{"as_of":"2026-08-10T16:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67e5f6ffb9f1fac078fdf4310a8917bdac3e8130bc24402bf94c9d7b898a9676","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T10:59:38.628286Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"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/2502.06816/citation-record","integrity":"/paper/2502.06816/integrity","json":"/paper/2502.06816/citation-record.json","paper":"/paper/2502.06816"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T10:59:39.578869Z","title":"Large circuit models: opportunities and challenges,","venue":null,"work_id":"ced8b047-f17d-4df5-a576-f3e4f6f90529","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:37.902572Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:7704273aab589180227507b77921591fd146ee5b8569a4619c263be8f7504bbc","observation_id":"2f4ce6bc-8828-4449-9866-c20648437ebb","resolution":{"observed_at":"2026-08-09T10:59:39.583786Z","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":"2502.18297","last_updated":"2025-02-25T15:34:00Z","snapshot_observed_at":"2026-08-07T17:49:48.807611Z","submitted_at":"2025-02-25T15:34:00Z","title":"DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18297","snapshot_observed_at":"2026-08-09T10:59:37.908294Z","title":"Deepcircuitx: A comprehensive repository-level dataset for rtl code understanding, generation, and ppa analysis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:37.908294Z"},"links":{"cited_paper":"/paper/2502.18297","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:9dca4c80cdd49534a3daeb0af49a1aaa41b74f64dc72290822f6853e3860b780","observation_id":"d6ee6953-a338-482e-9e01-0d6ecd5621ee","resolution":{"observed_at":"2026-08-09T10:59:37.908294Z","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":"2026-08-09T10:59:37.931859Z","title":"Deepgate: Learning neural representations of logic gates,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:37.931859Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:d93f32d737e4e165fa5f717b1fbaf3ab98d2c3f62dc9345c815ee16c9d690f2b","observation_id":"0eedbd09-ae67-452a-bc9b-d40af09271c6","resolution":{"observed_at":"2026-08-09T10:59:37.931859Z","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":"2026-08-09T10:59:39.544067Z","title":"Functionality matters in netlist representation learning,","venue":null,"work_id":"20292cd6-0c23-4507-af00-ef75424f8b0f","year":2022},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:37.993294Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:eba55ffa406dccc3ca9dd9cd36240856d0e0f676270c24f0e99c13a4e2795d82","observation_id":"08d49818-a1a0-45ae-8a80-5c4ee2b656d3","resolution":{"observed_at":"2026-08-09T10:59:39.549510Z","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":"2026-08-09T10:59:39.524742Z","title":"Deepgate2: Functionality-aware circuit representation learning,","venue":null,"work_id":"a7d689d3-f9c0-4259-82e3-8792f63aea91","year":2023},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.041044Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:c50c8314e5e46a6718f6f6664ff253c0213e34bb2e846be772872fa515368dc8","observation_id":"192baacd-0ce0-4057-a79a-497f583754dc","resolution":{"observed_at":"2026-08-09T10:59:39.533003Z","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":"2026-08-09T10:59:39.495576Z","title":"Gamora: Graph learning based symbolic reasoning for large-scale boolean networks,","venue":null,"work_id":"ae65dd0a-addf-4b3d-b426-79e55643f93f","year":2023},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.088947Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f644279518c86323be4db852dd8123507bdd1f944330bb3efe0c83454959075a","observation_id":"8ddf7650-7154-415e-9288-9d91b88ca3ad","resolution":{"observed_at":"2026-08-09T10:59:39.500890Z","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":"2403.01317","last_updated":"2024-04-10T23:31:08Z","snapshot_observed_at":"2026-08-09T18:30:13.876925Z","submitted_at":"2024-03-02T21:33:23Z","title":"Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits","version":4},"cited_work":{"arxiv_id":"2403.01317","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.01317","snapshot_observed_at":"2026-08-09T10:59:38.812334Z","title":"Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits","venue":"cs.LG","work_id":"b7f42404-b992-45be-91da-318b90b73306","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.112962Z"},"links":{"cited_paper":"/paper/2403.01317","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:eb408211a6a956776736574c416361d8ec31f601d0b9b3b6727485e334944e09","observation_id":"bfdf385e-520e-4863-9eef-2d58675af5f1","resolution":{"observed_at":"2026-08-09T10:59:38.817687Z","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":"2026-08-09T10:59:39.434448Z","title":"Deepgate3: Towards scalable circuit representation learning,","venue":null,"work_id":"64852346-9b5d-4ea6-9cda-b8342ab45151","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.118020Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:6a805c7868526e6e9a65a2da346615a6213fd9999fc78b21b599b8bbd04734f6","observation_id":"69f80a7c-7561-4c70-baa0-551e0b86c98b","resolution":{"observed_at":"2026-08-09T10:59:39.464750Z","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":"2026-08-09T10:59:39.418406Z","title":"Deepseq: Deep sequential circuit learning,","venue":null,"work_id":"e8b02f00-38e4-4425-8b6b-edcfc147722d","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.125322Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:9fd1a5fd52b8a4942f6904c552cdffa5069a0dfdeefd89cfa00820a34d77b484","observation_id":"3c6be2d5-2d2b-4bbc-8599-1f18670ff686","resolution":{"observed_at":"2026-08-09T10:59:39.423741Z","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":"2026-08-09T10:59:39.394744Z","title":"Polargate: Breaking the functionality representation bottleneck of and-inverter graph neural network,","venue":null,"work_id":"26cab863-5316-4473-ac41-7a2b4b3b76de","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.136998Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:7810365f125766f7ce22c057313e44003cb81a09e8ea507d05c06a69d6b0f4c7","observation_id":"b8e903bb-9f4f-4c63-9d89-58ff52b245c1","resolution":{"observed_at":"2026-08-09T10:59:39.400383Z","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":"2026-08-09T10:59:38.141498Z","title":"Deeptpi: Test point insertion with deep reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.141498Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f9e5642ccf7a665e560b3cea3982ddbae7558ffc5d1dfbfee501a47f5022c4ff","observation_id":"0a0bf9e4-4355-4fb1-9d18-0a78ccc67c1d","resolution":{"observed_at":"2026-08-09T10:59:38.141498Z","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":"2026-08-09T10:59:39.331022Z","title":"Lsoracle: A logic synthesis framework driven by arti- ficial intelligence,","venue":null,"work_id":"9c8dbd6a-313d-4b98-991a-654450739f14","year":2019},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.146483Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:edd89738f61f9731583214f506a9d98d517c91421dccd9264aeabbe61b58ff85","observation_id":"56be0852-27bc-4785-adad-91f4433e781c","resolution":{"observed_at":"2026-08-09T10:59:39.337582Z","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":"2026-08-09T10:59:38.152529Z","title":"Rtlrewriter: Methodologies for large models aided rtl code optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.152529Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:6d15e7d2c4ab7c2b56dd28eb7326a72836c5120683df868628e716c819f69640","observation_id":"8fcdc352-6f7b-46a1-a0ac-3c42bcc505dd","resolution":{"observed_at":"2026-08-09T10:59:38.152529Z","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":"2026-08-09T10:59:39.248005Z","title":"Circuitfusion: multimodal circuit representation learning for agile chip design,","venue":null,"work_id":"1fe37b87-d8b1-4593-8712-9308d8ef704c","year":2025},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.157363Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:7e8796eeef7eebb27b5eda2ef6bca86872f532d627efbe02a1dbd68b919e9115","observation_id":"32e78b17-83ac-4c85-8437-c50491cf3016","resolution":{"observed_at":"2026-08-09T10:59:39.284869Z","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":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-09T10:59:38.161874Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.161874Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:d07318f387188407d6e338eeaa34e9e1392f1a47428022fcd4a8f1cafd0047b3","observation_id":"d923af4e-19c7-4fde-97de-9d3e3f63f943","resolution":{"observed_at":"2026-08-09T10:59:38.161874Z","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":"2026-08-09T10:59:39.231716Z","title":"Efficient computation of eco patch functions,","venue":null,"work_id":"759464f2-c582-45ac-a661-de8d76f1615d","year":2018},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.167068Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:276ed37d585908546581fbadb21de8827027571c45c45b60f83f86c0438f7574","observation_id":"f07fc5a2-76c0-4ea6-ae6a-0b41061473a6","resolution":{"observed_at":"2026-08-09T10:59:39.236970Z","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":"2026-08-09T10:59:39.215797Z","title":"Abc: A system for sequential synthesis and verification,","venue":null,"work_id":"4ced2a4f-d479-4f58-bc07-c11ad791a34c","year":2007},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.172729Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:ac4e4d950f6f7a3b4387595b86a3b4bc2aa8e88bf9ef2c5ba0030e4bedcebec3","observation_id":"271705c6-54d6-48a3-a97d-59cd0bb31ec9","resolution":{"observed_at":"2026-08-09T10:59:39.221302Z","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":"2403.19446","last_updated":"2025-07-02T13:07:57Z","snapshot_observed_at":"2026-07-06T17:52:34.385599Z","submitted_at":"2024-03-28T14:15:50Z","title":"Logic Optimization Meets SAT: A Novel Framework for Circuit-SAT Solving","version":2},"cited_work":{"arxiv_id":"2403.19446","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.19446","snapshot_observed_at":"2026-08-09T10:59:38.772255Z","title":"Logic Optimization Meets SAT: A Novel Framework for Circuit-SAT Solving","venue":"cs.LO","work_id":"12685cfa-8174-42c9-a8fa-0f7c2fdcb612","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.177250Z"},"links":{"cited_paper":"/paper/2403.19446","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:2583cd5b517e24f0cb0e39f7c53bee87de876bdf4eb0f5c6fea6055f7dd50873","observation_id":"e203c5b3-434d-4c70-a561-7cddbadb439a","resolution":{"observed_at":"2026-08-09T10:59:38.777670Z","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":"2502.01681","last_updated":"2025-05-20T08:17:48Z","snapshot_observed_at":"2026-08-09T18:30:08.670680Z","submitted_at":"2025-02-02T05:25:34Z","title":"DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01681","snapshot_observed_at":"2026-08-09T10:59:38.182095Z","title":"Deepgate4: Efficient and effective representation learning for circuit design at scale,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.182095Z"},"links":{"cited_paper":"/paper/2502.01681","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:fb1978ee58224fb42d196aa29c05829e0545d98bb5413d42dee08bdd3ae3222d","observation_id":"bd2e7ba8-c6b0-44bc-85ad-d40557d44431","resolution":{"observed_at":"2026-08-09T10:59:38.182095Z","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":"2026-08-09T10:59:39.201398Z","title":"Virtually every asic ends up an fpga,","venue":null,"work_id":"6461873d-c17d-4e33-9077-e4006b998ec8","year":2007},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.209351Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f9d1f841f91b2368657926faecf7257cd82231165b9ff99dc5f19dff6fbd8fba","observation_id":"5d524a0c-b4f4-4fff-a720-2e9bd39fb3d0","resolution":{"observed_at":"2026-08-09T10:59:39.206370Z","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":"2026-08-09T10:59:39.187035Z","title":"Match and replace: A functional eco engine for multierror circuit rectification,","venue":null,"work_id":"8af60884-0882-465d-b821-2b032de1eff6","year":2013},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.260488Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:6f1d30d8c19cf77f43d74f2f1b1e5e6c01cd5f9dc6d8d97f7547f519b8081062","observation_id":"353e3a34-d351-4334-b540-a1838d85d9e1","resolution":{"observed_at":"2026-08-09T10:59:39.191792Z","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":"2026-08-09T10:59:39.173080Z","title":"Engineering change order for combinational and sequential design rectification,","venue":null,"work_id":"43c4b4a7-11dc-4e7c-8616-a7e614b26289","year":2020},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.318638Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:a3e1f2264a071c61c083fdea32f1b91276cca40b810b1a3a843004c3a68c0894","observation_id":"468fcad2-efef-4140-a3fb-867613612567","resolution":{"observed_at":"2026-08-09T10:59:39.177705Z","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":"2026-08-09T10:59:39.157892Z","title":"Learning to automate the design updates from observed engineering changes in the chip development cycle,","venue":null,"work_id":"1442ff06-ce20-43b6-b806-d4a5f7fedec0","year":2020},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.360647Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:890626c6dd26404494109f89a4ad0ff1be69d9a678dfa6a0cd8b6f10346db0ac","observation_id":"07069bc7-c6b2-455c-b907-65dd68bfd271","resolution":{"observed_at":"2026-08-09T10:59:39.162686Z","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":"2026-08-09T10:59:39.142913Z","title":"Feep: Functional eco synthesis with efficient patch minimization,","venue":null,"work_id":"1d286d9f-38e6-4bfb-a776-2dabe2bf335b","year":2023},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.421088Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f3d29ade51b9ddecdbb4e42c5a0133442319ee05e485e0c8cc353f3b3abba3c6","observation_id":"4d2cb379-2f39-4f59-b755-1b56b470101f","resolution":{"observed_at":"2026-08-09T10:59:39.148383Z","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":"2026-08-09T10:59:39.128522Z","title":"Reducing structural bias in technology mapping,","venue":null,"work_id":"923f7a4e-4729-4a17-96cb-617862aa91a4","year":2006},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.425711Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:4d8fa95ac010ed7c856cd2fd78ff0ffbc3166dfeb8509cd7db43b95d928fe264","observation_id":"c1efa891-2c55-434d-b46a-4a0ba9451dc5","resolution":{"observed_at":"2026-08-09T10:59:39.133270Z","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":"2026-08-09T10:59:39.113602Z","title":"An enhanced resubstitution algorithm for area-oriented logic optimization,","venue":null,"work_id":"1644f2f1-4295-42da-9827-4338af6cb23c","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.430406Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:87d916ebeb8b9c776b57e533578c31efe9b425fede83b586ead3202ebc3833ea","observation_id":"1c9ad2d5-f7a3-4589-b33b-f81d21b202ff","resolution":{"observed_at":"2026-08-09T10:59:39.118323Z","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":"2026-08-09T10:59:39.098491Z","title":"Logical effort: Designing for speed on the back of an envelope,","venue":null,"work_id":"453b0ea7-e9ba-4f66-aa92-d90090588900","year":1991},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.435273Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:26bf2afc2f9c5627eae2017b2b344d7944da9b0241be57cda13ecf9c119ad0cd","observation_id":"666e1ec0-d79a-4ba6-976f-188bcf6ab7d7","resolution":{"observed_at":"2026-08-09T10:59:39.103843Z","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":"2026-08-09T10:59:39.083207Z","title":"Technology mapping with boolean matching, supergates and choices,","venue":null,"work_id":"c0dc3524-a669-4949-a61a-9107b39467d4","year":2005},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.439991Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f8fa6b6299435d56648fcc8ebf39a2409ec9822c84b1bce18f2fc003d15fd4e7","observation_id":"bd139f21-0ea2-421e-8ff5-da88f8d330e0","resolution":{"observed_at":"2026-08-09T10:59:39.088783Z","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":"2026-08-09T10:59:39.068745Z","title":"Maptune: Advancing asic technology mapping via reinforcement learning guided library tuning,","venue":null,"work_id":"b9a5c942-a0d6-45e8-9a34-f5498b12a694","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.445986Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:74c41860c42a47885682ae21f680cc2cfbbf81f4bec51014d6d07d58bd211eff","observation_id":"e284a828-6edb-4555-a9e1-446825bf750e","resolution":{"observed_at":"2026-08-09T10:59:39.073600Z","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":"2026-08-09T10:59:39.053278Z","title":"Timing-driven technology mapping approximation based on reinforcement learning,","venue":null,"work_id":"91ab7034-1459-460b-80ed-40ba4553acc3","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.450933Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:7b9d3d3c265d6f051063a1ed980c020b9a3971f2361cfaed07ae6bc22383ec53","observation_id":"3c3ddb0f-75b4-42e7-aac8-74db8baccacc","resolution":{"observed_at":"2026-08-09T10:59:39.057969Z","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":"2026-08-09T10:59:38.455724Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.455724Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:cc865f3594e9f9e4bfbc97a0b39b9ac523a84a2db9f4e55ac3eb9e8e026cedcf","observation_id":"7e924a02-2cd4-4e9e-930f-d1298de7ec99","resolution":{"observed_at":"2026-08-09T10:59:38.455724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.02016","last_updated":"2025-05-04T07:35:34Z","snapshot_observed_at":"2026-08-07T15:56:40.554765Z","submitted_at":"2025-05-04T07:35:34Z","title":"ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.02016","snapshot_observed_at":"2026-08-09T10:59:38.461472Z","title":"Forgeeda: A comprehensive multimodal dataset for advancing eda,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.461472Z"},"links":{"cited_paper":"/paper/2505.02016","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:c6b9191cac3d0ccbe984ea5de994ccc9c32cd706fc69ae7b17d4f672431c6f09","observation_id":"55f581bd-2a6b-481f-a414-a989e9791c73","resolution":{"observed_at":"2026-08-09T10:59:38.461472Z","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":"2026-08-09T10:59:39.037627Z","title":"Skywater open source pdk","venue":null,"work_id":"55c16ab9-9dcf-4553-848d-bac0676215eb","year":2020},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.466552Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:6ab7d648dc6d2d9f6b55bddf11b82e64157a20e769a165e4f5a8edf89ae8e60b","observation_id":"2f453049-8727-4713-98ac-0995ec66fbab","resolution":{"observed_at":"2026-08-09T10:59:39.042482Z","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":"2026-08-09T10:59:39.021932Z","title":null,"venue":null,"work_id":"caec289e-0970-40cb-84ff-5dbdca6084c2","year":2023},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.471035Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:0bded5719f7b00a0da57b06902f8e7786d57d633555dd9db430e7d58cbdaf097","observation_id":"6716dd47-6074-410f-92ec-16a5423ce3a3","resolution":{"observed_at":"2026-08-09T10:59:39.026791Z","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":"2026-08-09T10:59:39.005239Z","title":"Globalfoundries gf180mcu open source pdk","venue":null,"work_id":"e4232217-e9f8-43f0-b637-59c7fcca0a27","year":2022},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.519392Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:35984821f4e6276396efd42c5e434c3efe32ceafc88c5be3c96988108cf26590","observation_id":"fc1de53a-4896-43a9-9bf9-f652f706c904","resolution":{"observed_at":"2026-08-09T10:59:39.010932Z","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":"2026-08-09T10:59:38.988989Z","title":null,"venue":null,"work_id":"dd75e95a-2250-4d0c-8fd6-eaef9835007e","year":2009},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.564065Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:cb6bed7d07f0b4fdde2b6e79b9a55c897a4585f6a88190fc4ec70821c690eeda","observation_id":"7bdf3982-8d4f-45a0-82bf-018e21f5ff2b","resolution":{"observed_at":"2026-08-09T10:59:38.994078Z","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":"1802.03685","last_updated":"2019-03-12T00:56:48Z","snapshot_observed_at":"2026-08-01T01:23:29.214436Z","submitted_at":"2018-02-11T03:04:28Z","title":"Learning a SAT Solver from Single-Bit Supervision","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03685","snapshot_observed_at":"2026-08-09T10:59:38.568368Z","title":"Learning a sat solver from single-bit supervision,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.568368Z"},"links":{"cited_paper":"/paper/1802.03685","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:f01cd1b7765ff5e6095f122d04bb66b4450dec648f5223e35163d8465713cc19","observation_id":"61867eba-2605-4c5f-a964-8af66dc48af7","resolution":{"observed_at":"2026-08-09T10:59:38.568368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-09T10:59:38.573879Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.573879Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:4eee09082cec3f3e3590aa5bc12c8fd959e2939471dc48f1755e8ef7d2ecd32a","observation_id":"86e9ac20-f77a-4c01-a69e-4b4951d2c62d","resolution":{"observed_at":"2026-08-09T10:59:38.573879Z","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":"2026-08-09T10:59:38.973801Z","title":"A robust functional eco engine by sat proof minimization and interpolation techniques,","venue":null,"work_id":"c7d13027-abc9-42d6-abe3-c50edc7f4659","year":2010},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.579177Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:73b0e35977f7cef3bd6ccb0364c226fbddc0a08617c9e5da66c446f3f2116ccf","observation_id":"30703ed6-6e6c-430b-b11f-69d9689197ed","resolution":{"observed_at":"2026-08-09T10:59:38.978634Z","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":"2026-08-09T10:59:38.958154Z","title":"Reformer: The efficient trans- former,","venue":null,"work_id":"4f05c7d0-f0aa-4606-b5da-f55b3c8bd716","year":2020},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.584004Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:4a613a461c7acd38fd9a1a447c8c8c974549ab0533359b41e703a2cfc1446615","observation_id":"d642472d-f616-4ab5-a239-b02af7597b68","resolution":{"observed_at":"2026-08-09T10:59:38.963603Z","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":"2026-08-09T10:59:38.942703Z","title":"2017 iccad cad contest problem a: Resource-aware patch generation,","venue":null,"work_id":"e02c6de6-1d78-4a2a-821f-c9253cbd1d07","year":2017},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.588746Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:e40022b6ffb5c5dec626e85852497c211ff0c309c18f239a19f101959d4d0c93","observation_id":"620e70a8-b12e-4df4-a7ce-b238aa770ceb","resolution":{"observed_at":"2026-08-09T10:59:38.948217Z","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":"2026-08-09T10:59:38.925482Z","title":"Combinational profiles of sequential benchmark circuits,","venue":null,"work_id":"758c81c4-868d-414a-babf-d1d18452e2a2","year":1989},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.593375Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:dcb4ff2e0c8f07d4d88c7404dc56f0cf6cf67c7a504cd4c96369fa0b2f9be8fd","observation_id":"0633edcf-2092-45a7-b561-4cae94009ac6","resolution":{"observed_at":"2026-08-09T10:59:38.931198Z","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":"2026-08-09T10:59:38.908490Z","title":"Characteristics of the itc’99 benchmark circuits,","venue":null,"work_id":"64f09908-84a1-40d7-bee6-580143768ccc","year":1999},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.597869Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:126b1cfe47422756016a3e28d0af1a8abceb4c77f93fd11b2aa60ab5a02183ae","observation_id":"1c265d19-d5c4-458c-b94c-920bfdb1fc3b","resolution":{"observed_at":"2026-08-09T10:59:38.913480Z","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":"2026-08-09T10:59:38.892572Z","title":"Iwls 2005 benchmarks,","venue":null,"work_id":"3b5952e0-16ba-4d5b-a090-deb3406edbcc","year":2005},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.607665Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:c3b0f98c195c01a180bae1d0dc6404806fbe44be81b059a71ca52663486a3148","observation_id":"917f7175-27e1-41dd-af86-e300231fe63f","resolution":{"observed_at":"2026-08-09T10:59:38.897946Z","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":"2026-08-09T10:59:38.876501Z","title":"Boolean matching for large libraries,","venue":null,"work_id":"5a9577d3-d46b-4ffc-b17a-08c320dde99e","year":1998},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.613684Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:3db559c9d658f2e82d329a732693242f1fd0f3bc0dfeba6c95663ed63b4a08d7","observation_id":"347c6336-00c6-47fd-8dbc-9df419441de2","resolution":{"observed_at":"2026-08-09T10:59:38.881989Z","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":"2026-08-09T10:59:38.859920Z","title":"Delay-optimal technology mapping by dag covering,","venue":null,"work_id":"fc0a3b3d-de23-4d92-9c89-3a7dc57f34aa","year":1998},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.618679Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:4e1edba7cf8c96141afbfaef89c6bc7ab7138d69123478760611d5d382d4c531","observation_id":"ac584a68-5e64-4e35-b0ba-b66989e724be","resolution":{"observed_at":"2026-08-09T10:59:38.865687Z","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":"2026-08-09T10:59:38.843976Z","title":"The epfl combinational benchmark suite,","venue":null,"work_id":"accc9c6d-ec1d-470e-9214-0d555bd08eec","year":2015},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.623373Z"},"links":{"citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:d6c321120d6753f4a1fa7e27f9c9186cd273299cd35090a66c5fa5111d372ee1","observation_id":"46f82bb3-faae-46b4-909b-de6712d2b6fb","resolution":{"observed_at":"2026-08-09T10:59:38.849066Z","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":"2406.06241","last_updated":"2024-06-10T13:14:31Z","snapshot_observed_at":"2026-08-09T18:49:29.314742Z","submitted_at":"2024-06-10T13:14:31Z","title":"Practical Boolean Decomposition for Delay-driven LUT Mapping","version":1},"cited_work":{"arxiv_id":"2406.06241","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.06241","snapshot_observed_at":"2026-08-09T10:59:38.666748Z","title":"Practical Boolean Decomposition for Delay-driven LUT Mapping","venue":"cs.LO","work_id":"cfbfad85-3361-4d53-b480-8dbeb11ceb0f","year":2024},"citing_paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T10:59:38.628286Z"},"links":{"cited_paper":"/paper/2406.06241","citing_paper":"/paper/2502.06816"},"observation_digest":"sha256:1a126b35132d7aabe75240b76b3b495909dd93816a16fc5ada0729ffa335375f","observation_id":"dc878c7c-7786-443c-9f70-86f9e93dae67","resolution":{"observed_at":"2026-08-09T10:59:38.674418Z","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"}}],"paper":{"arxiv_id":"2502.06816","last_updated":"2025-07-08T03:25:12Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T18:30:07.528409Z","submitted_at":"2025-02-05T02:39:47Z","title":"DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":3,"verified_fuzzy":33},"total_outbound_references":48},"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 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2502.06816."}