{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:V25J26DSQMMKXDXPSBJBDDLNC4","short_pith_number":"pith:V25J26DS","canonical_record":{"source":{"id":"2411.13560","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-07T02:49:53Z","cross_cats_sorted":["cs.AR","cs.ET","eess.SP"],"title_canon_sha256":"55e8fbae5d0ecf72462ddf5351183abab27ebe14d0128cf93a573aec6adc9b4b","abstract_canon_sha256":"8d65ddb6b26da3e6c2795b92e66c02289264ffd2d3921e89dac73abb22b35741"},"schema_version":"1.0"},"canonical_sha256":"aeba9d78728318ab8eef9052118d6d173e185bb5bdbde97e048514e0f0550bb3","source":{"kind":"arxiv","id":"2411.13560","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.13560","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.13560v1","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13560","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_12","alias_value":"V25J26DSQMMK","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_16","alias_value":"V25J26DSQMMKXDXP","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_8","alias_value":"V25J26DS","created_at":"2026-07-05T09:38:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:V25J26DSQMMKXDXPSBJBDDLNC4","target":"record","payload":{"canonical_record":{"source":{"id":"2411.13560","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-07T02:49:53Z","cross_cats_sorted":["cs.AR","cs.ET","eess.SP"],"title_canon_sha256":"55e8fbae5d0ecf72462ddf5351183abab27ebe14d0128cf93a573aec6adc9b4b","abstract_canon_sha256":"8d65ddb6b26da3e6c2795b92e66c02289264ffd2d3921e89dac73abb22b35741"},"schema_version":"1.0"},"canonical_sha256":"aeba9d78728318ab8eef9052118d6d173e185bb5bdbde97e048514e0f0550bb3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:33.617107Z","signature_b64":"XVZCgq7fvw1BTLv0Xmg2ch4QqQepkd9zoQCshxhh+WBs1FemfdtJp1rgjrSm96dug826BOh5yqsA8acmKV5jCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aeba9d78728318ab8eef9052118d6d173e185bb5bdbde97e048514e0f0550bb3","last_reissued_at":"2026-07-05T09:38:33.616577Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:33.616577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.13560","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:38:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TF9n4ZPKl2xC6Ti9zmKWZnXoM+b1Xi+e7I/86oaSWy93ZYInbdz6AHBAsmMlupPXYZjI/vgC0zrR8dw2/i62BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:28:27.781049Z"},"content_sha256":"0a7caf4e968823b8484f0540669bb7634840656a2f886f3c2f50ed88b95124b5","schema_version":"1.0","event_id":"sha256:0a7caf4e968823b8484f0540669bb7634840656a2f886f3c2f50ed88b95124b5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:V25J26DSQMMKXDXPSBJBDDLNC4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AR","cs.ET","eess.SP"],"primary_cat":"cs.AI","authors_text":"Alvin Liu, Bingyu Chen, Cheng Chang, Genhao Zhang, Lei He, Tianjia Zhou, Ting-Jung Lin, Yaxing Wang, Yichen Shi, Yuhao Gao, Zhiping Yu, Zhuofu Tao","submitted_at":"2024-11-07T02:49:53Z","abstract_excerpt":"High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experience-driven, which makes the automation of AMS circuit design a formidable challenge. Large language models (LLMs) have emerged as powerful tools for Electronic Design Automation (EDA) applications, fostering advancements in the automatic design process for large-scale AMS circuits. However, the absence of high-quality datasets has led to issues such as model hallucination, which undermines the robustness of automatic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13560","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/2411.13560/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:38:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1mIue7asvHLu8kGrS99ulI4+0NJsqjMZRH994noRXqKrSPHssDGBIrPbsZiu690dmfJ5UJHrZuIKs5Jsz/CvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:28:27.781549Z"},"content_sha256":"57addc87bf018414cd0b7f0da7d52337d15b90e0a8cb02665efc720599860c68","schema_version":"1.0","event_id":"sha256:57addc87bf018414cd0b7f0da7d52337d15b90e0a8cb02665efc720599860c68"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V25J26DSQMMKXDXPSBJBDDLNC4/bundle.json","state_url":"https://pith.science/pith/V25J26DSQMMKXDXPSBJBDDLNC4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V25J26DSQMMKXDXPSBJBDDLNC4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T23:28:27Z","links":{"resolver":"https://pith.science/pith/V25J26DSQMMKXDXPSBJBDDLNC4","bundle":"https://pith.science/pith/V25J26DSQMMKXDXPSBJBDDLNC4/bundle.json","state":"https://pith.science/pith/V25J26DSQMMKXDXPSBJBDDLNC4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V25J26DSQMMKXDXPSBJBDDLNC4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:V25J26DSQMMKXDXPSBJBDDLNC4","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":"8d65ddb6b26da3e6c2795b92e66c02289264ffd2d3921e89dac73abb22b35741","cross_cats_sorted":["cs.AR","cs.ET","eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-07T02:49:53Z","title_canon_sha256":"55e8fbae5d0ecf72462ddf5351183abab27ebe14d0128cf93a573aec6adc9b4b"},"schema_version":"1.0","source":{"id":"2411.13560","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.13560","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.13560v1","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13560","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_12","alias_value":"V25J26DSQMMK","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_16","alias_value":"V25J26DSQMMKXDXP","created_at":"2026-07-05T09:38:33Z"},{"alias_kind":"pith_short_8","alias_value":"V25J26DS","created_at":"2026-07-05T09:38:33Z"}],"graph_snapshots":[{"event_id":"sha256:57addc87bf018414cd0b7f0da7d52337d15b90e0a8cb02665efc720599860c68","target":"graph","created_at":"2026-07-05T09:38:33Z","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/2411.13560/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experience-driven, which makes the automation of AMS circuit design a formidable challenge. Large language models (LLMs) have emerged as powerful tools for Electronic Design Automation (EDA) applications, fostering advancements in the automatic design process for large-scale AMS circuits. However, the absence of high-quality datasets has led to issues such as model hallucination, which undermines the robustness of automatic","authors_text":"Alvin Liu, Bingyu Chen, Cheng Chang, Genhao Zhang, Lei He, Tianjia Zhou, Ting-Jung Lin, Yaxing Wang, Yichen Shi, Yuhao Gao, Zhiping Yu, Zhuofu Tao","cross_cats":["cs.AR","cs.ET","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-07T02:49:53Z","title":"AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13560","kind":"arxiv","version":1},"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":"sha256:0a7caf4e968823b8484f0540669bb7634840656a2f886f3c2f50ed88b95124b5","target":"record","created_at":"2026-07-05T09:38:33Z","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":"8d65ddb6b26da3e6c2795b92e66c02289264ffd2d3921e89dac73abb22b35741","cross_cats_sorted":["cs.AR","cs.ET","eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-07T02:49:53Z","title_canon_sha256":"55e8fbae5d0ecf72462ddf5351183abab27ebe14d0128cf93a573aec6adc9b4b"},"schema_version":"1.0","source":{"id":"2411.13560","kind":"arxiv","version":1}},"canonical_sha256":"aeba9d78728318ab8eef9052118d6d173e185bb5bdbde97e048514e0f0550bb3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aeba9d78728318ab8eef9052118d6d173e185bb5bdbde97e048514e0f0550bb3","first_computed_at":"2026-07-05T09:38:33.616577Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:38:33.616577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XVZCgq7fvw1BTLv0Xmg2ch4QqQepkd9zoQCshxhh+WBs1FemfdtJp1rgjrSm96dug826BOh5yqsA8acmKV5jCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:38:33.617107Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.13560","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a7caf4e968823b8484f0540669bb7634840656a2f886f3c2f50ed88b95124b5","sha256:57addc87bf018414cd0b7f0da7d52337d15b90e0a8cb02665efc720599860c68"],"state_sha256":"e73f7d96f44c3dad0742e4cabe3325fa07b397836ba7d4ec1f0ea2d9a560d220"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CShmGhnbJp5bNaFjIHuS//nolqy7w4yvzBTRWDYWilgvT/SFzkRAzjylPnRubRcy20swlnKAvcSqTDFVIv1IBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T23:28:27.785327Z","bundle_sha256":"66a55de667cebbc633c0249f7a08ea90892a5f2034adeb23f88043d73f635ea1"}}