{"as_of":"2026-08-18T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:392296952c28b4e8208d905e60f7ff0e2146540d1bfc77cff6d72e398d7e99b1","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:34:57.412377Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T07:09:25.049534Z","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":"2026-07-03T05:27:39.713417Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"cited_work":{"arxiv_id":"2507.10547","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.10547","snapshot_observed_at":"2026-07-03T05:27:39.713417Z","title":"Jiahui Zhang, Fangneng Zhan, Christian Theobalt, and Shijian Lu","venue":null,"work_id":"b309c574-28a2-4e4e-bbc1-d330bb9237d2","year":2025},"citing_paper":{"arxiv_id":"2606.04461","last_updated":"2026-06-03T05:10:51Z","snapshot_observed_at":"2026-08-15T16:27:36.136478Z","submitted_at":"2026-06-03T05:10:51Z","title":"ChannelTok: Efficient Flexible-Length Vision Tokenization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T07:09:25.049534Z"},"links":{"cited_paper":"/paper/2507.10547","citing_paper":"/paper/2606.04461"},"observation_digest":"sha256:abac7583cd42639e33292b404d33c2b8e49604ebd201707af16bf5ddbf002f60","observation_id":"e620f385-af54-4bb4-9596-395452360203","resolution":{"observed_at":"2026-07-02T07:06:44.314153Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"cited_work":{"arxiv_id":"2507.10547","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.10547","snapshot_observed_at":"2026-07-03T05:27:39.713417Z","title":"Jiahui Zhang, Fangneng Zhan, Christian Theobalt, and Shijian Lu","venue":null,"work_id":"b309c574-28a2-4e4e-bbc1-d330bb9237d2","year":2025},"citing_paper":{"arxiv_id":"2606.11363","last_updated":"2026-06-09T18:43:29Z","snapshot_observed_at":"2026-08-12T12:48:37.501537Z","submitted_at":"2026-06-09T18:43:29Z","title":"NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T13:18:47.472178Z"},"links":{"cited_paper":"/paper/2507.10547","citing_paper":"/paper/2606.11363"},"observation_digest":"sha256:ea21b83bb3af71813dc8fa48aa9de599c9ff4c76afaef58c6698d576b41016a7","observation_id":"d51873ce-da92-49df-911b-7e5cf6ec0189","resolution":{"observed_at":"2026-07-03T05:27:39.714854Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.10547/citation-record","integrity":"/paper/2507.10547/integrity","json":"/paper/2507.10547/citation-record.json","paper":"/paper/2507.10547"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.03575","last_updated":"2025-07-09T19:35:31Z","snapshot_observed_at":"2026-08-03T00:21:10.886100Z","submitted_at":"2025-01-07T06:55:50Z","title":"Cosmos World Foundation Model Platform for Physical AI","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.03575","snapshot_observed_at":"2026-08-06T17:34:53.324850Z","title":"Cosmos world foundation model platform for physical ai","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.324850Z"},"links":{"cited_paper":"/paper/2501.03575","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:f59d20cc742ff63a93c78ee77c9176d11ab9f28ec5772bd31bff28e9dedaa069","observation_id":"6d13776c-15ee-4bca-b638-36f7d06c1d33","resolution":{"observed_at":"2026-08-06T17:34:53.324850Z","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":"2026-08-06T17:34:58.329020Z","title":"Sequential modeling enables scalable learning for large vision models","venue":null,"work_id":"97b17b1b-fdba-4cd6-bd16-82247aa3629b","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.408286Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:7f1e0016704ffb44cce8d082657a2b04d6c431f1b013e7812d2ef688d4cf2f09","observation_id":"bef4090c-818f-4beb-9fd2-2bf59bf1030e","resolution":{"observed_at":"2026-08-06T17:34:58.334245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.313501Z","title":"Beit: Bert pre-training of image transformers","venue":null,"work_id":"235a4f17-621a-48ff-ba88-29269e32fc8f","year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.478757Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:a6d059ca1e46421fea483abab72e0cd3d756cb3bfc15f8333662552f0721f942","observation_id":"d5d3bf88-dcd6-4b53-8c38-9171da923c6e","resolution":{"observed_at":"2026-08-06T17:34:58.318202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-06T17:34:53.581160Z","title":"Estimating or propagating gradients through stochastic neurons for conditional computation","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.581160Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:2256130fadd967f18e67c043f577bbc5d84fc660270da9399ce40d3ed4c51ea9","observation_id":"e4bf82d0-e1c5-4576-9ce8-cac96b3ff7d0","resolution":{"observed_at":"2026-08-06T17:34:53.581160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-06T17:34:53.715164Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.715164Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:97e2a06134811c61e26574fc43b39f11c083c0845dcb762b51c100f299cfa670","observation_id":"fdd3f91e-c10f-4482-9e2d-bdc58a23cdee","resolution":{"observed_at":"2026-08-06T17:34:53.715164Z","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":"2026-08-06T17:34:58.297179Z","title":"Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction","venue":null,"work_id":"71325de2-4da0-44b1-a568-59ea9ae3c5b8","year":2023},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.814304Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:e97eed11cd3e12e3e5876b289b7e31e9e3631a1cae6915c9f204cd7f4c74a1e3","observation_id":"0c05fa44-a9bf-4215-a599-476fda321c54","resolution":{"observed_at":"2026-08-06T17:34:58.302847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.281317Z","title":"Efficient-vqgan: Towards high-resolution image generation with efficient vision transformers","venue":null,"work_id":"a326ddbd-a35b-49ee-bff4-06c56502665b","year":2023},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.920145Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:9e6edd3762eea0fd68af50ac92ba9a08c2e0c993dd19a38f61f74565b93cc6fc","observation_id":"bb6bfa9e-147f-4c9f-834f-ba3f713f8066","resolution":{"observed_at":"2026-08-06T17:34:58.286375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.264152Z","title":"Maskgit: Masked generative image transformer","venue":null,"work_id":"04bf1aaa-d898-4d7c-90d1-abe806839c8b","year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:53.987383Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:cd642622053fdf7471f2e171bed0fb76865dddba303ea7f3d4356c91e6b89e46","observation_id":"4f1bdfaa-8854-41a2-9b2d-cc9daf1031ae","resolution":{"observed_at":"2026-08-06T17:34:58.270033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10733","last_updated":"2025-05-18T21:17:22Z","snapshot_observed_at":"2026-08-18T11:02:33.658704Z","submitted_at":"2024-10-14T17:15:07Z","title":"Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10733","snapshot_observed_at":"2026-08-06T17:34:54.066252Z","title":"Deep compression autoencoder for efficient high-resolution diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.066252Z"},"links":{"cited_paper":"/paper/2410.10733","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:2b29819a3c5d8196c76f4c5f3de49f8d99272580ecf88319af2aedc703dbd226","observation_id":"89a4f98d-1ae8-4717-a93a-01bdde1a3bd8","resolution":{"observed_at":"2026-08-06T17:34:54.066252Z","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":"2026-08-06T17:34:58.245269Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"20482180-01bb-45be-b508-1099db9f55ab","year":2009},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.175346Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:da711fe4a6e9573b677b26ef4941181ff535c2f42184cbf2b744711a9f6a1522","observation_id":"1c03790f-a295-454c-9eb0-d9c59cbd7c44","resolution":{"observed_at":"2026-08-06T17:34:58.250244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.227987Z","title":"Taming transformers for high-resolution image synthesis","venue":null,"work_id":"47c05ce8-43ca-420a-9061-59dd9f19ee6d","year":2021},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.281501Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:53c37ede060ad8e184aa560f30a45deb08c3db73f70e85c428efeddc3e87fd69","observation_id":"a9a9de34-9404-4e23-a976-c3ab8fd302dc","resolution":{"observed_at":"2026-08-06T17:34:58.233381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.210308Z","title":"Making llama see and draw with seed tokenizer","venue":null,"work_id":"70a4755f-3ed1-4b5e-8032-d5275cd736f1","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.365428Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:daad7a7aa39f141bd5aa990cdf1d836f661259f8ef6b60109d8d0d879b466e26","observation_id":"4538924e-32da-42c5-840b-a19971c1011e","resolution":{"observed_at":"2026-08-06T17:34:58.216090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.194720Z","title":"Generative adversarial nets","venue":null,"work_id":"7da99c48-b2f9-4d7f-910b-1c31e70c6374","year":2014},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.464540Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:0855b75e6dc656d284968165477bc1fa6af9e998c900c9a9aaa614920176b662","observation_id":"67235580-562d-46fe-bf08-80cedea93729","resolution":{"observed_at":"2026-08-06T17:34:58.199109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.178933Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","venue":null,"work_id":"28968bb8-b8f3-4975-a1e7-03ee50d7ea4d","year":2017},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.567211Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:0618256ef3f627a07189979bb1cb932b2f2c28797ca6b14a8231a45ea8789be6","observation_id":"02754de5-52a4-46be-90d9-1955ec69ea5e","resolution":{"observed_at":"2026-08-06T17:34:58.184091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:34:54.645993Z","title":"Reducing the dimensionality of data with neural networks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.645993Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:9f913256c45c20711d857c5c951a2481f48885bf8034eed280e4f405e1079fbc","observation_id":"a7767200-c83a-4fdd-92e6-d50c86af8e40","resolution":{"observed_at":"2026-08-06T17:34:54.645993Z","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":"2026-08-06T17:34:58.153088Z","title":"Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks","venue":null,"work_id":"72ba54c5-dd30-4c11-b85c-6b38feb74d1a","year":2023},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.738081Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:a7517100f850f4add86dafa29d5b4ed5aa6ef4baf7035446e42c734156b60767","observation_id":"266b44db-84e4-4598-9684-44a76ff543c2","resolution":{"observed_at":"2026-08-06T17:34:58.158896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.136035Z","title":"Image-to-image translation with conditional adversarial networks","venue":null,"work_id":"c9c75b60-0827-40e3-9fa1-c433df634d20","year":2017},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.848942Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:8755d314ffc47548867399f55fed25005d95647164b28679a442342d28625566","observation_id":"b0ef9193-d2fc-4197-ad45-269bc5b15302","resolution":{"observed_at":"2026-08-06T17:34:58.142003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.119056Z","title":"Unified language-vision pretraining in llm with dynamic discrete visual tokenization","venue":null,"work_id":"b2315192-1f72-47c0-8098-86c0ff28415b","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:54.928444Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:b510bfbfe90e43c3c608ad12bc439412eb16ea1a15a4e6f5d0a62ec7948878d1","observation_id":"fb512558-c19b-4771-a97b-d47ab80e48dc","resolution":{"observed_at":"2026-08-06T17:34:58.124232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.102281Z","title":"Perceptual losses for real-time style transfer and super-resolution","venue":null,"work_id":"f9af1ae3-d0ee-461a-a321-99b35363e6f6","year":2016},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.040423Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:ebf2a128b5443959c1d551076c1f2b86f478affa5856a7142daee4c153e6b561","observation_id":"d6813e7a-4472-43bb-90ff-88fb8bce4b60","resolution":{"observed_at":"2026-08-06T17:34:58.107432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-06T17:34:55.123361Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.123361Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:b408aef0f1cbb011ad6559961e9446cd1259d6e849304e675eaa3eacf7cc7029","observation_id":"844c2563-09f3-4f63-9d4e-90da474ca2d9","resolution":{"observed_at":"2026-08-06T17:34:55.123361Z","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":"2026-08-06T17:34:58.085718Z","title":"Autoencoding beyond pixels using a learned similarity metric","venue":null,"work_id":"70ab9c23-1e20-4f15-962d-b119dc6603ce","year":2016},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.212851Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:c54b2352013e1033fe943c62634009350cff51e695b9aeacb272d61498a7f170","observation_id":"9bc0ca49-2723-43bc-ba51-d1aca8a4a0ca","resolution":{"observed_at":"2026-08-06T17:34:58.091571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.066683Z","title":"Autoregressive image generation using residual quantization","venue":null,"work_id":"4e6325a1-1ff4-44c3-bff0-ba4a354345f1","year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.325306Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:5d6f33660c02a12b7851be29cd66f1eafbad39d367f547aa627b60295f7c047f","observation_id":"376f562c-8ea0-4bef-98ae-f1cd8816b237","resolution":{"observed_at":"2026-08-06T17:34:58.073056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:58.047548Z","title":"Imagefolder: Autoregressive image generation with folded tokens","venue":null,"work_id":"0b5bf9b4-79bb-498d-9812-a255402de0de","year":2025},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.418469Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:505d0cc0d21ec878d6e9b0f7fc5738ce37fab5473ef7b9d718095ac0479854c3","observation_id":"7b9476ec-888c-4a38-bd6d-a99fe9aa7fd7","resolution":{"observed_at":"2026-08-06T17:34:58.053548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:34:55.486605Z","title":"Coda: Repurposing continuous vaes for discrete tokenization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.486605Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:8acbef4423756a4c17e10198bf624b1e9b1b2cf9f1bab6fb507ad418716716d2","observation_id":"078135d7-761c-45ba-81ae-65a034f8b129","resolution":{"observed_at":"2026-08-06T17:34:55.486605Z","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":"2026-08-06T17:34:55.610229Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.610229Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:b9b66f76873a038019055052ad525ef1460e09e8921feb5650b3bbb44ae1f141","observation_id":"63fc74cb-16f1-4789-8ea4-f77b1d0a1193","resolution":{"observed_at":"2026-08-06T17:34:55.610229Z","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":"2026-08-06T17:34:55.710416Z","title":"Unitok: A unified tokenizer for visual generation and understanding","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.710416Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:e2ad0e9efd7a5310e6cdeaa4d254b9100fab32296ed840657509dde0b506217e","observation_id":"a476ca04-1e45-457b-9c42-0e3225327cbb","resolution":{"observed_at":"2026-08-06T17:34:55.710416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15505","last_updated":"2023-10-12T07:55:05Z","snapshot_observed_at":"2026-07-06T16:24:17.829828Z","submitted_at":"2023-09-27T09:13:40Z","title":"Finite Scalar Quantization: VQ-VAE Made Simple","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15505","snapshot_observed_at":"2026-08-06T17:34:55.810987Z","title":"Finite scalar quantization: Vq-vae made simple","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.810987Z"},"links":{"cited_paper":"/paper/2309.15505","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:f976876202f033b42dd5707bd6f588772e2857b974d10db2cbf7fe8582996e20","observation_id":"8db4ecb3-7768-4f44-af38-9e7cf65cb2db","resolution":{"observed_at":"2026-08-06T17:34:55.810987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-06T17:34:55.872765Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.872765Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:b77b77d79e3c936183f2b53b2c6af6395a36199f1fb09eda308f37304ec8956d","observation_id":"d8b04a58-ea4a-4c0b-a2ac-15afaad2195b","resolution":{"observed_at":"2026-08-06T17:34:55.872765Z","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":"2026-08-06T17:34:58.017980Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":"4dae133f-5cd3-4424-a26a-6fa2b0b661df","year":2019},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:55.994749Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:c9fde910c2da6076052b92c483200939966c69401e249e3c7e753bc7192212bc","observation_id":"30328996-8a35-4d2f-94ea-dd0463b5e4a2","resolution":{"observed_at":"2026-08-06T17:34:58.024236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:57.998424Z","title":"Generating diverse high-fidelity images with vq-vae-2","venue":null,"work_id":"8e3d247b-5da8-4fd0-aa4e-212e37f00832","year":2019},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.116231Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:3ca156907a7f99e7c2b539aaa3cc1e09d3e74a4154e3eda0ec06672cbb8270e8","observation_id":"059f2c69-cc51-42d3-89bf-7843480f9fe9","resolution":{"observed_at":"2026-08-06T17:34:58.004521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:34:56.263915Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.263915Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:2b96cef6669841f24ec12c72adcd6252d59b21f24c11ac13a51378cead6233c2","observation_id":"1d929ec7-fb04-44f3-92a7-92393d153cf4","resolution":{"observed_at":"2026-08-06T17:34:56.263915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06525","last_updated":"2024-06-10T17:59:52Z","snapshot_observed_at":"2026-08-13T22:05:34.844117Z","submitted_at":"2024-06-10T17:59:52Z","title":"Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06525","snapshot_observed_at":"2026-08-06T17:34:56.385577Z","title":"Autoregressive model beats diffusion: Llama for scalable image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.385577Z"},"links":{"cited_paper":"/paper/2406.06525","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:8ea9247c27ec56f088be2cb54a4ff9fbe099489ae6a7ce27a147fa21ee9de927","observation_id":"c6fa32a0-b339-4fa7-88a2-71040c987b3c","resolution":{"observed_at":"2026-08-06T17:34:56.385577Z","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":"2026-08-06T17:34:57.968192Z","title":"Visual autoregressive modeling: Scalable image generation via next-scale prediction","venue":null,"work_id":"d7d863e1-2d82-4de2-a71a-18e500da1900","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.479039Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:446246461071044211b215bde754035925eb62aec30a6250c1d3a2caf8a2e0a7","observation_id":"8693338f-dcd4-4928-a44f-d51fe3e0537f","resolution":{"observed_at":"2026-08-06T17:34:57.974098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:34:56.630559Z","title":"Neural discrete representation learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.630559Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:c956819dfc39a0da5cabe89cd7fe8e26e64e9e11b0f311f2c03140dec4b23505","observation_id":"6fa439a7-cd9c-40d3-a1e1-aec5151407fe","resolution":{"observed_at":"2026-08-06T17:34:56.630559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.16430","last_updated":"2025-08-29T02:25:47Z","snapshot_observed_at":"2026-08-16T12:48:10.376859Z","submitted_at":"2025-03-20T17:59:59Z","title":"Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16430","snapshot_observed_at":"2026-08-06T17:34:56.721604Z","title":"Bridging continuous and discrete tokens for autoregressive visual generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.721604Z"},"links":{"cited_paper":"/paper/2503.16430","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:017991c0b8d0bfcc896acbd116e916314dc0d31ade4c08fdfd458d2acc198e6e","observation_id":"6e6c8907-d525-4e33-89c3-0ebdcfa23c5e","resolution":{"observed_at":"2026-08-06T17:34:56.721604Z","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":"2026-08-06T17:34:57.940264Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":"55e74a11-a33c-482f-b2e7-219d932a1fe0","year":2004},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:56.855740Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:0405b7c464d6d5312dbd88bf28b4cecc46bc7ee0b2b0c7ba566c6f943dab9959","observation_id":"a38ed33f-67da-4871-8be9-2ac9c12565b7","resolution":{"observed_at":"2026-08-06T17:34:57.945992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16211","last_updated":"2024-12-08T19:55:36Z","snapshot_observed_at":"2026-08-16T13:15:48.133270Z","submitted_at":"2024-09-24T16:12:12Z","title":"MaskBit: Embedding-free Image Generation via Bit Tokens","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.16211","snapshot_observed_at":"2026-08-06T17:34:57.020631Z","title":"Maskbit: Embedding-free image generation via bit tokens","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.020631Z"},"links":{"cited_paper":"/paper/2409.16211","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:9801bc0fdbb5945fbf2f55be3897b4844602d3b607f3e1fed1213f64d8bb4702","observation_id":"1276ac86-5a7f-483a-bb71-01fe61f451b5","resolution":{"observed_at":"2026-08-06T17:34:57.020631Z","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":"2026-08-06T17:34:57.147209Z","title":"Vector-quantized image modeling with improved vqgan","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.147209Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:37d8a114f7af2423a3c57899f72286573096fb2f4bc28b574681341945786a13","observation_id":"43104f53-51cc-47b3-8215-d2bb232bcba0","resolution":{"observed_at":"2026-08-06T17:34:57.147209Z","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":"2026-08-06T17:34:57.912440Z","title":"Language model beats diffusion-tokenizer is key to visual generation","venue":null,"work_id":"1712b369-8db4-4441-9f09-f946aebe42fa","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.269686Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:e6f439bd78d802bd31731dabdbbb0c53f1d878048dd975ecc32cb1c571766b03","observation_id":"7e3e3bbc-72e6-47c9-9524-faaa152eb636","resolution":{"observed_at":"2026-08-06T17:34:57.918105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:57.896102Z","title":"An image is worth 32 tokens for reconstruction and generation","venue":null,"work_id":"c60c60b3-4aa3-4d74-b3c3-c433662636e1","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.390771Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:e3d42095fa1e6602265567f4a91a9b89cb6c0c48a6cb8ce8cc20f7d2100e8f2b","observation_id":"7ebc17c2-5621-4ae2-8840-c6efd4dd35dd","resolution":{"observed_at":"2026-08-06T17:34:57.901482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15195","last_updated":"2024-12-19T18:58:14Z","snapshot_observed_at":"2026-08-14T11:36:16.366064Z","submitted_at":"2024-12-19T18:58:14Z","title":"Preventing Local Pitfalls in Vector Quantization via Optimal Transport","version":1},"cited_work":{"arxiv_id":"2412.15195","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.15195","snapshot_observed_at":"2026-08-06T17:34:57.475555Z","title":"Preventing Local Pitfalls in Vector Quantization via Optimal Transport","venue":"cs.CV","work_id":"7310724e-e542-4d6c-a696-7ea72d78cf40","year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.395098Z"},"links":{"cited_paper":"/paper/2412.15195","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:911fa24d136e726a883989fedd608c4e59247819d4d13e44932da0ba32e8f228","observation_id":"4ee414ec-de86-4feb-b144-a30196dd9f79","resolution":{"observed_at":"2026-08-06T17:34:57.483082Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:57.878240Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":"1f88f7a9-3dad-4363-a5e1-5d72d51ccbbb","year":2018},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.399405Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:bb715e01877c0a0febfafcd0b3423e7191c9f36a7bd7c7b6c923edd2c5af94b4","observation_id":"8b41b840-113d-4c7b-9eba-9564390f40f7","resolution":{"observed_at":"2026-08-06T17:34:57.884191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T17:34:57.858966Z","title":"Movq: Modulating quantized vectors for high-fidelity image generation","venue":null,"work_id":"00c23370-7586-43de-a1bc-c1d614329c89","year":2022},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.403771Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:4d0a5375de51d527ab581161a3db8f11a9b3fbe9b249364b68ad7e38dc43d849","observation_id":"0bd58538-844c-4411-bfc5-55ae55ad6689","resolution":{"observed_at":"2026-08-06T17:34:57.865394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11837","last_updated":"2024-06-17T17:59:57Z","snapshot_observed_at":"2026-08-17T04:16:36.952747Z","submitted_at":"2024-06-17T17:59:57Z","title":"Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11837","snapshot_observed_at":"2026-08-06T17:34:57.408009Z","title":"Scaling the codebook size of vqgan to 100,000 with a utilization rate of 99\\ arXiv, abs/2406.11837, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.408009Z"},"links":{"cited_paper":"/paper/2406.11837","citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:1cdffa15126e995073202615fb793d58e9797dd9284812a417e1856d22c77b47","observation_id":"750424a1-162d-4ad8-82f6-b5b90669c3c6","resolution":{"observed_at":"2026-08-06T17:34:57.408009Z","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":"2026-08-06T17:34:57.412377Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T17:34:57.412377Z"},"links":{"citing_paper":"/paper/2507.10547"},"observation_digest":"sha256:eaa96db35401a15245421a5714225c672da7d2dfa32fa876e20d167f0a11c4ae","observation_id":"eac202ce-a54a-4c99-bf3a-278b245311c0","resolution":{"observed_at":"2026-08-06T17:34:57.412377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.10547","last_updated":"2025-07-14T17:59:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T06:35:50.115529Z","submitted_at":"2025-07-14T17:59:41Z","title":"Quantize-then-Rectify: Efficient VQ-VAE Training"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":25},"total_outbound_references":45},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2507.10547."}