{"as_of":"2026-08-07T10:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcedda6c7d0f3fdfad5181c710400b8acc03a512102503456c6a3dee2191d634","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:24:17.854387Z","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":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T15:11:09.157452Z","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":"2026-07-04T06:29:37.605132Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2604.03420","last_updated":"2026-05-08T06:46:47Z","snapshot_observed_at":"2026-07-31T05:51:44.917483Z","submitted_at":"2026-04-03T19:33:25Z","title":"Zero-Shot Quantization via Weight-Space Arithmetic","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T20:07:41.196837Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2604.03420"},"observation_digest":"sha256:30de73d2d58da35be0484ea47d7795186b2615f99e9d762a107379d44c77c96a","observation_id":"cc0bb48c-2b7f-4807-b097-a7f8e6b65a48","resolution":{"observed_at":"2026-05-13T20:08:12.611754Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2604.15416","last_updated":"2026-04-16T17:55:36Z","snapshot_observed_at":"2026-07-06T23:02:58.361418Z","submitted_at":"2026-04-16T17:55:36Z","title":"StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T12:10:44.802059Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2604.15416"},"observation_digest":"sha256:cd0061b61dd6feda7f8f855f495f5b37a0d22b5389d33f511289fbfeebfacf37","observation_id":"7c13a4b4-3f55-48c8-a253-9f9f7be4021e","resolution":{"observed_at":"2026-05-10T12:15:22.222337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2605.10886","last_updated":"2026-07-09T02:30:06Z","snapshot_observed_at":"2026-07-12T23:17:31.243229Z","submitted_at":"2026-05-11T17:32:29Z","title":"LoKA: Low-precision Kernel Applications for Recommendation Models At Scale","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-12T04:33:41.411292Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2605.10886"},"observation_digest":"sha256:d00b20b8c2d0c4b585d2d92d312c4d11277b163c2b8ecd6de66aaaceb99ea928","observation_id":"159391d1-f2ed-4895-9a8d-0999c5284a78","resolution":{"observed_at":"2026-05-12T06:06:28.230353Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2605.10886","last_updated":"2026-07-09T02:30:06Z","snapshot_observed_at":"2026-07-12T23:17:31.243229Z","submitted_at":"2026-05-11T17:32:29Z","title":"LoKA: Low-precision Kernel Applications for Recommendation Models At Scale","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-15T04:55:01.973832Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2605.10886"},"observation_digest":"sha256:5656cb337e20282425a48f62c06fbbf1e2fff2f5cf44df89e8af9ddb36506b37","observation_id":"e25d6f60-c26b-4a7a-8a7e-5ec5fe1938a5","resolution":{"observed_at":"2026-05-15T04:59:46.033838Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2605.21442","last_updated":"2026-05-20T17:32:08Z","snapshot_observed_at":"2026-08-01T15:02:26.952382Z","submitted_at":"2026-05-20T17:32:08Z","title":"torchtune: PyTorch native post-training library","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-21T05:43:28.852881Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2605.21442"},"observation_digest":"sha256:247640e55c8cb40623f3f533f093ff878b7645ed4cc2959bedd379097720e04b","observation_id":"931e6dab-9c33-4ac9-8d17-87802f39186a","resolution":{"observed_at":"2026-05-21T05:43:58.665641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":"2507.16099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-04T06:29:37.605132Z","title":"Torchao: Pytorch-native training-to-serving model optimization","venue":null,"work_id":"f035972b-87bf-4c8a-85cb-625a86a4f9fc","year":2025},"citing_paper":{"arxiv_id":"2606.21413","last_updated":"2026-06-19T13:28:59Z","snapshot_observed_at":"2026-08-07T08:11:02.632627Z","submitted_at":"2026-06-19T13:28:59Z","title":"CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-06-26T14:26:38.264174Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2606.21413"},"observation_digest":"sha256:cee0e9b1b8a78112d80df1f7707052cfa233c9ad850df9426b9dadcaf028015f","observation_id":"74c80bb9-a86d-47ca-a07a-74b108be140b","resolution":{"observed_at":"2026-07-04T06:29:37.606958Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-07-13T01:10:03.032181Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.08993","last_updated":"2026-07-09T23:52:57Z","snapshot_observed_at":"2026-08-03T16:02:09.671176Z","submitted_at":"2026-07-09T23:52:57Z","title":"StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T01:10:03.032181Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2607.08993"},"observation_digest":"sha256:63d5986920290da993137c4afd0d31d62d2587351a13c9328322f0da4c980b21","observation_id":"136220bb-1fef-44b7-9aba-4e218fb10ad2","resolution":{"observed_at":"2026-07-13T01:10:03.032181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16099","snapshot_observed_at":"2026-08-01T15:11:09.157452Z","title":"TorchAO: Pytorch-native training-to-serving model optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18540","last_updated":"2026-08-06T17:54:18Z","snapshot_observed_at":"2026-08-07T09:21:42.859052Z","submitted_at":"2026-07-20T22:14:04Z","title":"Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T15:11:09.157452Z"},"links":{"cited_paper":"/paper/2507.16099","citing_paper":"/paper/2607.18540"},"observation_digest":"sha256:ade89a0b9ff1e7c15645f418dacd440e33ee3445611a70ced15ae64e032b774e","observation_id":"1b1ccc5c-42f3-402b-8bb4-12c1d15be4f8","resolution":{"observed_at":"2026-08-01T15:11:09.157452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.16099/citation-record","integrity":"/paper/2507.16099/integrity","json":"/paper/2507.16099/citation-record.json","paper":"/paper/2507.16099"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.16672","last_updated":"2025-03-20T19:37:12Z","snapshot_observed_at":"2026-07-06T20:56:22.633984Z","submitted_at":"2025-03-20T19:37:12Z","title":"Accelerating Transformer Inference and Training with 2:4 Activation Sparsity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16672","snapshot_observed_at":"2026-08-06T15:24:16.428845Z","title":"Accelerating transformer inference and training with 2:4 activation sparsity","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.428845Z"},"links":{"cited_paper":"/paper/2503.16672","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:53c0b386fdd9f717dc737905d67dd0da60d7b3f4bd984406cfc9e8849bd8d5b2","observation_id":"6ac0db3a-5086-4245-be3f-038de41f42d0","resolution":{"observed_at":"2026-08-06T15:24:16.428845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.15748","last_updated":"2025-03-19T23:38:49Z","snapshot_observed_at":"2026-07-06T20:55:47.967653Z","submitted_at":"2025-03-19T23:38:49Z","title":"PARQ: Piecewise-Affine Regularized Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.15748","snapshot_observed_at":"2026-08-06T15:24:16.681949Z","title":"Parq: Piecewise-affine regularized quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.681949Z"},"links":{"cited_paper":"/paper/2503.15748","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:03bb6c2032e41f628ec037649530f7d6ab458a07b065493cb490bd0eba378177","observation_id":"564d2034-e16c-47d9-8aa3-0f0cbb3509e4","resolution":{"observed_at":"2026-08-06T15:24:16.681949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06511","last_updated":"2025-06-07T19:16:22Z","snapshot_observed_at":"2026-08-07T00:12:09.462694Z","submitted_at":"2024-10-09T03:26:11Z","title":"TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06511","snapshot_observed_at":"2026-08-06T15:24:16.814568Z","title":"Torchtitan: One-stop pytorch native solution for production ready llm pre-training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.814568Z"},"links":{"cited_paper":"/paper/2410.06511","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:c40b9c6aa0e6a84acecb65ad16f66e2c9d10c44ef41e8883069999bdfb1bd8b6","observation_id":"b832b031-4885-4357-8925-1d1b508f3784","resolution":{"observed_at":"2026-08-06T15:24:16.814568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16406","last_updated":"2025-02-20T06:07:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-26T02:15:49Z","title":"SpinQuant: LLM quantization with learned rotations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16406","snapshot_observed_at":"2026-08-06T15:24:16.930551Z","title":"Spinquant: Llm quantization with learned rotations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.930551Z"},"links":{"cited_paper":"/paper/2405.16406","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:c497ad85e4b2f7e4b73cf35ca7b8199c685a9f51872886657cad1b3847f647d9","observation_id":"28dc0925-555a-4564-98fb-2ff809e8a9e7","resolution":{"observed_at":"2026-08-06T15:24:16.930551Z","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":"2026-08-06T15:24:17.043894Z","title":"Paretoq: Scaling laws in extremely low-bit llm quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.043894Z"},"links":{"citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:7a102e0e829bfd4d746cc9200978b80ee0f4dea4fe143e7716145ffd76a012a3","observation_id":"30b9630a-19d8-4f88-b4d8-f816bd38a0c7","resolution":{"observed_at":"2026-08-06T15:24:17.043894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08378","last_updated":"2021-04-16T21:27:32Z","snapshot_observed_at":"2026-08-04T11:24:11.301809Z","submitted_at":"2021-04-16T21:27:32Z","title":"Accelerating Sparse Deep Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08378","snapshot_observed_at":"2026-08-06T15:24:17.178556Z","title":"The llama 4 herd: The beginning of a new era of natively multimodal ai in- novation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.178556Z"},"links":{"cited_paper":"/paper/2104.08378","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:5a4e2306ea01790357d2894fdfbbca433da3df5b04b358fe398e6e1d76397dda","observation_id":"74821af5-3621-4e38-a94a-85b7a787aef5","resolution":{"observed_at":"2026-08-06T15:24:17.178556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10537","last_updated":"2023-10-19T16:38:33Z","snapshot_observed_at":"2026-07-06T16:33:55.369704Z","submitted_at":"2023-10-16T16:07:41Z","title":"Microscaling Data Formats for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10537","snapshot_observed_at":"2026-08-06T15:24:17.313135Z","title":"D., Zhao, R., More, A., Hall, M., Khodamoradi, A., Deng, S., Choudhary, D., Cornea, M., Dellinger, E., Denolf, K., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.313135Z"},"links":{"cited_paper":"/paper/2310.10537","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:7dde037f915a9bba83898527f6c23454758dbbb85ec808d9db70d8ad0bcee11e","observation_id":"b732f6c7-90c8-4a87-af9b-f6255f175922","resolution":{"observed_at":"2026-08-06T15:24:17.313135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03771","snapshot_observed_at":"2026-08-06T15:24:17.619438Z","title":"Diffusers: State-of-the- art diffusion models","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.619438Z"},"links":{"cited_paper":"/paper/1910.03771","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:2467768d1dd77eed99825d6315738a00a419aee539bfbc4de6cff6824d98ae72","observation_id":"c6293d38-3f58-4ad3-a320-9682b93cfbd1","resolution":{"observed_at":"2026-08-06T15:24:17.619438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03507","last_updated":"2024-06-02T21:24:12Z","snapshot_observed_at":"2026-07-06T17:40:15.746482Z","submitted_at":"2024-03-06T07:29:57Z","title":"GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03507","snapshot_observed_at":"2026-08-06T15:24:17.752626Z","title":"Galore: Memory-efficient llm train- ing by gradient low-rank projection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.752626Z"},"links":{"cited_paper":"/paper/2403.03507","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:4898aa3c6af85149122ee4390d1a0a3a1502036d51f947e43b4cc20b36ccbe65","observation_id":"66dda01f-7d0e-465e-9fa7-60b22784bcb4","resolution":{"observed_at":"2026-08-06T15:24:17.752626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07104","last_updated":"2024-06-06T00:10:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-12T09:34:27Z","title":"SGLang: Efficient Execution of Structured Language Model Programs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07104","snapshot_observed_at":"2026-08-06T15:24:17.854387Z","title":"H., Cao, S., Kozyrakis, C., Stoica, I., Gonzalez, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.854387Z"},"links":{"cited_paper":"/paper/2312.07104","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:51757add0bb91c6192d84fa8cc78521a5b9ec8624bb0f53420059e69e97fee47","observation_id":"ebc71b69-400d-4859-bc55-e642d9b44877","resolution":{"observed_at":"2026-08-06T15:24:17.854387Z","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":"2026-08-06T15:24:18.296921Z","title":"Torchao: Low-bit arm cpu and metal ker- nels for linear and embedding ops","venue":null,"work_id":"042d762d-2e5c-4ee5-ac51-d718c8cbeb9e","year":2025},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:17.440761Z"},"links":{"citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:a1e1a23a794a34a2d4f26b638911287fe809023c9e51e9bf2ec86e840c858f50","observation_id":"9b71d901-8d56-41e2-bc1f-11bebb7fc930","resolution":{"observed_at":"2026-08-06T15:24:18.383878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06674","last_updated":"2023-12-07T19:40:50Z","snapshot_observed_at":"2026-07-06T17:00:00.321552Z","submitted_at":"2023-12-07T19:40:50Z","title":"Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06674","snapshot_observed_at":"2026-08-06T15:24:16.568903Z","title":"Llama guard: Llm-based input-output safeguard for human-ai conversations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.568903Z"},"links":{"cited_paper":"/paper/2312.06674","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:42fe68a79eca4fcf660070136a508d51881baa1835e8c982ec8f0f06b3027008","observation_id":"f1902be7-ee35-4736-88f9-ce89c36aae13","resolution":{"observed_at":"2026-08-06T15:24:16.568903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.07339","last_updated":"2022-11-10T18:14:31Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-08-15T17:08:50Z","title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.07339","snapshot_observed_at":"2026-08-06T15:24:16.077657Z","title":"Llm.int8(): 8-bit matrix multiplication for transformers at scale","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.077657Z"},"links":{"cited_paper":"/paper/2208.07339","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:ae88d5d3344c9d13eb71d0d1aafd1e0f0f78776a36e8720567015198ae5e31fe","observation_id":"c3f3c4be-15f1-46e3-a09e-4fea89dbad4a","resolution":{"observed_at":"2026-08-06T15:24:16.077657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T15:24:16.299446Z","title":"Deepseek-r1: In- centivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.299446Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:ba56d821c806c8abb84c2475466258a9af6b0b154b5e037a165306ae8729e5f3","observation_id":"9d3bdde2-b7e0-4445-bd84-c6aaa43f1abe","resolution":{"observed_at":"2026-08-06T15:24:16.299446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-06T15:24:16.203023Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T15:24:16.203023Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2507.16099"},"observation_digest":"sha256:46237f5f069c816e9fa8753faf099bb937fdcf1986632d6ec306857dc645f8a9","observation_id":"3ac0f1cc-33d8-4b9f-a5b7-b6e9d9b4f38c","resolution":{"observed_at":"2026-08-06T15:24:16.203023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.16099","last_updated":"2025-07-21T22:50:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:16:06.787418Z","submitted_at":"2025-07-21T22:50:12Z","title":"TorchAO: PyTorch-Native Training-to-Serving Model Optimization"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":15},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 8 inbound Pith citation observations for arXiv:2507.16099."}