{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LF4QOWHAARIKGBYDN67RJNQKHZ","short_pith_number":"pith:LF4QOWHA","canonical_record":{"source":{"id":"2505.09155","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T05:32:55Z","cross_cats_sorted":[],"title_canon_sha256":"84a692904695cdd29c51520cc924339f3b375366bd934fcb75038ff3fe0ca01f","abstract_canon_sha256":"da1e889e313ce8b4cdff7fe96b401d578514b9d6f22fe369e549cbf16bd408ed"},"schema_version":"1.0"},"canonical_sha256":"59790758e00450a307036fbf14b60a3e4584478d35d8549cb08e128b8b62f9df","source":{"kind":"arxiv","id":"2505.09155","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.09155","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"arxiv_version","alias_value":"2505.09155v1","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09155","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_12","alias_value":"LF4QOWHAARIK","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_16","alias_value":"LF4QOWHAARIKGBYD","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_8","alias_value":"LF4QOWHA","created_at":"2026-07-05T11:02:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LF4QOWHAARIKGBYDN67RJNQKHZ","target":"record","payload":{"canonical_record":{"source":{"id":"2505.09155","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T05:32:55Z","cross_cats_sorted":[],"title_canon_sha256":"84a692904695cdd29c51520cc924339f3b375366bd934fcb75038ff3fe0ca01f","abstract_canon_sha256":"da1e889e313ce8b4cdff7fe96b401d578514b9d6f22fe369e549cbf16bd408ed"},"schema_version":"1.0"},"canonical_sha256":"59790758e00450a307036fbf14b60a3e4584478d35d8549cb08e128b8b62f9df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:51.140121Z","signature_b64":"5wMqI1BKoZRyBUiV8XNdGVlglZ7l2ckRVTtejAqlm2Kvivw9MukwXfeWF+tX/6PhTXDYj30/+1Z8sXyS/DY6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59790758e00450a307036fbf14b60a3e4584478d35d8549cb08e128b8b62f9df","last_reissued_at":"2026-07-05T11:02:51.139641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:51.139641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.09155","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":"2026-07-05T11:02:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PoU2xtJ61pv1nQEEZw87Hboabi1xnUZ18QmJ9DR0CrHGEck8t4eQESonQDwnTlLq21m2lexubj67hKqzfFA5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:57:30.984341Z"},"content_sha256":"09fc68fa81e3ba186f650b64601fb7cd7fa0dd9b8a145653c8dfdce8d10f4d93","schema_version":"1.0","event_id":"sha256:09fc68fa81e3ba186f650b64601fb7cd7fa0dd9b8a145653c8dfdce8d10f4d93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LF4QOWHAARIKGBYDN67RJNQKHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongyang Wang, Lei He, Li Huang, Ting-Jung Lin, Yichen Shi, Yuhao Gao, Zhiping Yu, Zhuofu Tao","submitted_at":"2025-05-14T05:32:55Z","abstract_excerpt":"Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of high-quality schematic-netlist training data. Existing work such as AMSnet applies schematic parsing to generate netlists. However, these methods rely on hard-coded heuristics and are difficult to apply to complex or noisy schematics in this paper. We therefore propose a novel net detection mechanism based on segmentation with high robustness. The proposed method also recovers positional information, allowing digital r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09155","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/2505.09155/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":"2026-07-05T11:02:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ODx6/R7zaaQB8stLW6hFpwjNGER+CYlmLbsT9nhhywQlVqFDPrY4YI/JMgRgUCwwYL8CzSrmciLvJ2a7PRTGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:57:30.984911Z"},"content_sha256":"3261f4fcd48c5a3195ef95b18ae85d4d5261038ba0cbb07ecd7e71eb81c71914","schema_version":"1.0","event_id":"sha256:3261f4fcd48c5a3195ef95b18ae85d4d5261038ba0cbb07ecd7e71eb81c71914"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/bundle.json","state_url":"https://pith.science/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/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":"2026-08-09T21:57:30Z","links":{"resolver":"https://pith.science/pith/LF4QOWHAARIKGBYDN67RJNQKHZ","bundle":"https://pith.science/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/bundle.json","state":"https://pith.science/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LF4QOWHAARIKGBYDN67RJNQKHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LF4QOWHAARIKGBYDN67RJNQKHZ","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":"da1e889e313ce8b4cdff7fe96b401d578514b9d6f22fe369e549cbf16bd408ed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T05:32:55Z","title_canon_sha256":"84a692904695cdd29c51520cc924339f3b375366bd934fcb75038ff3fe0ca01f"},"schema_version":"1.0","source":{"id":"2505.09155","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.09155","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"arxiv_version","alias_value":"2505.09155v1","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09155","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_12","alias_value":"LF4QOWHAARIK","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_16","alias_value":"LF4QOWHAARIKGBYD","created_at":"2026-07-05T11:02:51Z"},{"alias_kind":"pith_short_8","alias_value":"LF4QOWHA","created_at":"2026-07-05T11:02:51Z"}],"graph_snapshots":[{"event_id":"sha256:3261f4fcd48c5a3195ef95b18ae85d4d5261038ba0cbb07ecd7e71eb81c71914","target":"graph","created_at":"2026-07-05T11:02:51Z","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/2505.09155/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of high-quality schematic-netlist training data. Existing work such as AMSnet applies schematic parsing to generate netlists. However, these methods rely on hard-coded heuristics and are difficult to apply to complex or noisy schematics in this paper. We therefore propose a novel net detection mechanism based on segmentation with high robustness. The proposed method also recovers positional information, allowing digital r","authors_text":"Hongyang Wang, Lei He, Li Huang, Ting-Jung Lin, Yichen Shi, Yuhao Gao, Zhiping Yu, Zhuofu Tao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T05:32:55Z","title":"AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09155","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":"sha256:09fc68fa81e3ba186f650b64601fb7cd7fa0dd9b8a145653c8dfdce8d10f4d93","target":"record","created_at":"2026-07-05T11:02:51Z","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":"da1e889e313ce8b4cdff7fe96b401d578514b9d6f22fe369e549cbf16bd408ed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T05:32:55Z","title_canon_sha256":"84a692904695cdd29c51520cc924339f3b375366bd934fcb75038ff3fe0ca01f"},"schema_version":"1.0","source":{"id":"2505.09155","kind":"arxiv","version":1}},"canonical_sha256":"59790758e00450a307036fbf14b60a3e4584478d35d8549cb08e128b8b62f9df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59790758e00450a307036fbf14b60a3e4584478d35d8549cb08e128b8b62f9df","first_computed_at":"2026-07-05T11:02:51.139641Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:51.139641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5wMqI1BKoZRyBUiV8XNdGVlglZ7l2ckRVTtejAqlm2Kvivw9MukwXfeWF+tX/6PhTXDYj30/+1Z8sXyS/DY6Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:51.140121Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.09155","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09fc68fa81e3ba186f650b64601fb7cd7fa0dd9b8a145653c8dfdce8d10f4d93","sha256:3261f4fcd48c5a3195ef95b18ae85d4d5261038ba0cbb07ecd7e71eb81c71914"],"state_sha256":"1da486084f454872135c6145c18ab431a46354cd58734f2a8b1e71fe1be8504b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4ZaTKZ0JA3tBfZ5fwgnbsxUvhbtHYqwaPJW5TuDK7S88KZJ0oRkupmMDj54ml4g9eVEm1UcrLJ0/Gw9bBdl/Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:57:30.990455Z","bundle_sha256":"72604ffba65b449ef6479e19549dd51b869567ec76ee5159dccecdd1dd6c68c5"}}