{"as_of":"2026-08-11T05:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed39b2fd0a92a8d53f3d87fae61e985bb1729b5f92fb24b8da72c2c21500ec9e","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":"2026-08-10T06:31:04.303077+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":"2026-08-10T20:49:04.789442Z","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:cf4348591b96a64ca8220a2759d654b0a49dbe497d844c8ed15cef5972ddb826","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":"2026-08-09T06:06:43.554360Z","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":"2026-08-10T20:49:04.789442Z","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:cc35fc2fd40de22e87fb9e677c7fd0fdae899995cc37b34e5e940e30e0b39595","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":"2026-08-10T20:49:04.789442Z","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":"sha256:86b9044a80a5abced8f74086506b46e11c06a60d2e1232e8f1509e2263fc445f","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1111.6756","last_updated":"2011-11-29T10:40:44Z","snapshot_observed_at":"2026-08-10T13:23:33.956636Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:57578afba88c2b0e5ea9d3ebb0a3e6d58ac4cad2aa190cbd9dd5c22fbe5799cb","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:f9d5a30b9e518801dbdc12362fb7613173ada07ee5c2a613d7600b1c19bd2e55","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:6782967db9867bca8503d925996a3035562af0815f4058b7afd24d1c693ab12b","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.04091","last_updated":"2020-05-07T07:27:08Z","snapshot_observed_at":"2026-08-10T13:24:02.590992Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:18283dc34f38ccf7b4ee2a767e7ef2da495f39c730e687e5faa85c0f52199042","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:5bc524bcad5d13e61d96eea18082d5e60d74dbfde043cdc9ebc85ad01b7a89e2","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:374a87c33ea1f8ad3557fc6417d531590f112d24059f4deaa7636206b445c58e","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:8a82f7ae632fca06a557ffdc4724904d146e45f429c688e53ce82c069110da56","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":"2026-08-10T20:49:04.789442Z","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":"sha256:15108f47ec8c0a4c15359f14e0e7d7ff2eac0a8a0ad2940b00f74b193f491530","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:3308c4aedd7486879f453b8e614fe943769134de39207792d449428af823e608","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:6fdaca4758f705fafbc92ef66a8a6daa5e9c7f5a439c119a37504f0ce7cc93b9","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:6b598dcad3e81554e336536518859eaee18c380807e4e52b626f778d99e56a01","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":"2026-08-10T20:49:04.789442Z","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":"sha256:9dca7ef92ff3ac36fb3e6bdbca0fcf52a17979b028573b9db525b386bb789c5b","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":"2026-08-10T20:49:04.789442Z","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":"sha256:070a0b8b84dd3007a73bdcf6c7c6a986897ba90f2c028a21fb91fe281e2709da","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":"2026-08-10T20:49:04.789442Z","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":"sha256:98b89c74e5d558725c5777e784e2b604a5f945969ac119bff657813cea1810e6","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:9aa27c1262adf11130cd203ae25f76fd5e2437073fa86e21c3e3358032b882ac","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology","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":"2026-08-10T20:49:04.789442Z","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":"sha256:7d23bccbdaeb631be98bc08e5dcc08d392eb1f3874c76c29904d1062b8350577","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:c54c057f4a9f1339957ead934740ae47b79904e0daab356a079779950721e43c","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:f3ee77d1702a8e08883142537a9ff20eac1208048b2b3427fa185976f6e88ab3","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":"2026-08-10T20:49:04.789442Z","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":"sha256:0673b6389a02b3f39baff3cf6a9e6d11450a6fdca5f4fa7b8fc15e7ed276ce88","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":"2026-08-10T20:49:04.789442Z","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":"sha256:7281f77bb82d733fcd8753db546f2443c37ef857f33235af8a8229ec80ea32aa","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:2bc91d9c7a0ecf9e78f99a75dae1826f4bce87aece5f20b230b7e2fbc5ceba46","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.13685","last_updated":"2020-05-27T22:25:10Z","snapshot_observed_at":"2026-08-08T18:43:34.434601Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:4c1f812191b43eea3b7b80b4a62c008ce5bbb14af146595d338cd71eb57dc72d","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:746cc1677ddba1238dd6a89eb8b12bc411245704be36e48db4d4766f838fe6b0","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:b3436e15434ebf69aeef0baaebca3ab3a032bf056c7ec5f8266598d6bbcb2fec","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":"2026-08-09T21:06:12.292736Z","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":"2026-08-10T20:49:04.789442Z","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:b79456df94f56ae7e44fb55e0b98843836755a70302794e22f69595a47b0879d","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00671","last_updated":"2020-03-04T19:48:50Z","snapshot_observed_at":"2026-08-08T12:15:56.708458Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:682cc3ded2ec5c022ab27230549029fd3b19e3af29a475601d63f69464d8e786","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:6e6d122d392c2d4dd24b663b16557d5d81d517923f8e06f5b8fc8931cd5915db","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":"2026-08-10T20:49:04.789442Z","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":"sha256:69fcee9fb13babc77929286b334ce74fd9ecf05f69490fa59ba45bec21728ed1","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:442cdb84fb4c703a09bb165c60e96609f5b5d3dfb9abf14947d1b8039927490c","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":"2026-08-10T20:49:04.789442Z","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":"sha256:4756fa92181457b419741d0645e501571cc342c8423092713da7a5c34fd65834","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"Parallel Computing","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":"2026-08-10T20:49:04.789442Z","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:e25c7005fbbef3cbf51f8456a9ea1ec7a69397eab913639429dc183ada05d03f","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:59e5528fafc0418c34921d30c14d7585e4f4cbb291e0b24c99723f0e4dd12455","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:c2994474b87d618e001f379c37a065e19b8a6226622c7d55567656cf1eaf1682","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1702.02181","last_updated":"2017-02-22T04:43:02Z","snapshot_observed_at":"2026-08-10T13:22:56.782884Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:16baf7f2ff269e69e397113fc0de7d26d47871264dfea324d253b8580f84acc3","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":"2026-08-10T20:49:04.789442Z","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":"sha256:8556380c7ee010772cdba8780d0b2103076548957abb1c1981cb1a8ff4fadb03","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":"2026-08-10T20:49:04.789442Z","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":"sha256:0c922db19a053c8d4b80d31ddffb2b2eb45115f48118f63d0b71d925f1a943ef","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:5d9effbcb2214bdac2b32717cc1afe105a487d52cb9e262c2a3d2fc98f335c17","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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:cc27fc91cfb69248dcc22f4e134f3e70a3a37e833a12667604f458251fcf18ae","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":"2026-08-09T00:24:05.963466Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:1787e55765da64ce972597d4a974e4d475902b1e0de5bca0b33824fdaac6f986","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":"2026-08-10T20:49:04.789442Z","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":"sha256:0967b81ccb3cdbcd93ae225d851f3ab20c277f71ce0cc3389202c46d1d9c541c","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:256314a2be0b6ed041ffb0dc21cce1334d9e9e88b7e4e10b287a67a386caba31","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:3ddb1c6999b2fbb2786cf94783ce1c991afb67bbd9ef2794d27abb2533b506a1","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:31e08114898e1e5c34848964c416bbd437692e45429e3edf87373e93f96cf0c6","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:54a820307ed973d9ab8d402d6d1a41e4f36d563e8fc02955717008c37f56c671","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":"2026-08-10T20:49:04.789442Z","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":"sha256:8249f66fbaf0df9cc584e63156966271ebc4cacbfa872eba1350f8f46ebeb1c8","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":"2026-08-10T20:49:04.789442Z","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":"sha256:5b202a5fd244299ffb445d82fb83e5a85b3775f0cc6c27022dd1a0b169c727ad","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:5088dbe9b27232fde671ae8468d8e65fbd0d1d024897f36b45cebef740f14afa","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:75a97b335221291ac69284ff7a2cf462be30fc5b75ed55173ce0b4006554575c","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:381812651e49822e24b1edf8a1571e9e0006b90d20edf9b8c06d1ecdc08d6955","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:4e887a3deb83ce735f86a8c349520d53c6d8fbcbf73c11dd2cb8723f71a1c4ac","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":"2026-08-10T20:49:04.789442Z","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":"sha256:1a10e338c413d7d6a234a8260f45dc51cf26713709e45c8de2989b10fc81a8f2","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":"2026-08-10T20:49:04.789442Z","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:df9672d25d877f95f0c0a30ddd01f96a47b1db702f2275d51346149107dc8cac","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:7f9598364963ee49f5109ccd6511714c38194731f3f902edcd83bbe18e9d126e","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.06762","last_updated":"2023-10-15T07:00:36Z","snapshot_observed_at":"2026-08-10T20:56:14.726230Z","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":"2026-08-10T20:49:04.789442Z","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":"sha256:373855c7615427a9b953f5c31f954e337e8ae43a0d6416802d82092ea0fb36d2","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":"2026-08-10T20:49:04.789442Z","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":"sha256:17c5c76c358dd1e9161562db34753f9959e264bf2c4d66471893e6b646ec6af7","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":"2026-08-10T20:49:04.789442Z","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":"sha256:80f9a89d87323f8ad01229252ba875aa020ed554415d92a054831fa65e9f1d8f","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"sha256:2c61c73af1c69985a91566a88847a610f1e3b0c1e6c0312eb588f1c8f08340f9","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":"2026-08-10T20:49:04.789442Z","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:b718a1621c76039946147bd7d412b1995333b28813b4405ce469a9b040954281","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 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-10T20:49:04.789442Z","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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2506.01880."}