{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BCH3VNZMRVSJQWBIBU7WKLER5P","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"aa36cdca6104663ee18cee55f6eb0a49cf03c201e52b44ebf6fdbde9e39c55c8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AR","submitted_at":"2025-03-28T07:27:27Z","title_canon_sha256":"6700b17845bb9824d6bb9fd79bdc534f81a3eeb5c9839ae37c254d2f2aaceeb1"},"schema_version":"1.0","source":{"id":"2504.03711","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.03711","created_at":"2026-07-03T01:17:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.03711v2","created_at":"2026-07-03T01:17:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.03711","created_at":"2026-07-03T01:17:11Z"},{"alias_kind":"pith_short_12","alias_value":"BCH3VNZMRVSJ","created_at":"2026-07-03T01:17:11Z"},{"alias_kind":"pith_short_16","alias_value":"BCH3VNZMRVSJQWBI","created_at":"2026-07-03T01:17:11Z"},{"alias_kind":"pith_short_8","alias_value":"BCH3VNZM","created_at":"2026-07-03T01:17:11Z"}],"graph_snapshots":[{"event_id":"sha256:13cd453e283d77bff41e341ee692ffbfedd55e3674bc22173f2fae80441f46ab","target":"graph","created_at":"2026-07-03T01:17:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.03711/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications. Most recently, foundation AI models for circuits have emerged as a new technology trend. Unlike traditional task-specific AI solutions, these new AI models are developed through two stages: 1) self-supervised pre-training on a large amount of unlabeled data to learn intrinsic circuit properties; and 2) efficient fine-tuning for specific downstream applications, such as early-stage design quality evaluation, circuit-related context generation, an","authors_text":"Jing Wang, Shang Liu, Wenji Fang, Yao Lu, Yuchao Wu, Yuzhe Ma, Zhiyao Xie","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AR","submitted_at":"2025-03-28T07:27:27Z","title":"A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.03711","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:16c2b244054ef80019f76be777e7504e088cdc9232a5678fbbbb9caaa57ad552","target":"record","created_at":"2026-07-03T01:17:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"aa36cdca6104663ee18cee55f6eb0a49cf03c201e52b44ebf6fdbde9e39c55c8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AR","submitted_at":"2025-03-28T07:27:27Z","title_canon_sha256":"6700b17845bb9824d6bb9fd79bdc534f81a3eeb5c9839ae37c254d2f2aaceeb1"},"schema_version":"1.0","source":{"id":"2504.03711","kind":"arxiv","version":2}},"canonical_sha256":"088fbab72c8d649858280d3f652c91ebf68933634be622724a5460ed6fd669f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"088fbab72c8d649858280d3f652c91ebf68933634be622724a5460ed6fd669f5","first_computed_at":"2026-07-03T01:17:11.238916Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-03T01:17:11.238916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EyZgmMjWzmftY9z3o3SjPTnIYzqsJ1bWg3wGyUGJYx+3xB4CNWLIiaROv2bQSoGCBcvR/+VaHFm2rdBM8F+ZCA==","signature_status":"signed_v1","signed_at":"2026-07-03T01:17:11.239385Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.03711","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16c2b244054ef80019f76be777e7504e088cdc9232a5678fbbbb9caaa57ad552","sha256:13cd453e283d77bff41e341ee692ffbfedd55e3674bc22173f2fae80441f46ab"],"state_sha256":"bc796ec2f6fb962567f7872734b52a07ec844be428820b946432959564652291"}