{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5DJPUUQQ4RMVG7SOW3CU5UB4LV","short_pith_number":"pith:5DJPUUQQ","canonical_record":{"source":{"id":"2505.24105","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T01:13:22Z","cross_cats_sorted":[],"title_canon_sha256":"098d785e4fc5058fc44a284e4dd3b1f91680ce02a40e181bc85c227f076bd641","abstract_canon_sha256":"174f77e8f88f6ddca5bc5f299bc20c531ac288067fad26a643fb6bd0a6ee69f5"},"schema_version":"1.0"},"canonical_sha256":"e8d2fa5210e459537e4eb6c54ed03c5d51b69511f293b780ad865a099ed45fea","source":{"kind":"arxiv","id":"2505.24105","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24105","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24105v1","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24105","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_12","alias_value":"5DJPUUQQ4RMV","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_16","alias_value":"5DJPUUQQ4RMVG7SO","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_8","alias_value":"5DJPUUQQ","created_at":"2026-07-05T11:12:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5DJPUUQQ4RMVG7SOW3CU5UB4LV","target":"record","payload":{"canonical_record":{"source":{"id":"2505.24105","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T01:13:22Z","cross_cats_sorted":[],"title_canon_sha256":"098d785e4fc5058fc44a284e4dd3b1f91680ce02a40e181bc85c227f076bd641","abstract_canon_sha256":"174f77e8f88f6ddca5bc5f299bc20c531ac288067fad26a643fb6bd0a6ee69f5"},"schema_version":"1.0"},"canonical_sha256":"e8d2fa5210e459537e4eb6c54ed03c5d51b69511f293b780ad865a099ed45fea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:41.949396Z","signature_b64":"E7SYeRuttqOgD0Yx2tgk1+Q6dh3uB6DMPVW6DTtoHm6gvfBefHkowKQLFi0kyiDpX1iUBH3mUFU/iiz0NHh8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8d2fa5210e459537e4eb6c54ed03c5d51b69511f293b780ad865a099ed45fea","last_reissued_at":"2026-07-05T11:12:41.948870Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:41.948870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.24105","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":"2026-07-05T11:12:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kW1y/gDfGRRj0K3L4BMRdQpQD6LAvcBwiWxKEZCM4wqjvSaELNj5dU47h7HkPWIl/QzwvpHQHg9grore3s4ICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:35:55.403571Z"},"content_sha256":"1d757036a1538d95691facb5329f555cbcf35b787f7c0af6c7a39e08c4c9dbf7","schema_version":"1.0","event_id":"sha256:1d757036a1538d95691facb5329f555cbcf35b787f7c0af6c7a39e08c4c9dbf7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5DJPUUQQ4RMVG7SOW3CU5UB4LV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiacheng Lin, Jimeng Sun, Zhenbang Wu","submitted_at":"2025-05-30T01:13:22Z","abstract_excerpt":"We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). While RLVR has succeeded in mathematics and coding, its application to healthcare contexts presents unique challenges due to the specialized knowledge and reasoning required for electronic health record (EHR) interpretation. Our pilot study on the MEDCALC benchmark reveals two key failure modes: (1) misapplied knowledge, where models possess relevant medical knowledge but apply it incorrectly, and (2) missing knowledge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24105","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/2505.24105/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":"2026-07-05T11:12:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/MvW/MqmCtNOpEWwxZV5RdcrIRIAKDzN6npJHdq1yRRyHqyANE/D1h/nz9YY5ujtvGHGKMZBMx4EQAsSwJVdAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:35:55.404928Z"},"content_sha256":"8b99111728fa97b0fd3dbb5dfd82a973868c8ef6603d29bbd2d2a4b17347f9f8","schema_version":"1.0","event_id":"sha256:8b99111728fa97b0fd3dbb5dfd82a973868c8ef6603d29bbd2d2a4b17347f9f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/bundle.json","state_url":"https://pith.science/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/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":"2026-08-08T17:35:55Z","links":{"resolver":"https://pith.science/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV","bundle":"https://pith.science/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/bundle.json","state":"https://pith.science/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5DJPUUQQ4RMVG7SOW3CU5UB4LV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5DJPUUQQ4RMVG7SOW3CU5UB4LV","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":"174f77e8f88f6ddca5bc5f299bc20c531ac288067fad26a643fb6bd0a6ee69f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T01:13:22Z","title_canon_sha256":"098d785e4fc5058fc44a284e4dd3b1f91680ce02a40e181bc85c227f076bd641"},"schema_version":"1.0","source":{"id":"2505.24105","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24105","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24105v1","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24105","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_12","alias_value":"5DJPUUQQ4RMV","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_16","alias_value":"5DJPUUQQ4RMVG7SO","created_at":"2026-07-05T11:12:41Z"},{"alias_kind":"pith_short_8","alias_value":"5DJPUUQQ","created_at":"2026-07-05T11:12:41Z"}],"graph_snapshots":[{"event_id":"sha256:8b99111728fa97b0fd3dbb5dfd82a973868c8ef6603d29bbd2d2a4b17347f9f8","target":"graph","created_at":"2026-07-05T11:12:41Z","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/2505.24105/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). While RLVR has succeeded in mathematics and coding, its application to healthcare contexts presents unique challenges due to the specialized knowledge and reasoning required for electronic health record (EHR) interpretation. Our pilot study on the MEDCALC benchmark reveals two key failure modes: (1) misapplied knowledge, where models possess relevant medical knowledge but apply it incorrectly, and (2) missing knowledge","authors_text":"Jiacheng Lin, Jimeng Sun, Zhenbang Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T01:13:22Z","title":"Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24105","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":"sha256:1d757036a1538d95691facb5329f555cbcf35b787f7c0af6c7a39e08c4c9dbf7","target":"record","created_at":"2026-07-05T11:12:41Z","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":"174f77e8f88f6ddca5bc5f299bc20c531ac288067fad26a643fb6bd0a6ee69f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T01:13:22Z","title_canon_sha256":"098d785e4fc5058fc44a284e4dd3b1f91680ce02a40e181bc85c227f076bd641"},"schema_version":"1.0","source":{"id":"2505.24105","kind":"arxiv","version":1}},"canonical_sha256":"e8d2fa5210e459537e4eb6c54ed03c5d51b69511f293b780ad865a099ed45fea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e8d2fa5210e459537e4eb6c54ed03c5d51b69511f293b780ad865a099ed45fea","first_computed_at":"2026-07-05T11:12:41.948870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:41.948870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E7SYeRuttqOgD0Yx2tgk1+Q6dh3uB6DMPVW6DTtoHm6gvfBefHkowKQLFi0kyiDpX1iUBH3mUFU/iiz0NHh8AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:41.949396Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24105","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d757036a1538d95691facb5329f555cbcf35b787f7c0af6c7a39e08c4c9dbf7","sha256:8b99111728fa97b0fd3dbb5dfd82a973868c8ef6603d29bbd2d2a4b17347f9f8"],"state_sha256":"ca15e2d2c109b7d99e3f7a8f8d812a43e0aac0da451ccd988772f9689675d8e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hNt4tP89ZYLXSMoYjbEOMGlm889fFma2juAsZ2IczPat1g8RUMz0LBwDzm4OAvKuVD+c2xarYM4rbmRRxOO1BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T17:35:55.410964Z","bundle_sha256":"c17e3207cfa98ada7c61721a92cc3c9daae3cadd345a116d16970efee6000d85"}}