{"as_of":"2026-08-14T17:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d097fc7a329239d364ea89f316d4e4856e73da77327fb63859fcc4483b401c2f","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T04:49:48.064177Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.22432/citation-record","integrity":"/paper/2607.22432/integrity","json":"/paper/2607.22432/citation-record.json","paper":"/paper/2607.22432"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:49:44.215595Z","title":"1965.Handbook of mathemat- ical functions: with formulas, graphs, and mathematical tables","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.215595Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:0a66f716b537c474b6812a3c96e389caf72b712b790261777a1ff96bb64aaaa4","observation_id":"e1f1f516-a82f-4f2a-b62a-a9f751f2deb3","resolution":{"observed_at":"2026-08-01T04:49:44.215595Z","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":"2026-08-01T04:49:44.258807Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.258807Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:da5c18674b422395fdfe229405ae82dd4a0cda0625e35964e36a59f1c240d7ca","observation_id":"3a59b7e8-5087-4e0e-86fa-d56a9aefb486","resolution":{"observed_at":"2026-08-01T04:49:44.258807Z","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":"2026-08-01T04:49:44.299550Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.299550Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:cc584c5839c56c989192e57a22b8ed6df6f53ce49b22d6dff31970fe7cd8cef5","observation_id":"7ee2d9f3-4c78-4f9a-bb29-f69c334e5537","resolution":{"observed_at":"2026-08-01T04:49:44.299550Z","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":"2026-08-01T04:49:44.362626Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.362626Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:04ebb0e5c1f0f3a97abdcee7bdd151cfb6f9345eadb3e0e04ba9dac9c8e66467","observation_id":"dda207a5-880d-4c33-ad97-338fdbe607b8","resolution":{"observed_at":"2026-08-01T04:49:44.362626Z","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":"2026-08-01T04:49:44.443872Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.443872Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:01d992c96b0285a9db0b0a74d67afc168a20d9a62776c5f2bbe55e44fcb11900","observation_id":"18cf8c5a-bcfb-4970-a0f3-44bf9c2b9a1f","resolution":{"observed_at":"2026-08-01T04:49:44.443872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01698","last_updated":"2025-05-15T02:46:53Z","snapshot_observed_at":"2026-08-12T23:51:13.217643Z","submitted_at":"2024-06-03T18:00:50Z","title":"Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01698","snapshot_observed_at":"2026-08-01T04:49:44.520794Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.520794Z"},"links":{"cited_paper":"/paper/2406.01698","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:6d15794245e4eeb108c82312949323d9e1733d88fa7221a50e11195e96ab74f3","observation_id":"c51afc1d-95f4-4a48-b5dd-c68499f93faf","resolution":{"observed_at":"2026-08-01T04:49:44.520794Z","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":"2026-08-01T04:49:44.602571Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.602571Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:e426658d0bc6c780ac437c3c920fc05d59ab414cdbcebf25228a168fc3ceb986","observation_id":"d1a9d1d6-d703-4784-a405-0ec82ae03443","resolution":{"observed_at":"2026-08-01T04:49:44.602571Z","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":"2026-08-01T04:49:44.737092Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.737092Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:88fec23c49272d8508853efe09c5031c0dd4294438ab784de33055a5724b84f4","observation_id":"d557960a-71a8-4c88-8557-21fbcd4a25d8","resolution":{"observed_at":"2026-08-01T04:49:44.737092Z","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":"2026-08-01T04:49:45.024627Z","title":"Conte, Mary Ann Hirsch, and W-MW Hwu","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.024627Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:bcfded1b8f3875e0766041a8cc184dfdacb6300d94dfe10a889212e4c750931e","observation_id":"bfc14fdd-c14e-4cc1-94d0-aba39fb16fc2","resolution":{"observed_at":"2026-08-01T04:49:45.024627Z","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":"2026-08-01T04:49:45.132086Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.132086Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:14671694f9cb61236939649e8ec333a5a33d4810028cd17c3d963af43b6a88fd","observation_id":"55f3d9f9-69d3-47e6-bf8d-f59dfce5aafc","resolution":{"observed_at":"2026-08-01T04:49:45.132086Z","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":"2026-08-01T04:49:45.249486Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.249486Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8175005ccd4180a0cd7dfe014a1929cb4a3358fcd4bb1eba25d875bb4929fe16","observation_id":"c67141d4-307c-40d7-9564-c11ba1e1d195","resolution":{"observed_at":"2026-08-01T04:49:45.249486Z","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":"2026-08-01T04:49:45.370936Z","title":"NVIDIA CUTLASS.https://github.com/NVIDIA/ cutlass","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.370936Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:fd0a5c473b43fba9b0d0a60aee17018f9e246f7f29ef9a802832addd71a6ec51","observation_id":"fd8163c8-16a9-424c-8bd7-b28fcf9af919","resolution":{"observed_at":"2026-08-01T04:49:45.370936Z","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":"2026-08-01T04:49:45.510101Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.510101Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8e747bc86ddf4a271e5561e20e7eb3299b136ab1f34b26ed0d82409eede15874","observation_id":"ffc6ac63-3bf0-43a3-8236-357ff176a2af","resolution":{"observed_at":"2026-08-01T04:49:45.510101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12487","last_updated":"2024-12-17T02:44:35Z","snapshot_observed_at":"2026-08-12T04:03:51.433821Z","submitted_at":"2024-12-17T02:44:35Z","title":"Echo: Simulating Distributed Training At Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12487","snapshot_observed_at":"2026-08-01T04:49:45.597984Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.597984Z"},"links":{"cited_paper":"/paper/2412.12487","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8bcfc11780c43f4c6bb6a3d427005586f7bd80b184f06e8b4065a8ae028eb86d","observation_id":"d50c1229-7fe5-4da3-912f-165a901a2dd1","resolution":{"observed_at":"2026-08-01T04:49:45.597984Z","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":"2026-08-01T04:49:45.655415Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.655415Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:6963326bc0e615f965537f63d69772edcb5867191693466c73221925fb411562","observation_id":"e0837a5f-794a-4d82-9fad-4618997264ac","resolution":{"observed_at":"2026-08-01T04:49:45.655415Z","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":"2026-08-01T04:49:45.727438Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.727438Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:f4741e173ba1c3e9b3d7a93945ce469a038be15e712a16a699333fe2bd7a0a75","observation_id":"0595c525-cf75-494a-adae-fe52dc0ebfa8","resolution":{"observed_at":"2026-08-01T04:49:45.727438Z","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":"2026-08-01T04:49:45.803534Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.803534Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:cca9758c1807667afeec00e1845284efa492e04cb8ac516ffc5327a6dfad8e23","observation_id":"300a6b26-803d-4462-914c-74f6f732f8c5","resolution":{"observed_at":"2026-08-01T04:49:45.803534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.21661","last_updated":"2025-05-27T18:38:15Z","snapshot_observed_at":"2026-08-09T18:18:04.503238Z","submitted_at":"2025-05-27T18:38:15Z","title":"KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.21661","snapshot_observed_at":"2026-08-01T04:49:45.955512Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.955512Z"},"links":{"cited_paper":"/paper/2505.21661","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:01241e38fb2c292d4df8883ab2f0aff0efefcaa59a832ec4bd449aa966390f6b","observation_id":"d12f834c-5d30-49cb-94e8-e4e59f0174e5","resolution":{"observed_at":"2026-08-01T04:49:45.955512Z","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":"2026-08-01T04:49:46.077248Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.077248Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ee84f203c362cebaedbbbb8ff1630a8a3249a48f373bba329db4f39dd175d337","observation_id":"d2a0449d-ecf0-4093-a9c2-ecdc02bed5c6","resolution":{"observed_at":"2026-08-01T04:49:46.077248Z","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":"2026-08-01T04:49:46.223948Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.223948Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:a6c1a03725f530784c9461fe97d7aa226a88e022f74709c4b28b9eb47b20687d","observation_id":"d85dbf43-86fc-4060-a276-5d9bdf8cd70f","resolution":{"observed_at":"2026-08-01T04:49:46.223948Z","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":"2026-08-01T04:49:46.461674Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.461674Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:719b82d064ca28b85862742305fb1ca77c38ba17ac745997848b1ee460f80cc6","observation_id":"74ea5052-c78e-4872-b8af-0f7725e880b5","resolution":{"observed_at":"2026-08-01T04:49:46.461674Z","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":"2026-08-01T04:49:46.634983Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.634983Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:5664581054dd714ae31733c41cd0398e75ba857d9be58eddf1dd942df15d882d","observation_id":"149e7818-16ff-43a8-8bab-b200b2982bc7","resolution":{"observed_at":"2026-08-01T04:49:46.634983Z","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":"2026-08-01T04:49:46.778137Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.778137Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:0fbcdf53fe966225d5e5835eacae1c4ed7382f66cba408a2b6708b339c01ff49","observation_id":"fd331c8e-ee6a-47ef-8de1-2e1aafe2914a","resolution":{"observed_at":"2026-08-01T04:49:46.778137Z","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":"2026-08-01T04:49:46.914794Z","title":null,"venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:46.914794Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:1925ea5c01b92ba7be8951089b705448b8891cc85bf257b18b1ddefdc4f4d39d","observation_id":"b3b84741-77eb-4b65-bc1a-364efb903785","resolution":{"observed_at":"2026-08-01T04:49:46.914794Z","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":"2026-08-01T04:49:47.068440Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.068440Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:a77ac9768881d0cd24610fed2b614c6c0514001105c2ff43dc2e41f4bb00eab1","observation_id":"c94beaf0-562f-4eaa-ae9a-5abf492e1264","resolution":{"observed_at":"2026-08-01T04:49:47.068440Z","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":"2026-08-01T04:49:47.227508Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.227508Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:0b6d173c1f2c91b66a95c83994eec165c6576be4053c2ca6b38116239103df2a","observation_id":"f0434be7-f730-4982-ba44-c72d495c6546","resolution":{"observed_at":"2026-08-01T04:49:47.227508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.09307","last_updated":"2025-04-12T18:43:24Z","snapshot_observed_at":"2026-08-07T16:06:16.284754Z","submitted_at":"2025-04-12T18:43:24Z","title":"Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.09307","snapshot_observed_at":"2026-08-01T04:49:47.386842Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.386842Z"},"links":{"cited_paper":"/paper/2504.09307","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:7235677db053eb3b4fa7ee9e3e2d69fa2d1638e90ad247f8aebb91f551a850ad","observation_id":"bccfc25c-ef62-4f7b-8317-e62d3538d54a","resolution":{"observed_at":"2026-08-01T04:49:47.386842Z","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":"2026-08-01T04:49:47.590141Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.590141Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:4b658894115046a85e69acdb27df758308af5971fbb7d7e37baa76f4789bc200","observation_id":"80cb23ac-9663-4c74-8124-d1305fe87af0","resolution":{"observed_at":"2026-08-01T04:49:47.590141Z","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":"2026-08-01T04:49:47.796212Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.796212Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ac4a873ce13f465385ce199e07dfb9f7a191f654d21495820d50460043b0bc58","observation_id":"24563647-009a-41be-9d4b-41c29d1fed00","resolution":{"observed_at":"2026-08-01T04:49:47.796212Z","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":"2026-08-01T04:49:47.935017Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.935017Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:af3f8b4324a2e3caceafc03029fcdc0742955f1fefeebb099e186f8def34b039","observation_id":"b25e987b-fa1a-4923-b1f4-b151a50f025c","resolution":{"observed_at":"2026-08-01T04:49:47.935017Z","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":"2026-08-01T04:49:47.983310Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.983310Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:b1ba44738c27ce9f01d7f9a2a49bb85836c0641b7a3358b5c2b1ac7e278b1a5c","observation_id":"0faaf6de-ecba-41ba-8df8-3a5e53cd407d","resolution":{"observed_at":"2026-08-01T04:49:47.983310Z","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":"2026-08-01T04:49:47.986080Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.986080Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ad414c98578ef3af275454596041541cf5651f0d29cd3d9b347af0fa475721c2","observation_id":"f04f561c-773d-492b-b0ec-12140976b774","resolution":{"observed_at":"2026-08-01T04:49:47.986080Z","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":"2026-08-01T04:49:47.988985Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.988985Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8e0f48d67271e2d9dd635b9bd7d3ac76c86125b6fa6b8ac0f0c7bb5daa78adec","observation_id":"1c9b10b6-9ea5-4f21-98cf-8c8f43e3b021","resolution":{"observed_at":"2026-08-01T04:49:47.988985Z","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":"2026-08-01T04:49:47.991469Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.991469Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:fa5959dc9a5df5580c48dbb89142246c8198f3a13c17023975e1a5529b34017c","observation_id":"fa08f017-4e44-4958-9f1b-2cef4f6c5925","resolution":{"observed_at":"2026-08-01T04:49:47.991469Z","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":"2026-08-01T04:49:47.993515Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.993515Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:354aca060b263da9fe3f76ce6c1530f2d639373ea6d7f398ec31cde395faf703","observation_id":"5c243228-bcb1-4a35-bcb7-6a6980c5e2d3","resolution":{"observed_at":"2026-08-01T04:49:47.993515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.00904","last_updated":"2025-07-29T03:08:31Z","snapshot_observed_at":"2026-08-14T03:49:45.690346Z","submitted_at":"2025-07-29T03:08:31Z","title":"Forecasting LLM Inference Performance via Hardware-Agnostic Analytical Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.00904","snapshot_observed_at":"2026-08-01T04:49:47.995962Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.995962Z"},"links":{"cited_paper":"/paper/2508.00904","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:e57508dfe64c835ff065248008e7f4690c3b9927e8b5db02acfa9b2b7efe60e0","observation_id":"698ecdc0-510d-4e08-b85b-71d6fdff1ccc","resolution":{"observed_at":"2026-08-01T04:49:47.995962Z","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":"2026-08-01T04:49:47.999075Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:47.999075Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:d2319b37fe397377aa790a20ee44ece8e89493f6bf31408eeaf5e7ec4c486582","observation_id":"722a5edc-810a-49b7-9517-567a3a8bb7b3","resolution":{"observed_at":"2026-08-01T04:49:47.999075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20399","last_updated":"2024-10-27T10:07:16Z","snapshot_observed_at":"2026-08-12T22:14:26.013823Z","submitted_at":"2024-10-27T10:07:16Z","title":"ThunderKittens: Simple, Fast, and Adorable AI Kernels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20399","snapshot_observed_at":"2026-08-01T04:49:48.001679Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.001679Z"},"links":{"cited_paper":"/paper/2410.20399","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8563795efaaebf560b74c77ce381f36804537c511c083531aaa0473e7b2d39c1","observation_id":"7e856ecd-f898-4804-9c14-5d9529cf6d1f","resolution":{"observed_at":"2026-08-01T04:49:48.001679Z","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":"2026-08-01T04:49:48.004494Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.004494Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:53b62f9e2520b88ce4704ce2c1c4546b20765c658730de7b8fc8be369d0517f0","observation_id":"c91c02d2-521f-4767-9e5a-a0a3a038c841","resolution":{"observed_at":"2026-08-01T04:49:48.004494Z","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":"2026-08-01T04:49:48.007043Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.007043Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:952a29766b3fe2eca757c1f79af3568af671abb45eca9f024e9a5d995e8781a2","observation_id":"6625720a-ca3c-4d1c-ba45-510403e75980","resolution":{"observed_at":"2026-08-01T04:49:48.007043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09275","last_updated":"2025-06-10T22:25:29Z","snapshot_observed_at":"2026-08-14T16:42:28.054150Z","submitted_at":"2025-06-10T22:25:29Z","title":"A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09275","snapshot_observed_at":"2026-08-01T04:49:48.009790Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.009790Z"},"links":{"cited_paper":"/paper/2506.09275","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:874ed4faa88bcd7670a17bf13a07106d03b1806cd9c46b2f394caf587a82cd75","observation_id":"647f283d-79ea-4de8-bb0a-aa62ba6552c5","resolution":{"observed_at":"2026-08-01T04:49:48.009790Z","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":"2026-08-01T04:49:48.012339Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.012339Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:1b77e1ffdd60819b38fee9a81f577e6742f92271568c9d99eb3840ad8937a866","observation_id":"f407f05b-1159-4770-9b6b-2a35bb8b328d","resolution":{"observed_at":"2026-08-01T04:49:48.012339Z","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":"2026-08-01T04:49:48.015197Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.015197Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:1feec7580e7a7a287e9747e9cacbbb8caaba21ca46fd68b4537b969363ff4a6c","observation_id":"1f73252f-663b-43b8-a480-8a06d814588e","resolution":{"observed_at":"2026-08-01T04:49:48.015197Z","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":"2026-08-01T04:49:48.017904Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.017904Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:c3cce140fbfc33cdb6e054eb4a92360407e143cf12e6fbc3b921673901d71abf","observation_id":"ebedb57b-c5df-4fe3-bac4-c5c034b471b6","resolution":{"observed_at":"2026-08-01T04:49:48.017904Z","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":"2026-08-01T04:49:48.020499Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.020499Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:9b506f4ad3fe6552a1debbf24d33fd4139af562026eae25bae190a8a5e364f97","observation_id":"8a195bd6-a315-41be-9249-f42054c6f1c8","resolution":{"observed_at":"2026-08-01T04:49:48.020499Z","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":"2026-08-01T04:49:48.022912Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.022912Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:3f010134aace140c8456d47a5e19df4cd2e94c8352a4c39b5f3ce39cc81f5f71","observation_id":"8c42e4a0-1000-4740-8b16-42d3d2a54980","resolution":{"observed_at":"2026-08-01T04:49:48.022912Z","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":"2026-08-01T04:49:48.025489Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.025489Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:f3570757595af088410efe600f89408e52ccf5f6349764c06222e95de0fc18f9","observation_id":"fd259839-c340-4844-a3aa-b5fc96d9b57a","resolution":{"observed_at":"2026-08-01T04:49:48.025489Z","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":"2026-08-01T04:49:48.030883Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.030883Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:df88a61aaefa174bbc01795eaad93040a47b6d544ae0c7799c532b1761c80fac","observation_id":"edd8fc3b-5ee3-4f14-9c83-b83765df0f8b","resolution":{"observed_at":"2026-08-01T04:49:48.030883Z","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":"2026-08-01T04:49:48.033384Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.033384Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:82ab79d22dc9226202a768161da5c37d4982751ae69858a2c2f8ac5e6d234bfb","observation_id":"e052c5ff-0b0a-4a45-83f7-4cb9cb65e397","resolution":{"observed_at":"2026-08-01T04:49:48.033384Z","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":"2026-08-01T04:49:48.035888Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.035888Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:5c12036ce649a8a5e6351cc0600a7e4da2a9e69ae0accaabeced909569833358","observation_id":"ac0499a4-0676-4bbf-be6d-47de73a9d8f6","resolution":{"observed_at":"2026-08-01T04:49:48.035888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20886","last_updated":"2025-06-25T23:36:44Z","snapshot_observed_at":"2026-08-14T01:50:38.257706Z","submitted_at":"2025-06-25T23:36:44Z","title":"Omniwise: Predicting GPU Kernels Performance with LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20886","snapshot_observed_at":"2026-08-01T04:49:48.027979Z","title":"arXiv preprint arXiv:2506.20886(2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.027979Z"},"links":{"cited_paper":"/paper/2506.20886","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ffb7747fcb5a4b26e9d525dc5f59c2b7329e32c8bf3d27033eac8bfdaeaeb0b6","observation_id":"8c11b90e-a308-4ac7-a717-a16ea97d961c","resolution":{"observed_at":"2026-08-01T04:49:48.027979Z","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":"2026-08-01T04:49:48.040736Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.040736Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:1ce3866b4e727f4e86aa1c62b901b896d7406a2e5a8a2e047d2e1af651dd2c7d","observation_id":"c2bb410a-58fc-48f9-8bb6-f143419263de","resolution":{"observed_at":"2026-08-01T04:49:48.040736Z","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":"2026-08-01T04:49:48.046673Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.046673Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:8007dbbf771c9ec2d1d9295c3fc64f717165e8b4d9e4ac75cc6a9f42fc2e1c8a","observation_id":"77499c9b-e393-44b0-8dd4-8f8001bc70d6","resolution":{"observed_at":"2026-08-01T04:49:48.046673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.14910","last_updated":"2026-04-28T03:10:59Z","snapshot_observed_at":"2026-08-11T00:10:38.786250Z","submitted_at":"2026-01-21T11:47:56Z","title":"PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.14910","snapshot_observed_at":"2026-08-01T04:49:48.049219Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.049219Z"},"links":{"cited_paper":"/paper/2601.14910","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:00af1cbac8d89f8e1f646973e9a9c6285876ec3ad2347ef8eaa8027de1060766","observation_id":"32a5c1cc-b5c7-4120-8d5a-840ee8919785","resolution":{"observed_at":"2026-08-01T04:49:48.049219Z","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":"2026-08-01T04:49:48.038209Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.038209Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ee1d15ac93674997c741e4d5e320cf83de0543b6f59298b49e41e4d5598e2ecb","observation_id":"25cb2a2e-7264-4ef6-918e-33c95b40666f","resolution":{"observed_at":"2026-08-01T04:49:48.038209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20313","last_updated":"2025-04-03T10:23:19Z","snapshot_observed_at":"2026-08-14T16:59:34.681714Z","submitted_at":"2025-03-26T08:25:12Z","title":"TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.20313","snapshot_observed_at":"2026-08-01T04:49:48.054315Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.054315Z"},"links":{"cited_paper":"/paper/2503.20313","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:bd54be763da599bfcd4ddaf458969d6b0a5871756af6a5ba0d3e5f3a09fa8036","observation_id":"73245f55-c103-4996-a310-f472fc6c2d50","resolution":{"observed_at":"2026-08-01T04:49:48.054315Z","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":"2026-08-01T04:49:48.056947Z","title":"2024.{DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.056947Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:d2b9727fb4c11e12353a788282c07c1729a27ba4ddca6b0de5db7d0c35f6f57a","observation_id":"b8b74589-efdf-4f59-a824-52e1cee0201a","resolution":{"observed_at":"2026-08-01T04:49:48.056947Z","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":"2026-08-01T04:49:48.059597Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.059597Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:aaee095d1faf10affd47ec91addab70becb7dddd117910eb103ce64ae07dc459","observation_id":"991ad23c-b236-40b0-ae9d-97e8f0f01aa2","resolution":{"observed_at":"2026-08-01T04:49:48.059597Z","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":"2026-08-01T04:49:48.061908Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.061908Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:ecbc8f83ef07262943318a8b224cd8ea89ba528cbf40977b9ddc063b6c3f5460","observation_id":"84c4bf34-1f31-4d37-add8-840782c1a6ca","resolution":{"observed_at":"2026-08-01T04:49:48.061908Z","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":"2026-08-01T04:49:48.051945Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.051945Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:0cc40d383230a02908cfa70dd1c8dfcdcec342a8740059424fbf7b7d2cebed5c","observation_id":"b7179cf6-8f2b-4ad4-b6e0-352b325ef2e3","resolution":{"observed_at":"2026-08-01T04:49:48.051945Z","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":"2026-08-01T04:49:48.064177Z","title":"In2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.064177Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:f206334491b7daa2fc761de2811dc076e0718deda8d2e797a63c897c2c8086c9","observation_id":"f70c2b9c-8940-4104-85df-ef115fa13bfd","resolution":{"observed_at":"2026-08-01T04:49:48.064177Z","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":"2026-08-01T04:49:45.884715Z","title":"In2021 USENIX Annual Technical Conference (USENIX ATC 21)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:45.884715Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:9e62c8155347d8d5c641f6d8b8581a731cf45dd35aceb276a67d89bf516ffb66","observation_id":"81081b10-52e0-4121-9dfa-9204a35ae503","resolution":{"observed_at":"2026-08-01T04:49:45.884715Z","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":"2026-08-01T04:49:44.667397Z","title":"InProceedings of the 50th Annual International Symposium on Computer Architecture","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.667397Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:76b2954f3f84232c300ae06b415ec506d8ad01c7b3b02de2eb721ff702f2734e","observation_id":"f5565b6d-8cbf-4de6-8ff6-8a9f00d015a5","resolution":{"observed_at":"2026-08-01T04:49:44.667397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16363","last_updated":"2024-05-01T20:42:28Z","snapshot_observed_at":"2026-08-13T04:10:18.599255Z","submitted_at":"2024-02-26T07:33:05Z","title":"LLM Inference Unveiled: Survey and Roofline Model Insights","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16363","snapshot_observed_at":"2026-08-01T04:49:48.043261Z","title":"arXiv preprint arXiv:2402.16363(2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:48.043261Z"},"links":{"cited_paper":"/paper/2402.16363","citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:d4b0a6068833a58bf46d4e341e9bb857cb0e63dff0216557e1355a4e3efd033e","observation_id":"7af833aa-6c1b-444c-9024-198ac481d754","resolution":{"observed_at":"2026-08-01T04:49:48.043261Z","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":"2026-08-01T04:49:44.887914Z","title":"InUSENIX Annual Technical Conference.https: //api.semanticscholar.org/CorpusID:280049543","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T04:49:44.887914Z"},"links":{"citing_paper":"/paper/2607.22432"},"observation_digest":"sha256:6a80a51b41d2bb3fddd62781638f0e6870d7eebebde9e656097305cbae702839","observation_id":"6d08b09d-10c0-47d4-bd49-1c0ed4f87d73","resolution":{"observed_at":"2026-08-01T04:49:44.887914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.22432","last_updated":"2026-07-24T15:50:23Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-06T21:57:48.528474Z","submitted_at":"2026-07-24T15:50:23Z","title":"TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":65,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":65},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2607.22432."}