{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AJOP2LMO45JELL324FTHE7XWUD","short_pith_number":"pith:AJOP2LMO","canonical_record":{"source":{"id":"2605.16480","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.BM","submitted_at":"2026-05-15T17:44:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"94ca4ad630776d953f42e36b87e77b7d7f40f80707668682182271bc1a939dae","abstract_canon_sha256":"78e6f7213125c1641475524bffea4e4df0e068cb519dbc53f2b99d300786c270"},"schema_version":"1.0"},"canonical_sha256":"025cfd2d8ee75245af7ae166727ef6a0ff5287b2feb8be4d271e69c004e7c266","source":{"kind":"arxiv","id":"2605.16480","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16480","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16480v1","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16480","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_12","alias_value":"AJOP2LMO45JE","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_16","alias_value":"AJOP2LMO45JELL32","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_8","alias_value":"AJOP2LMO","created_at":"2026-05-20T00:02:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AJOP2LMO45JELL324FTHE7XWUD","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16480","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.BM","submitted_at":"2026-05-15T17:44:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"94ca4ad630776d953f42e36b87e77b7d7f40f80707668682182271bc1a939dae","abstract_canon_sha256":"78e6f7213125c1641475524bffea4e4df0e068cb519dbc53f2b99d300786c270"},"schema_version":"1.0"},"canonical_sha256":"025cfd2d8ee75245af7ae166727ef6a0ff5287b2feb8be4d271e69c004e7c266","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:24.235821Z","signature_b64":"4oaadsYozY7FjMAn/LtXXSYgk8ALt+/YoTvn0+F7vdBddGjrMnAq0TdP3JWsatWsuc95zQRZXgkOeTI9zgtHBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"025cfd2d8ee75245af7ae166727ef6a0ff5287b2feb8be4d271e69c004e7c266","last_reissued_at":"2026-05-20T00:02:24.235032Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:24.235032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16480","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":"2026-05-20T00:02:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fHMB1pc9AyslTAlJyHGKQ28Nzb3DufbVbRaV20zor2IWGFlMjPiROHfHadwoHZGWc5HRU7cpZu9TdcFXZYW5CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:02:07.764004Z"},"content_sha256":"49ac36f74f412045bf7ae83831d9ddab21379a5409e97200426a6aa2bc64b6e2","schema_version":"1.0","event_id":"sha256:49ac36f74f412045bf7ae83831d9ddab21379a5409e97200426a6aa2bc64b6e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AJOP2LMO45JELL324FTHE7XWUD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MoleCode unlocks structural intelligence in large language models","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings.","cross_cats":["cs.AI"],"primary_cat":"q-bio.BM","authors_text":"Boxuan Zhao, Chen Liu, Fanyang Mo, Hao Li, Jixiang Zhao, Kaiqing Lin, Liuzhenghao Lv, Li Yuan, Shanzhuo Zhang, Yimi Wang, Zhiyuan Yan","submitted_at":"2026-05-15T17:44:27Z","abstract_excerpt":"Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses atoms, bonds, branches and rings into a compact sequence in which topology is implicit, forcing LLMs to reconstruct molecular structure before performing the requested chemical operation. Here we introduce MoleCode, an LLM-native, training-free, graph-explicit molecular language in which all molecular components are represented as typed entities with persistent identifiers and explicit relations. MoleCode makes molec"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an LLM to operate on structure rather than recover it from syntax.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That frontier LLMs can immediately leverage the explicit Subgraph-Node-Edge grammar in prompts for improved reasoning without any training or fine-tuning, and that observed gains stem specifically from structural access rather than prompt length or other variables.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9f53fa8e24ecd9989e7fe0994890f60441482db045e537b4ec064c87db3b403d"},"source":{"id":"2605.16480","kind":"arxiv","version":1},"verdict":{"id":"21a50d9b-10d0-41ec-942f-8806fee0b0e7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:34:51.396221Z","strongest_claim":"MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an LLM to operate on structure rather than recover it from syntax.","one_line_summary":"MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That frontier LLMs can immediately leverage the explicit Subgraph-Node-Edge grammar in prompts for improved reasoning without any training or fine-tuning, and that observed gains stem specifically from structural access rather than prompt length or other variables.","pith_extraction_headline":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16480/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.215207Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:41:21.916494Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.110981Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:21:57.035161Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"e98367529e4c0a414fbde23364b7059aa05f8bde301d7cdee88f9601d6f726a8"},"references":{"count":60,"sample":[{"doi":"","year":2025,"title":"A survey on large language models in biology and chemistry","work_id":"68571fc3-630e-475f-80bf-3a40961495f7","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Large language models as molecular design engines","work_id":"6199e2c8-591e-4370-a725-a59c08c8c816","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Llamo: Large language model-based molecular graph assistant","work_id":"4976c0e1-a8ba-4e78-b11e-60b60e5c996f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists","work_id":"355cd5f1-c18c-4253-8456-ab50af239723","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"arXiv preprint arXiv:2204.11817 , year=","work_id":"8387d3c7-afc3-49b0-ab16-b7f7898f2645","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":60,"snapshot_sha256":"032c0af53b7a6b2466ac92ee4a07767ae3f9909cf015a478c7160b404f29de97","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"f872f4c400b8d3c6e36b52a00555ac81de340c061d666c6aa6c8ba6ba669ac2b"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"21a50d9b-10d0-41ec-942f-8806fee0b0e7"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LcIHWyZ+3njGpNvfDHxw+pIf6vbKvg1io6mkfaJh3T0IJFc2Mc8K6e2M13OFdZRoM05XOSgGiViu+DT4mo5oDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:02:07.764658Z"},"content_sha256":"bfead65b8f0a9829202865d5f77079993de5de0959f7267c97c1f9d431d0ac66","schema_version":"1.0","event_id":"sha256:bfead65b8f0a9829202865d5f77079993de5de0959f7267c97c1f9d431d0ac66"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJOP2LMO45JELL324FTHE7XWUD/bundle.json","state_url":"https://pith.science/pith/AJOP2LMO45JELL324FTHE7XWUD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJOP2LMO45JELL324FTHE7XWUD/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":"2026-05-28T03:02:07Z","links":{"resolver":"https://pith.science/pith/AJOP2LMO45JELL324FTHE7XWUD","bundle":"https://pith.science/pith/AJOP2LMO45JELL324FTHE7XWUD/bundle.json","state":"https://pith.science/pith/AJOP2LMO45JELL324FTHE7XWUD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJOP2LMO45JELL324FTHE7XWUD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AJOP2LMO45JELL324FTHE7XWUD","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":"78e6f7213125c1641475524bffea4e4df0e068cb519dbc53f2b99d300786c270","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.BM","submitted_at":"2026-05-15T17:44:27Z","title_canon_sha256":"94ca4ad630776d953f42e36b87e77b7d7f40f80707668682182271bc1a939dae"},"schema_version":"1.0","source":{"id":"2605.16480","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16480","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16480v1","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16480","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_12","alias_value":"AJOP2LMO45JE","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_16","alias_value":"AJOP2LMO45JELL32","created_at":"2026-05-20T00:02:24Z"},{"alias_kind":"pith_short_8","alias_value":"AJOP2LMO","created_at":"2026-05-20T00:02:24Z"}],"graph_snapshots":[{"event_id":"sha256:bfead65b8f0a9829202865d5f77079993de5de0959f7267c97c1f9d431d0ac66","target":"graph","created_at":"2026-05-20T00:02:24Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an LLM to operate on structure rather than recover it from syntax."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That frontier LLMs can immediately leverage the explicit Subgraph-Node-Edge grammar in prompts for improved reasoning without any training or fine-tuning, and that observed gains stem specifically from structural access rather than prompt length or other variables."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings."}],"snapshot_sha256":"9f53fa8e24ecd9989e7fe0994890f60441482db045e537b4ec064c87db3b403d"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"f872f4c400b8d3c6e36b52a00555ac81de340c061d666c6aa6c8ba6ba669ac2b"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.215207Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:41:21.916494Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.110981Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:21:57.035161Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16480/integrity.json","findings":[],"snapshot_sha256":"e98367529e4c0a414fbde23364b7059aa05f8bde301d7cdee88f9601d6f726a8","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses atoms, bonds, branches and rings into a compact sequence in which topology is implicit, forcing LLMs to reconstruct molecular structure before performing the requested chemical operation. Here we introduce MoleCode, an LLM-native, training-free, graph-explicit molecular language in which all molecular components are represented as typed entities with persistent identifiers and explicit relations. MoleCode makes molec","authors_text":"Boxuan Zhao, Chen Liu, Fanyang Mo, Hao Li, Jixiang Zhao, Kaiqing Lin, Liuzhenghao Lv, Li Yuan, Shanzhuo Zhang, Yimi Wang, Zhiyuan Yan","cross_cats":["cs.AI"],"headline":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings.","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.BM","submitted_at":"2026-05-15T17:44:27Z","title":"MoleCode unlocks structural intelligence in large language models"},"references":{"count":60,"internal_anchors":2,"resolved_work":60,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A survey on large language models in biology and chemistry","work_id":"68571fc3-630e-475f-80bf-3a40961495f7","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Large language models as molecular design engines","work_id":"6199e2c8-591e-4370-a725-a59c08c8c816","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Llamo: Large language model-based molecular graph assistant","work_id":"4976c0e1-a8ba-4e78-b11e-60b60e5c996f","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists","work_id":"355cd5f1-c18c-4253-8456-ab50af239723","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"arXiv preprint arXiv:2204.11817 , year=","work_id":"8387d3c7-afc3-49b0-ab16-b7f7898f2645","year":2022}],"snapshot_sha256":"032c0af53b7a6b2466ac92ee4a07767ae3f9909cf015a478c7160b404f29de97"},"source":{"id":"2605.16480","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T21:34:51.396221Z","id":"21a50d9b-10d0-41ec-942f-8806fee0b0e7","model_set":{"reader":"grok-4.3"},"one_line_summary":"MoleCode is a training-free, LLM-native representation that makes molecular graphs with explicit atoms, bonds, and topology directly readable and editable in language models, improving structural tasks over implicit string encodings.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MoleCode makes molecular topology directly readable, editable and auditable by LLMs instead of hidden in SMILES strings.","strongest_claim":"MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an LLM to operate on structure rather than recover it from syntax.","weakest_assumption":"That frontier LLMs can immediately leverage the explicit Subgraph-Node-Edge grammar in prompts for improved reasoning without any training or fine-tuning, and that observed gains stem specifically from structural access rather than prompt length or other variables."}},"verdict_id":"21a50d9b-10d0-41ec-942f-8806fee0b0e7"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:49ac36f74f412045bf7ae83831d9ddab21379a5409e97200426a6aa2bc64b6e2","target":"record","created_at":"2026-05-20T00:02:24Z","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":"78e6f7213125c1641475524bffea4e4df0e068cb519dbc53f2b99d300786c270","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.BM","submitted_at":"2026-05-15T17:44:27Z","title_canon_sha256":"94ca4ad630776d953f42e36b87e77b7d7f40f80707668682182271bc1a939dae"},"schema_version":"1.0","source":{"id":"2605.16480","kind":"arxiv","version":1}},"canonical_sha256":"025cfd2d8ee75245af7ae166727ef6a0ff5287b2feb8be4d271e69c004e7c266","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"025cfd2d8ee75245af7ae166727ef6a0ff5287b2feb8be4d271e69c004e7c266","first_computed_at":"2026-05-20T00:02:24.235032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:24.235032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4oaadsYozY7FjMAn/LtXXSYgk8ALt+/YoTvn0+F7vdBddGjrMnAq0TdP3JWsatWsuc95zQRZXgkOeTI9zgtHBw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:24.235821Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16480","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49ac36f74f412045bf7ae83831d9ddab21379a5409e97200426a6aa2bc64b6e2","sha256:bfead65b8f0a9829202865d5f77079993de5de0959f7267c97c1f9d431d0ac66"],"state_sha256":"8d580d72f1b21a9e4657d39ff3d1540d1e7d57c38f04c8674efe391537b18c08"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tBcIhLRLLNCZ/SzNj0Y42v9q0paTbIVT0w+ZmJtaxJneL30QWNJTtBkGumYQabPANLfOdKo8EaHYRho4ZPekDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T03:02:07.767556Z","bundle_sha256":"01032d9ea35ac9ab241a66f2b2d1be248080b9733aadcc8340594eca5640863a"}}