{"as_of":"2026-08-20T06:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa735b53740088a9bcd12837903c4c667bbe252beb99004d064f9bca208aaad7","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:52:19.045608Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T22:29:01.081748Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-12T20:56:15.495894Z","title":"D., Bercea, G., Chen, T., Eichenberger, A","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.09242","last_updated":"2024-11-14T07:16:23Z","snapshot_observed_at":"2026-08-17T02:37:20.167123Z","submitted_at":"2024-11-14T07:16:23Z","title":"FluidML: Fast and Memory Efficient Inference Optimization","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T20:56:15.495894Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2411.09242"},"observation_digest":"sha256:51b7224bfe9bbaaf3e0ee4528dd2f95474402082799782099f26e492418865bc","observation_id":"867c6d68-25de-4b0d-9dcd-11f0f7f0c093","resolution":{"observed_at":"2026-08-12T20:56:15.495894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-08T23:48:51.610577Z","title":"Le, Tong Chen, Gong Su, Haruki Imai, Yasushi Negishi, Anh Leu, Kevin O’Brien, Kiyokuni Kawachiya, and Alexandre E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04063","last_updated":"2025-02-06T13:20:29Z","snapshot_observed_at":"2026-08-16T10:39:25.938365Z","submitted_at":"2025-02-06T13:20:29Z","title":"A Multi-level Compiler Backend for Accelerated Micro-kernels Targeting RISC-V ISA Extensions","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T23:48:51.610577Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2502.04063"},"observation_digest":"sha256:8bd0fe11ea0435e54e944aee2f08544858ef2ef6adc6ce615603832db6455938","observation_id":"457d0097-0b57-4cd8-93b1-bf73d0cad007","resolution":{"observed_at":"2026-08-08T23:48:51.610577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-07T23:37:01.351277Z","title":"Compiling onnx neural net- work models using mlir.arXiv preprint arXiv:2008.08272,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.08821","last_updated":"2025-05-08T04:42:55Z","snapshot_observed_at":"2026-08-10T22:40:02.751747Z","submitted_at":"2025-02-12T22:24:49Z","title":"DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with Saliency Maps","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T23:37:01.351277Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2502.08821"},"observation_digest":"sha256:965cc4bb25111a7f65cd629c9675df139d20348cf05378e413c6d3f065f99ac8","observation_id":"5da1d094-c491-4ef3-896e-47a2bef3a989","resolution":{"observed_at":"2026-08-07T23:37:01.351277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-16T05:52:19.045608Z","title":"Compiling onnx neural network models using mlir","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-20T04:34:41.880001Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.045608Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:6a1710addca67e4325fcac8b54861b627c2adf9fd0b38459cadec5b087880db8","observation_id":"e898757b-3198-4b31-8a82-f111159ab967","resolution":{"observed_at":"2026-08-16T05:52:19.045608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-06T23:50:07.651147Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.16048","last_updated":"2025-06-19T06:05:26Z","snapshot_observed_at":"2026-08-19T19:06:53.907541Z","submitted_at":"2025-06-19T06:05:26Z","title":"WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T23:50:07.651147Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2506.16048"},"observation_digest":"sha256:07673e1cf7e71a0b52bbb4a1c8550a19299944994bed7c4726a382aeadcf172d","observation_id":"2fbd6698-31a0-4564-8fee-f31ad5e5b84b","resolution":{"observed_at":"2026-08-06T23:50:07.651147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-06T19:19:10.082227Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05940","last_updated":"2025-07-08T12:38:41Z","snapshot_observed_at":"2026-08-20T01:13:20.826516Z","submitted_at":"2025-07-08T12:38:41Z","title":"Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T19:19:10.082227Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2507.05940"},"observation_digest":"sha256:793e288b13d7fe77b838cfeb5628fe2eab65dea72a8ef5eec232578217d281b5","observation_id":"531f5d7d-7dcd-43b2-83df-f98de157ebff","resolution":{"observed_at":"2026-08-06T19:19:10.082227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-04T19:39:05.493826Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09154","last_updated":"2025-09-11T05:23:22Z","snapshot_observed_at":"2026-08-06T15:36:44.934159Z","submitted_at":"2025-09-11T05:23:22Z","title":"Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-04T19:39:05.493826Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2509.09154"},"observation_digest":"sha256:041371dab366b196e8740553283a07e59dbf06a725559d41322ed3a9544570ca","observation_id":"10177488-b553-4498-9659-11bcd74c5eb4","resolution":{"observed_at":"2026-08-04T19:39:05.493826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":"2008.08272","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-07-03T22:29:01.081748Z","title":"arXiv preprint arXiv:2008.08272(2020)","venue":null,"work_id":"2cf45a6f-da86-4c97-a08a-621578c45b2c","year":2008},"citing_paper":{"arxiv_id":"2606.06747","last_updated":"2026-06-18T01:49:06Z","snapshot_observed_at":"2026-08-14T22:38:02.597050Z","submitted_at":"2026-06-04T22:01:30Z","title":"Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T00:00:07.129336Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2606.06747"},"observation_digest":"sha256:d8abf9d073cc19649eab9dbb5d229dddf52418ec07e0a61c50cf8619880bcd82","observation_id":"be4090b4-7e5f-4d27-a9f8-56895a4efd83","resolution":{"observed_at":"2026-07-02T15:17:07.566893Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":"2008.08272","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-07-03T22:29:01.081748Z","title":"arXiv preprint arXiv:2008.08272(2020)","venue":null,"work_id":"2cf45a6f-da86-4c97-a08a-621578c45b2c","year":2008},"citing_paper":{"arxiv_id":"2606.18421","last_updated":"2026-06-16T19:15:17Z","snapshot_observed_at":"2026-07-06T23:53:54.600341Z","submitted_at":"2026-06-16T19:15:17Z","title":"Finding Compiler-Platform Interaction Bugs in Deep Learning Pipelines via Cross-Layer Constraints","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T23:30:03.186368Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2606.18421"},"observation_digest":"sha256:9bb04033c095ef85bfdeeea9984e3af564f35d3a529b654f1b4347a99a1cf47b","observation_id":"da951a0c-1636-4f6a-a23f-cfd8cf1f30ff","resolution":{"observed_at":"2026-07-03T22:29:01.083094Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-07-12T08:54:39.253884Z","title":"arXiv: 2008.08272","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.02616","last_updated":"2026-07-01T18:00:00Z","snapshot_observed_at":"2026-08-14T12:45:14.076223Z","submitted_at":"2026-07-01T18:00:00Z","title":"MLIR for Quantum Beyond Gate Cancellation: Quantum Circuit Mapping Reimagined","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-12T08:54:39.253884Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2607.02616"},"observation_digest":"sha256:0a90de8283998407fb1137b15d35b79494799937e3809babc2019d7ff2bd8a0a","observation_id":"3b471db9-e75b-4665-a872-d199f64de548","resolution":{"observed_at":"2026-07-12T08:54:39.253884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2008.08272/citation-record","integrity":"/paper/2008.08272/integrity","json":"/paper/2008.08272/citation-record.json","paper":"/paper/2008.08272"},"outbound":[],"paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","latest_version":2,"primary_category":"cs.PL","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2008.08272."}