{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HZS2ABDF7YSZE2P2PCEVUHWUXV","short_pith_number":"pith:HZS2ABDF","schema_version":"1.0","canonical_sha256":"3e65a00465fe259269fa78895a1ed4bd68fc20879df71f80a1f8541ff451f239","source":{"kind":"arxiv","id":"2506.06787","version":1},"attestation_state":"computed","paper":{"title":"FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Qiyun Zhao","submitted_at":"2025-06-07T13:04:07Z","abstract_excerpt":"As integrated circuit scale grows and design complexity rises, effective circuit representation helps support logic synthesis, formal verification, and other automated processes in electronic design automation. And-Inverter Graphs (AIGs), as a compact and canonical structure, are widely adopted for representing Boolean logic in these workflows. However, the increasing complexity and integration density of modern circuits introduce structural heterogeneity and global logic information loss in AIGs, posing significant challenges to accurate circuit modeling. To address these issues, we propose F"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.06787","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T13:04:07Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"cec76f4f54c50b01ccbaec792d92aaa877aef7127cf8968ed4f80793d6cf0bbc","abstract_canon_sha256":"92a8b6c5aed711c92b18be98416cd31ee465fd24fb7694e3af5f8efa9f832326"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:55.632875Z","signature_b64":"YM3FtvLPM0SIL77PUavlGxVUxFgGXwzha6miMl4yz8dIBsg5gAYqeFjW7L+ANU08fmPx8V+YAd3A1jXy7Ng2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e65a00465fe259269fa78895a1ed4bd68fc20879df71f80a1f8541ff451f239","last_reissued_at":"2026-07-05T11:17:55.632435Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:55.632435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Qiyun Zhao","submitted_at":"2025-06-07T13:04:07Z","abstract_excerpt":"As integrated circuit scale grows and design complexity rises, effective circuit representation helps support logic synthesis, formal verification, and other automated processes in electronic design automation. And-Inverter Graphs (AIGs), as a compact and canonical structure, are widely adopted for representing Boolean logic in these workflows. However, the increasing complexity and integration density of modern circuits introduce structural heterogeneity and global logic information loss in AIGs, posing significant challenges to accurate circuit modeling. To address these issues, we propose F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06787","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.06787/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.06787","created_at":"2026-07-05T11:17:55.632492+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06787v1","created_at":"2026-07-05T11:17:55.632492+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06787","created_at":"2026-07-05T11:17:55.632492+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZS2ABDF7YSZ","created_at":"2026-07-05T11:17:55.632492+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZS2ABDF7YSZE2P2","created_at":"2026-07-05T11:17:55.632492+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZS2ABDF","created_at":"2026-07-05T11:17:55.632492+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV","json":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV.json","graph_json":"https://pith.science/api/pith-number/HZS2ABDF7YSZE2P2PCEVUHWUXV/graph.json","events_json":"https://pith.science/api/pith-number/HZS2ABDF7YSZE2P2PCEVUHWUXV/events.json","paper":"https://pith.science/paper/HZS2ABDF"},"agent_actions":{"view_html":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV","download_json":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV.json","view_paper":"https://pith.science/paper/HZS2ABDF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06787&json=true","fetch_graph":"https://pith.science/api/pith-number/HZS2ABDF7YSZE2P2PCEVUHWUXV/graph.json","fetch_events":"https://pith.science/api/pith-number/HZS2ABDF7YSZE2P2PCEVUHWUXV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV/action/storage_attestation","attest_author":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV/action/author_attestation","sign_citation":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV/action/citation_signature","submit_replication":"https://pith.science/pith/HZS2ABDF7YSZE2P2PCEVUHWUXV/action/replication_record"}},"created_at":"2026-07-05T11:17:55.632492+00:00","updated_at":"2026-07-05T11:17:55.632492+00:00"}