{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NMY6ROC3IQE3ICRIDLPKPVIVED","short_pith_number":"pith:NMY6ROC3","canonical_record":{"source":{"id":"2606.25651","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-24T10:07:59Z","cross_cats_sorted":[],"title_canon_sha256":"bb6d43f48bee83db2ad0f8d787907dc78370d2b053f903b6818ea6a053d29258","abstract_canon_sha256":"73dfe0f9df10d592423763ce68214d55c7a87937c87e48ccde9804827718f343"},"schema_version":"1.0"},"canonical_sha256":"6b31e8b85b4409b40a281adea7d51520d26878616ad52366ea1a7093b48552f5","source":{"kind":"arxiv","id":"2606.25651","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.25651","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2606.25651v1","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25651","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"NMY6ROC3IQE3","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"NMY6ROC3IQE3ICRI","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"NMY6ROC3","created_at":"2026-06-25T01:18:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NMY6ROC3IQE3ICRIDLPKPVIVED","target":"record","payload":{"canonical_record":{"source":{"id":"2606.25651","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-24T10:07:59Z","cross_cats_sorted":[],"title_canon_sha256":"bb6d43f48bee83db2ad0f8d787907dc78370d2b053f903b6818ea6a053d29258","abstract_canon_sha256":"73dfe0f9df10d592423763ce68214d55c7a87937c87e48ccde9804827718f343"},"schema_version":"1.0"},"canonical_sha256":"6b31e8b85b4409b40a281adea7d51520d26878616ad52366ea1a7093b48552f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:18:11.684855Z","signature_b64":"3LxNNXrDON5E3VMrRdqNBgVGoEWtepvOaPpLV/oL3FMc0dH26OpfHlqumLghWsHWCKFIznHSjFk2BFvaLS1TBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b31e8b85b4409b40a281adea7d51520d26878616ad52366ea1a7093b48552f5","last_reissued_at":"2026-06-25T01:18:11.684404Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:18:11.684404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.25651","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":"2026-06-25T01:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WG9PqlikI1Xsy/U62ESDiAAOFOU8t2UvRH5a980GPMIMJxg1sABIVQu2zlQjzVqxf+E1SbhVkRmaL/crdYGMAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T22:30:53.621354Z"},"content_sha256":"35ea4365671ef17ce951b01eac307954faa7faa106bb9518e8a7338d05d0c969","schema_version":"1.0","event_id":"sha256:35ea4365671ef17ce951b01eac307954faa7faa106bb9518e8a7338d05d0c969"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NMY6ROC3IQE3ICRIDLPKPVIVED","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Congbo Ma, Farah E. Shamout, Hu Wang, Yichun Zhang","submitted_at":"2026-06-24T10:07:59Z","abstract_excerpt":"As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even minor mistakes can pose risks to patient safety. Existing methods for error detection and correction, including automated checks and heuristic-based approaches, do not generalize well across unseen datasets. In this paper, we propose MedGuards as a medical safety guardrail, which is a new framework that treats medical error detection and correction as a multi-agent in-context learning task. Specialized agents separatel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25651","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/2606.25651/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":"2026-06-25T01:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cALB0Y/8Z/8jALpmRIx5j6HxIIj3lXWfFTkbIoa2xSw7sMJcUbgNhzM1LxJYqzUye5tVWgxol/hDwb9m2eh8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T22:30:53.622007Z"},"content_sha256":"10bdbf8bcd311c463a2ee4a05601bbe868cf79f91d8ae9639c7b9a29a3830460","schema_version":"1.0","event_id":"sha256:10bdbf8bcd311c463a2ee4a05601bbe868cf79f91d8ae9639c7b9a29a3830460"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/bundle.json","state_url":"https://pith.science/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/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":"2026-06-29T22:30:53Z","links":{"resolver":"https://pith.science/pith/NMY6ROC3IQE3ICRIDLPKPVIVED","bundle":"https://pith.science/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/bundle.json","state":"https://pith.science/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NMY6ROC3IQE3ICRIDLPKPVIVED/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NMY6ROC3IQE3ICRIDLPKPVIVED","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":"73dfe0f9df10d592423763ce68214d55c7a87937c87e48ccde9804827718f343","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-24T10:07:59Z","title_canon_sha256":"bb6d43f48bee83db2ad0f8d787907dc78370d2b053f903b6818ea6a053d29258"},"schema_version":"1.0","source":{"id":"2606.25651","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.25651","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2606.25651v1","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25651","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"NMY6ROC3IQE3","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"NMY6ROC3IQE3ICRI","created_at":"2026-06-25T01:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"NMY6ROC3","created_at":"2026-06-25T01:18:11Z"}],"graph_snapshots":[{"event_id":"sha256:10bdbf8bcd311c463a2ee4a05601bbe868cf79f91d8ae9639c7b9a29a3830460","target":"graph","created_at":"2026-06-25T01:18:11Z","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/2606.25651/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even minor mistakes can pose risks to patient safety. Existing methods for error detection and correction, including automated checks and heuristic-based approaches, do not generalize well across unseen datasets. In this paper, we propose MedGuards as a medical safety guardrail, which is a new framework that treats medical error detection and correction as a multi-agent in-context learning task. Specialized agents separatel","authors_text":"Congbo Ma, Farah E. Shamout, Hu Wang, Yichun Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-24T10:07:59Z","title":"MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25651","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":"sha256:35ea4365671ef17ce951b01eac307954faa7faa106bb9518e8a7338d05d0c969","target":"record","created_at":"2026-06-25T01:18:11Z","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":"73dfe0f9df10d592423763ce68214d55c7a87937c87e48ccde9804827718f343","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-24T10:07:59Z","title_canon_sha256":"bb6d43f48bee83db2ad0f8d787907dc78370d2b053f903b6818ea6a053d29258"},"schema_version":"1.0","source":{"id":"2606.25651","kind":"arxiv","version":1}},"canonical_sha256":"6b31e8b85b4409b40a281adea7d51520d26878616ad52366ea1a7093b48552f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b31e8b85b4409b40a281adea7d51520d26878616ad52366ea1a7093b48552f5","first_computed_at":"2026-06-25T01:18:11.684404Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-25T01:18:11.684404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3LxNNXrDON5E3VMrRdqNBgVGoEWtepvOaPpLV/oL3FMc0dH26OpfHlqumLghWsHWCKFIznHSjFk2BFvaLS1TBg==","signature_status":"signed_v1","signed_at":"2026-06-25T01:18:11.684855Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.25651","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35ea4365671ef17ce951b01eac307954faa7faa106bb9518e8a7338d05d0c969","sha256:10bdbf8bcd311c463a2ee4a05601bbe868cf79f91d8ae9639c7b9a29a3830460"],"state_sha256":"99adccc5868b350ab4f61cf5f48b3fe0f132b61e713b8b71925d29678cacf601"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Zajq4ju9YLNPHyRm0/40GyAzF4k0FbGMA6EOxt0dR1nvSm5L1ppdi4eXkmDSOobLUm+02eklxbduIwUwO/XAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T22:30:53.626343Z","bundle_sha256":"fe68deba7477bde42f8af03d6cb052aa8a620b89b8d15fcb258a9cb142315297"}}