links":{"citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:4ec0fc5ee62641c331e4355424a5b0875c8c6e3ba67daac9430d56183d363140","observation_id":"227b0365-ef70-4e96-ad96-8f3f96f083f5","resolution":{"observed_at":"2026-08-10T11:08:55.446101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16669","last_updated":"2020-06-30T10:43:02Z","snapshot_observed_at":"2026-08-09T20:14:01.374815Z","submitted_at":"2020-06-30T10:43:02Z","title":"EasyQuant: Post-training Quantization via Scale Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.16669","snapshot_observed_at":"2026-08-10T11:08:55.342397Z","title":"Easyquant: Post-training quantization via scale optimization,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.342397Z"},"links":{"cited_paper":"/paper/2006.16669","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:e2c2b7f706e36ffe80620cf283b698eb6e438b350da9995cbd015c5448864e9e","observation_id":"008801fa-7cb9-4a47-961e-b0d068e2262d","resolution":{"observed_at":"2026-08-10T11:08:55.342397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09977","last_updated":"2024-03-15T02:48:47Z","snapshot_observed_at":"2026-08-17T01:16:32.185673Z","submitted_at":"2024-03-15T02:48:47Z","title":"EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09977","snapshot_observed_at":"2026-08-10T11:08:55.344254Z","title":"Efficientvmamba: Atrous selective scan for light weight visual mamba,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T11:08:55.344254Z"},"links":{"cited_paper":"/paper/2403.09977","citing_paper":"/paper/2501.16738"},"observation_digest":"sha256:8de105afd4252c56f11e3bb4c6f86b705dc88bacf5eb7e92ed61072fed7e1cd1","observation_id":"d908720d-f5d6-415c-a9a2-8945a4edf26b","resolution":{"observed_at":"2026-08-10T11:08:55.344254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.16738","last_updated":"2025-02-13T07:51:12Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-19T13:20:18.711691Z","submitted_at":"2025-01-28T06:22:30Z","title":"Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":23},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2501.16738."}