{"as_of":"2026-08-08T02:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffee4639baedbd0d542c93eb8b0893e072244ec3574f0f4dee29646cd4ed125a","coverage":[{"denominator":61,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:37:18.710415Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.01880/citation-record","integrity":"/paper/2506.01880/integrity","json":"/paper/2506.01880/citation-record.json","paper":"/paper/2506.01880"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:14.564529Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.564529Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:73b6f1c64e4d3923d9d057cfca1e073cf4cac79babf03934a903f8938374f7de","observation_id":"d9e0bcef-02d8-4d62-896b-cf77f205f09d","resolution":{"observed_at":"2026-08-07T11:37:14.564529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08743","last_updated":"2020-01-23T20:42:47Z","snapshot_observed_at":"2026-07-06T08:52:26.716343Z","submitted_at":"2020-01-23T20:42:47Z","title":"Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08743","snapshot_observed_at":"2026-08-07T11:37:14.626612Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.626612Z"},"links":{"cited_paper":"/paper/2001.08743","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:88f9a4964b39c2aee19ab3d98c817ddd60ca3be79b3f3fc5da952bbe780fc1d1","observation_id":"148c244b-3260-431b-9627-81ab7e8b563b","resolution":{"observed_at":"2026-08-07T11:37:14.626612Z","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":"2026-08-07T11:37:23.027247Z","title":null,"venue":null,"work_id":"99425e40-f98f-47a0-b32f-b4b168bfdc24","year":2015},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.667898Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:b2307e1fed0954c28f116f742ee523c82665381fbb37c48c0fad4bc4026b1526","observation_id":"52a21e26-4491-4c69-bfdf-6e914260ce9b","resolution":{"observed_at":"2026-08-07T11:37:23.031618Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1111.6756","last_updated":"2011-11-29T10:40:44Z","snapshot_observed_at":"2026-07-06T02:38:25.549929Z","submitted_at":"2011-11-29T10:40:44Z","title":"The Potential of Synergistic Static, Dynamic and Speculative Loop Nest Optimizations for Automatic Parallelization","version":1},"cited_work":{"arxiv_id":"1111.6756","doi":null,"metadata_source":"pith","pith_arxiv_id":"1111.6756","snapshot_observed_at":"2026-08-07T11:37:21.262515Z","title":"The Potential of Synergistic Static, Dynamic and Speculative Loop Nest Optimizations for Automatic Parallelization","venue":"cs.DC","work_id":"c67b1765-51a2-43c2-80b4-98fcc4be22c5","year":2011},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.759246Z"},"links":{"cited_paper":"/paper/1111.6756","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:d3b32424fa3027181616ebf6f02c8aeedbfaf838f2bc2ad6957a16d5c3d7a441","observation_id":"9c3bd9bc-6907-4b6a-9cfb-ee14709e4cbf","resolution":{"observed_at":"2026-08-07T11:37:21.334814Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:23.011277Z","title":null,"venue":null,"work_id":"a0543962-0349-471a-988f-bd6c903f2ae8","year":2015},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.838331Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:97d4013c7f3a7b9e82edc5d5ff1380208c00d9e8f7a6483d87b45c768c605b33","observation_id":"baf53c50-87f4-4a3f-8d2d-3b7abb1db5a6","resolution":{"observed_at":"2026-08-07T11:37:23.016231Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1302.5586","last_updated":"2013-02-22T13:43:12Z","snapshot_observed_at":"2026-07-06T03:07:03.705073Z","submitted_at":"2013-02-22T13:43:12Z","title":"PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs","version":1},"cited_work":{"arxiv_id":"1302.5586","doi":null,"metadata_source":"pith","pith_arxiv_id":"1302.5586","snapshot_observed_at":"2026-08-07T11:37:21.081186Z","title":"PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs","venue":"cs.PL","work_id":"6bbd6bf0-7189-4960-86cb-23fafd234708","year":2013},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.912755Z"},"links":{"cited_paper":"/paper/1302.5586","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:2821e5ad5f20eb5514a8c168bb96af97519f2379afdee4adbe6ee52cd58e5694","observation_id":"325f30e6-f067-4a7d-be15-b02cc175af94","resolution":{"observed_at":"2026-08-07T11:37:21.161997Z","resolver_source":"local_arxiv","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":"2005.04091","last_updated":"2020-05-07T07:27:08Z","snapshot_observed_at":"2026-07-31T01:00:19.232171Z","submitted_at":"2020-05-07T07:27:08Z","title":"TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning","version":1},"cited_work":{"arxiv_id":"2005.04091","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.04091","snapshot_observed_at":"2026-08-07T11:37:20.913837Z","title":"TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning","venue":"cs.DC","work_id":"b8ec328e-75c1-4397-911f-d23fe3642299","year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:14.994199Z"},"links":{"cited_paper":"/paper/2005.04091","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:7ceaa2a8a21958da615fb1bf8f9d7904ebd757dc6b178638d388477cd78f0230","observation_id":"c3ec9938-1862-471c-92c4-2030cc6b1711","resolution":{"observed_at":"2026-08-07T11:37:20.952894Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.995705Z","title":null,"venue":null,"work_id":"9a93fe87-d6cf-4e22-a5ec-be2908622a6e","year":null},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.102667Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:328cfa4f0be4423fc7236a6d65820731bba1266992f48630c8c0813570f74e1b","observation_id":"8d771028-4873-453b-9ff8-25925c2a3ccd","resolution":{"observed_at":"2026-08-07T11:37:23.000303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.824880Z","title":null,"venue":null,"work_id":"f736f04a-eac1-4b57-ab36-10f6c3475491","year":2019},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.271556Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:d0863b8ec4a927a4ebbae9da0ae16a1759ac97aa562918ae266f835c1f22c8e1","observation_id":"2340a3a1-f64b-4c0b-b82c-6ce0b8028725","resolution":{"observed_at":"2026-08-07T11:37:22.846646Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1804.10694","last_updated":"2018-12-20T16:25:40Z","snapshot_observed_at":"2026-08-03T08:07:15.196848Z","submitted_at":"2018-04-27T21:28:44Z","title":"Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.10694","snapshot_observed_at":"2026-08-07T11:37:15.316670Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.316670Z"},"links":{"cited_paper":"/paper/1804.10694","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:61fe8188768c1fe4e5e2b4be49df2099fdb1d79cf92523fd6e639050868ea71d","observation_id":"4f0f6aeb-3549-4a67-8860-7544bf9682e8","resolution":{"observed_at":"2026-08-07T11:37:15.316670Z","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":"2026-08-07T11:37:22.774085Z","title":null,"venue":null,"work_id":"80b4e38a-6597-4eae-a0fc-7827c65a067d","year":2008},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.406633Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:0894bd531212bca5a59cde986faed60bc40e66d8dfd58153c46dccdb55b2f711","observation_id":"1ca3ee93-e2d0-4317-9903-ed6bde177b3b","resolution":{"observed_at":"2026-08-07T11:37:22.784877Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.759256Z","title":null,"venue":null,"work_id":"906f257b-2486-4f45-be9e-1bda6f8b0d70","year":2008},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.447697Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:7f0c0eb597eb209f586b42dffa20aac3a4905f8139ce390d7c438350259f31b4","observation_id":"f22fae0d-0e6c-4466-8d05-7bf965eabbce","resolution":{"observed_at":"2026-08-07T11:37:22.763832Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.743799Z","title":"Ramanujam, and P","venue":null,"work_id":"2eb591fc-9cee-4ae4-8b1d-a91286352720","year":2008},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.524617Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:de13658896b1ac7e5171a5c7f88cb6638632168a894b9fa66d979aa258e6656b","observation_id":"29c48f63-10cc-47d1-a14f-4dae85210f96","resolution":{"observed_at":"2026-08-07T11:37:22.748127Z","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":"2104.13732","last_updated":"2021-04-29T08:04:04Z","snapshot_observed_at":"2026-07-06T11:04:24.086263Z","submitted_at":"2021-04-28T12:41:52Z","title":"A Reinforcement Learning Environment for Polyhedral Optimizations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.13732","snapshot_observed_at":"2026-08-07T11:37:15.577818Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.577818Z"},"links":{"cited_paper":"/paper/2104.13732","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:7467320e7084245fa9f284a1d3a06123bcfb37959853efd7820a7ff417c4abeb","observation_id":"e755675e-c129-468f-9162-fb44d386a1cb","resolution":{"observed_at":"2026-08-07T11:37:15.577818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14491","last_updated":"2022-01-31T07:20:20Z","snapshot_observed_at":"2026-07-06T11:14:04.454155Z","submitted_at":"2021-05-30T10:17:58Z","title":"How Attentive are Graph Attention Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14491","snapshot_observed_at":"2026-08-07T11:37:15.690692Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.690692Z"},"links":{"cited_paper":"/paper/2105.14491","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:02ab4b792318e1758c088e8d518bb28b6f7b73f1c986cc00c684f3ac2a83d102","observation_id":"d23d964d-0954-4a11-aa60-f76933c4d75d","resolution":{"observed_at":"2026-08-07T11:37:15.690692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04799","last_updated":"2018-10-05T18:47:38Z","snapshot_observed_at":"2026-08-07T20:53:31.333665Z","submitted_at":"2018-02-12T20:49:34Z","title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04799","snapshot_observed_at":"2026-08-07T11:37:15.742334Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.742334Z"},"links":{"cited_paper":"/paper/1802.04799","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:fd43f00dbfba79997ecff7529979711e178f2ee93df8381481fef74acf0dc2a6","observation_id":"e9d2cc3c-4d23-4b4c-9301-fd1bdfc6c4c5","resolution":{"observed_at":"2026-08-07T11:37:15.742334Z","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":"2026-08-07T11:37:22.729039Z","title":null,"venue":null,"work_id":"06bcc572-04b9-456a-a4eb-ce156ab87c73","year":null},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.811524Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:a0f2fcdcd1fd8a9c582e210d69155bcd83e255fe8b5b9f5a6bf2c0056de868e4","observation_id":"611d3cad-8814-4f65-963a-a4bd9e578320","resolution":{"observed_at":"2026-08-07T11:37:22.733476Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.714188Z","title":null,"venue":null,"work_id":"edfa9736-6b4a-4a5b-a352-af4d8de202dd","year":2022},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.920300Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:5c1818a41a11e021adb07d6f61612fcb57189b2db801d897edbfed0891319053","observation_id":"4abf6fa5-832d-418e-9dd7-0ab7d228daec","resolution":{"observed_at":"2026-08-07T11:37:22.718775Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11265-005-4937-3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:19.059656Z","title":null,"venue":null,"work_id":"43664783-5e0a-4a01-9cf4-3f4205223ac4","year":2005},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.979036Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:d8c7ffcae46d1db83caf59665291fe3274993950f444db6a6e38c723a7a81c45","observation_id":"257c224a-9e5e-4bee-8bfb-7f43f7fba2bd","resolution":{"observed_at":"2026-08-07T11:37:19.157721Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":"5364.55406","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:20.670392Z","title":"Feautrier","venue":null,"work_id":"46f31a4f-6a79-4ca7-a4ff-120a49158e0c","year":1988},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.014206Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:dbd0e095f7cbcacd32fce41a2efff92573a5067c646fc0721263fe5cf57aab85","observation_id":"ad3330d7-4a32-4f05-94fc-0db2e6495881","resolution":{"observed_at":"2026-08-07T11:37:20.751876Z","resolver_source":"raw_fallback","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:16.084717Z","title":"2011.Polyhedron Model","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.084717Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:2ff141b2417b33fe42d29b194f4126516706aed966abc3e3de322eccd1b37eae","observation_id":"0594b605-903f-4322-b341-46515b4e6b2d","resolution":{"observed_at":"2026-08-07T11:37:16.084717Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T11:37:16.114403Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.114403Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:46e0ca077ddeded038f7660410894ce7d6a8942fcd93385abde6047efd69a58c","observation_id":"1560143d-43e0-4fa9-960b-a63438399478","resolution":{"observed_at":"2026-08-07T11:37:16.114403Z","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":"2026-08-07T11:37:22.686738Z","title":"Sadayappan, and Sven Verdoolaege","venue":null,"work_id":"62f645be-d8b6-4c6c-b7a1-a96736bb059c","year":2014},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.177625Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:671782590115ace435be297b1b39ad0f477f1ba5d0ed0dc612c75723ce583428","observation_id":"6c0ee80d-cb00-4aaf-a1a5-337b211ab03b","resolution":{"observed_at":"2026-08-07T11:37:22.691699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.671926Z","title":null,"venue":null,"work_id":"d81e5bf7-5379-44c4-bd40-ec19b96d0f17","year":2012},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.247074Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:6a840d6372879e79a1c3f9ff80c8abca4d0643a2569314edf75e9589081126a5","observation_id":"31fcf7ce-ef14-47ad-bd8a-697c86c0967c","resolution":{"observed_at":"2026-08-07T11:37:22.676198Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2005.13685","last_updated":"2020-05-27T22:25:10Z","snapshot_observed_at":"2026-08-04T08:36:19.412128Z","submitted_at":"2020-05-27T22:25:10Z","title":"ProTuner: Tuning Programs with Monte Carlo Tree Search","version":1},"cited_work":{"arxiv_id":"2005.13685","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.13685","snapshot_observed_at":"2026-08-07T11:37:20.389502Z","title":"ProTuner: Tuning Programs with Monte Carlo Tree Search","venue":"cs.DC","work_id":"0145ed79-c997-4497-af26-8060a8d2b3e5","year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.284028Z"},"links":{"cited_paper":"/paper/2005.13685","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:2c1a14571aa82f60c47c4f291bfa81e8a5c6a316aa904a41b868691f53732df1","observation_id":"ae3fcefc-d6f2-4582-a030-509d20fd1d67","resolution":{"observed_at":"2026-08-07T11:37:20.458042Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.657066Z","title":null,"venue":null,"work_id":"21b21138-47c2-44ef-8e25-3af3a3d5967c","year":2023},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.338421Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:4c885e77ffb40a67c741615c071232e4752d57b58b2f3a509884c61e6cc4cf77","observation_id":"551344b0-a107-4642-a3c6-3b4ae9322ada","resolution":{"observed_at":"2026-08-07T11:37:22.661783Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:16.390493Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.390493Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:2cbdd14a4a2903cf13fb3df4d8fb951b2834de2c7f3a76e106ca1e4460aa98b7","observation_id":"363b4a71-5b76-4533-b609-81a2c79fb1b8","resolution":{"observed_at":"2026-08-07T11:37:16.390493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14698","last_updated":"2023-04-28T09:06:18Z","snapshot_observed_at":"2026-08-04T13:42:51.582026Z","submitted_at":"2023-04-28T09:06:18Z","title":"X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation","version":1},"cited_work":{"arxiv_id":"2304.14698","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.14698","snapshot_observed_at":"2026-08-07T11:37:20.179496Z","title":"X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation","venue":"cs.LG","work_id":"1b3304c8-9c8e-466d-ad30-a5f8133fdaa2","year":2023},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.433055Z"},"links":{"cited_paper":"/paper/2304.14698","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:a45211c9621f4c8c740c9413fc9e1ee49b9377fb23c1b15ed2d952fa056130f0","observation_id":"d9228d05-c3d3-471c-89e8-f1ce3bdd49aa","resolution":{"observed_at":"2026-08-07T11:37:20.242809Z","resolver_source":"local_arxiv","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":"2003.00671","last_updated":"2020-03-04T19:48:50Z","snapshot_observed_at":"2026-07-06T09:01:23.900135Z","submitted_at":"2020-03-02T05:35:32Z","title":"AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2003.00671","doi":null,"metadata_source":"pith","pith_arxiv_id":"2003.00671","snapshot_observed_at":"2026-08-07T11:37:20.026669Z","title":"AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning","venue":"cs.DC","work_id":"97a020ee-0c95-4545-97eb-8e55e35507bc","year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.491677Z"},"links":{"cited_paper":"/paper/2003.00671","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:09bb583ebd65a7c076aaa8157626b2aded2087fd9a2ca090dc0a990f66b69ec2","observation_id":"90a25505-0082-4ad9-873e-1126f77be879","resolution":{"observed_at":"2026-08-07T11:37:20.106443Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:16.540350Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.540350Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:be138484c8ed32565f6cecdacb1057b2b0e2472a0faa728446440651a7fdf09d","observation_id":"2d9b89af-9f1d-47b1-8fa2-95ecb47131cf","resolution":{"observed_at":"2026-08-07T11:37:16.540350Z","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":"2026-08-07T11:37:22.630615Z","title":"Irigoin and R","venue":null,"work_id":"fe3cf599-d9d1-4cd2-96f2-9e08f503d00f","year":1988},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.584897Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:00708642c738e9e1a3f6ea05b15041f892f0491b45a758357c0365bf163b44c1","observation_id":"54fb6591-36b1-4877-aa31-18c381840ca2","resolution":{"observed_at":"2026-08-07T11:37:22.635459Z","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":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-07T11:37:16.641895Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.641895Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:ad5f044174ea905ad8a1bb6957d526a6011f7f1920c8d9a501d65a00342b35f4","observation_id":"5fad93f1-e3ed-4ee1-9241-d8e2d3817ace","resolution":{"observed_at":"2026-08-07T11:37:16.641895Z","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":"2026-08-07T11:37:22.614889Z","title":null,"venue":null,"work_id":"b7f3f5e8-6b60-4bcc-baf9-be7efdb59220","year":2017},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.693534Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:c8bf61bc0492f64934f8ee53402748c77e0bd63b122767080b91b5568978eb28","observation_id":"f50c7060-dbaf-4995-986e-623f279b0260","resolution":{"observed_at":"2026-08-07T11:37:22.619698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0167-8191(98)00029-5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:18.826848Z","title":null,"venue":null,"work_id":"ef7cec9e-498f-41ba-b23d-88068e415f5c","year":1998},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.741185Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:083e4e011bd67105f314d9c97400209caf4021bd17dfeb27530db6f9fd812f7f","observation_id":"9e988c15-4453-438d-a1d8-597e4b87e4e3","resolution":{"observed_at":"2026-08-07T11:37:18.957757Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.598646Z","title":null,"venue":null,"work_id":"b40a718d-c467-484a-8bc4-76a474dcbdbb","year":2025},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.794150Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:75b6fe9dc658655642e4af00f0c7d1bfcdae45d1ae213fb60797bce731d41e23","observation_id":"74b67ab4-d9d6-46e9-b043-8731464cf046","resolution":{"observed_at":"2026-08-07T11:37:22.604248Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.583851Z","title":null,"venue":null,"work_id":"4ccf7e2a-b33c-4806-8d63-fc541b9a623f","year":2019},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.850145Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:5e1d4670082fcad58b36712ff64449cdfae907ad782abadd3a1b44f3a308889a","observation_id":"b8d82523-13d6-4343-b454-fd8fbab2d714","resolution":{"observed_at":"2026-08-07T11:37:22.588073Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1702.02181","last_updated":"2017-02-22T04:43:02Z","snapshot_observed_at":"2026-08-03T09:30:27.788688Z","submitted_at":"2017-02-07T19:59:43Z","title":"Deep Learning with Dynamic Computation Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.02181","snapshot_observed_at":"2026-08-07T11:37:16.894265Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.894265Z"},"links":{"cited_paper":"/paper/1702.02181","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:a21599f0a22303380e8c16943e36e5a98f0b599a9b9a4843e63bcf481d687ab3","observation_id":"20ad90e2-d706-43a6-be14-18dfcc4d44bd","resolution":{"observed_at":"2026-08-07T11:37:16.894265Z","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":"2026-08-07T11:37:16.950807Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:16.950807Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:58bcc369313732bbcfa8c2731838115fc622e8cd0148efc8e290c1c9eb472af9","observation_id":"97c238d8-6d4c-428c-b9a8-d8d2a9aa41b2","resolution":{"observed_at":"2026-08-07T11:37:16.950807Z","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":"2026-08-07T11:37:22.568674Z","title":"2020.A deep learning based cost model for automatic code optimization in tiramisu","venue":null,"work_id":"43519023-c04b-4e84-9ba8-36107c023c07","year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.002429Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:4c938ec22c9c3c635970a93be813e21f48c80f9459bef5303fe6e23e852f52b0","observation_id":"5e037cb0-93b1-489d-afef-eec19d12f32b","resolution":{"observed_at":"2026-08-07T11:37:22.573532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.553536Z","title":null,"venue":null,"work_id":"79144156-be86-46b5-bc6a-b608ad3733df","year":2023},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.050173Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:b72fda6a7608f472af7ac3d4eaacf4772b56b051e5a5a4fbc9913c0e7d590d4c","observation_id":"7c9ccd90-fb57-4386-8bfc-9c3a388e6df3","resolution":{"observed_at":"2026-08-07T11:37:22.558401Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:17.119777Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.119777Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:4a09b313d528f20f3d0367a0b669dd913526ddcb120915993205e1a20b9f277f","observation_id":"7d78d250-d363-4dca-8d1f-c75bf5abed51","resolution":{"observed_at":"2026-08-07T11:37:17.119777Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.02494","last_updated":"2020-02-10T11:57:18Z","snapshot_observed_at":"2026-07-06T07:50:53.148987Z","submitted_at":"2019-05-07T12:15:06Z","title":"Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.02494","snapshot_observed_at":"2026-08-07T11:37:17.180734Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.180734Z"},"links":{"cited_paper":"/paper/1905.02494","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:d8b804f497b90fc3ee1a67e3dcbb6e40f434064d38d06a8fa55e4315ff8b3c56","observation_id":"a5132241-1dd4-4056-ada6-adef4a10a551","resolution":{"observed_at":"2026-08-07T11:37:17.180734Z","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":"2026-08-07T11:37:22.536888Z","title":null,"venue":null,"work_id":"364253c8-03f5-498e-956d-0b01e6ba393e","year":2019},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.254382Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:827f7f4c99b52a16b66b73149de20802154d2021fddc973431f5b5bb33dd6fa7","observation_id":"6209845a-f116-4b86-ae50-b9247009bab0","resolution":{"observed_at":"2026-08-07T11:37:22.541671Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.521632Z","title":null,"venue":null,"work_id":"c8676eed-b42f-46c5-af6a-13a756052ca4","year":2012},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.293455Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:3bc65a9c4f75236aaa3916119e8bc2f5543dff7e10ac238a4c5176bf4ad02de6","observation_id":"c6585340-6b05-4cb2-bb20-ce0c21821775","resolution":{"observed_at":"2026-08-07T11:37:22.526323Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.505531Z","title":"Ramanujam, P","venue":null,"work_id":"6c9256d6-0f55-4b70-9ea5-655b01df93a4","year":null},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.375803Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:cd53cc1c9f0329ca514913718d18b29a39ecc2f7befd472da9210d7dd91a6dcc","observation_id":"11ae586b-8789-4ff9-b8d2-5126730bebaa","resolution":{"observed_at":"2026-08-07T11:37:22.510193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.473779Z","title":"Quilleré and S","venue":null,"work_id":"b53c72b6-3e6b-491a-861a-d613857d17cc","year":2000},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.501340Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:9c719877507d6cf9aeee80125e46334bc8ba981f933b72ccffd54dc823ddec08","observation_id":"3022a394-6df1-4c96-96d4-ea78a31c4501","resolution":{"observed_at":"2026-08-07T11:37:22.478927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:17.580652Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.580652Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:71cbc56ce3ebb2ddf6b84fa1a40c4bd5c99918a61df7834d5b0f4d648259140c","observation_id":"6222457e-4d22-4cad-a94b-f24eb0ce668d","resolution":{"observed_at":"2026-08-07T11:37:17.580652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T11:37:17.677852Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.677852Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:13da6181829f00083c65370f7e710d66da8fe78450ea2f79c471f9473a15fb7f","observation_id":"15307476-c37e-4ef4-bf20-dce42251c195","resolution":{"observed_at":"2026-08-07T11:37:17.677852Z","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":"2026-08-07T11:37:22.374370Z","title":"Sutton and A.G","venue":null,"work_id":"6d9f99a5-6b55-455d-9f6d-30bb1d19fea6","year":2018},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.757033Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:977fd40347072808e5d82e805ae2f8ba8e47e57339587be0f57eb88d3fd19391","observation_id":"00f4166c-9ed2-4b92-94a5-f0608aa0660c","resolution":{"observed_at":"2026-08-07T11:37:22.447530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:22.072327Z","title":null,"venue":null,"work_id":"cf8ca39a-90b0-4621-9406-0e68f9f2aab1","year":2001},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.768810Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:fd831b0fee4af01480f39bbfcbd7a21f4ec537b17583fa18da29fcc8733b00ff","observation_id":"4ad406a4-efb9-4980-b75f-64deaae70c4c","resolution":{"observed_at":"2026-08-07T11:37:22.234418Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:21.850087Z","title":null,"venue":null,"work_id":"b881a023-a466-41e5-9bd2-c0a0a336977a","year":2010},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.868847Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:8e463ef10d3a9d51e3c0a2cd7d46671e7f16e4d87c73248a10e769bf364d1207","observation_id":"681a2f3a-e2f8-43a9-93de-f0e4dfea1a43","resolution":{"observed_at":"2026-08-07T11:37:21.949994Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3401.11834","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:19.466088Z","title":null,"venue":null,"work_id":"70313384-dc5d-4f2c-bde4-8a6a814f24f4","year":2006},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.983637Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:b42a1eef620fc58aa18da8ce1f83e2b533a3c8d9f26b784b21b3a9742a981669","observation_id":"ac849872-b2a8-4a42-8dc7-37e1161dd089","resolution":{"observed_at":"2026-08-07T11:37:19.537337Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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":"1802.04730","last_updated":"2018-06-29T00:16:36Z","snapshot_observed_at":"2026-08-07T06:36:37.208888Z","submitted_at":"2018-02-13T16:53:01Z","title":"Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04730","snapshot_observed_at":"2026-08-07T11:37:18.156929Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.156929Z"},"links":{"cited_paper":"/paper/1802.04730","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:1c7b53b0ff9808173d1f4a02bcc140a4a228b292daec9fa3fbb9139f9d0e8f01","observation_id":"8ac21825-94ca-4d5c-b79b-053f379d124a","resolution":{"observed_at":"2026-08-07T11:37:18.156929Z","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":"2026-08-07T11:37:18.288372Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.288372Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:b4ec16ba092efc2c06328f2f4dab513c6847b10155103c67e2618eeb5a2a005d","observation_id":"66282a02-1566-4cb7-a1a6-955daf68a080","resolution":{"observed_at":"2026-08-07T11:37:18.288372Z","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":"2026-08-07T11:37:21.679551Z","title":null,"venue":null,"work_id":"4eb0b3b7-2f91-47d3-9ea8-c7e82d536d29","year":1991},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.433409Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:74939e2b5df2ad2cb1e0dbcd6ae346c4f2e09015a2edf15d690b68060a7ede3a","observation_id":"874fc4f4-60b1-48df-a0f6-4e3f0d3287a2","resolution":{"observed_at":"2026-08-07T11:37:21.760438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:37:21.542841Z","title":null,"venue":null,"work_id":"67433f57-56d0-4557-b507-c259176af79f","year":2020},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.472914Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:a9bf69e61b263acea331272bf7f849b28ff8ccd8decf159bf704978d3e8ee44e","observation_id":"2dc017a4-87ca-463e-a2c8-a9f1ebdff207","resolution":{"observed_at":"2026-08-07T11:37:21.601240Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2006.06762","last_updated":"2023-10-15T07:00:36Z","snapshot_observed_at":"2026-07-06T09:28:16.483371Z","submitted_at":"2020-06-11T19:40:09Z","title":"Ansor: Generating High-Performance Tensor Programs for Deep Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.06762","snapshot_observed_at":"2026-08-07T11:37:18.587768Z","title":"Gonzalez, and Ion Stoica","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.587768Z"},"links":{"cited_paper":"/paper/2006.06762","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:73fe6176b8418b2607a9bdc22c44f6bc1ed9fd2c6588b8adb09d080a79cf891a","observation_id":"61b60399-70d8-4395-9fd8-444d5b1eb3ab","resolution":{"observed_at":"2026-08-07T11:37:18.587768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.10924","last_updated":"2021-12-17T07:05:38Z","snapshot_observed_at":"2026-08-03T20:33:06.414779Z","submitted_at":"2020-09-23T04:00:53Z","title":"FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.10924","snapshot_observed_at":"2026-08-07T11:37:18.710415Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:18.710415Z"},"links":{"cited_paper":"/paper/2009.10924","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:61450f16965c35e7a8bf3d4fb94ba8a50fa8960c5a68d0ec3b2c8b99fc90eb9d","observation_id":"4da71da3-82fd-4be6-8473-add67e484281","resolution":{"observed_at":"2026-08-07T11:37:18.710415Z","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":"2026-08-07T11:37:22.489997Z","title":"In 38th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages (POPL’11)","venue":null,"work_id":"5beead99-870b-473a-8f4b-7b79c8d97968","year":null},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:17.444124Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:94b6c58b749c5ecbe2569ee080a5fe38300cc4554885dbdb10b7e959df9c775d","observation_id":"2fa3edec-9ada-49f5-9954-0c95236ee886","resolution":{"observed_at":"2026-08-07T11:37:22.495010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.08166","last_updated":"2019-01-08T23:35:15Z","snapshot_observed_at":"2026-08-07T03:58:31.394717Z","submitted_at":"2018-05-21T16:38:12Z","title":"Learning to Optimize Tensor Programs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.08166","snapshot_observed_at":"2026-08-07T11:37:15.849112Z","title":"arXiv:1805.08166","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.849112Z"},"links":{"cited_paper":"/paper/1805.08166","citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:8984378666471130ed3451ea9caddb1684036d1868abf439c723ff2aa4400c3f","observation_id":"cc5a5706-ae76-4f09-8d11-6786ad043efa","resolution":{"observed_at":"2026-08-07T11:37:15.849112Z","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":"2026-08-07T11:37:22.959391Z","title":"Proceedings of Machine Learning and Systems 3 (2021), 181–193","venue":null,"work_id":"73979066-d9fd-47a7-b130-63036cba1c10","year":2021},"citing_paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T11:37:15.195507Z"},"links":{"citing_paper":"/paper/2506.01880"},"observation_digest":"sha256:e49d0e8ccb5aa42c54e7fe4d05116d2ff4f0ed58f7af4eb865680e3216aad478","observation_id":"f66847b5-4ac7-428e-bac4-098f196c9d42","resolution":{"observed_at":"2026-08-07T11:37:22.984033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.01880","last_updated":"2025-06-02T17:09:59Z","latest_version":1,"primary_category":"cs.PL","snapshot_observed_at":"2026-08-07T11:29:50.615992Z","submitted_at":"2025-06-02T17:09:59Z","title":"Pearl: Automatic Code Optimization Using Deep Reinforcement Learning"},"reference_resolution":{"displayed":61,"state_counts":{"malformed_identifier":2,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":40,"verified_exact":9,"verified_fuzzy":9},"total_outbound_references":61},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2506.01880."}