{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MRS4WZ24HR4ELLVBHQ5VBI7JIW","short_pith_number":"pith:MRS4WZ24","canonical_record":{"source":{"id":"2501.02869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-06T09:22:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aba38614a74d772b48ab1971dfb03588c515bfb8e1abdb50dc953602894f0c2c","abstract_canon_sha256":"39a743b512195de839d657b208979e0b0007391ddbb26feba09f7bd60b05f218"},"schema_version":"1.0"},"canonical_sha256":"6465cb675c3c7845aea13c3b50a3e945af24ab8164ce94cfb84edf8eb5d6a424","source":{"kind":"arxiv","id":"2501.02869","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.02869","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"arxiv_version","alias_value":"2501.02869v1","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02869","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_12","alias_value":"MRS4WZ24HR4E","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_16","alias_value":"MRS4WZ24HR4ELLVB","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_8","alias_value":"MRS4WZ24","created_at":"2026-07-05T09:57:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MRS4WZ24HR4ELLVBHQ5VBI7JIW","target":"record","payload":{"canonical_record":{"source":{"id":"2501.02869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-06T09:22:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aba38614a74d772b48ab1971dfb03588c515bfb8e1abdb50dc953602894f0c2c","abstract_canon_sha256":"39a743b512195de839d657b208979e0b0007391ddbb26feba09f7bd60b05f218"},"schema_version":"1.0"},"canonical_sha256":"6465cb675c3c7845aea13c3b50a3e945af24ab8164ce94cfb84edf8eb5d6a424","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:26.370881Z","signature_b64":"8tv3sNyo2/z/sH8lYrEnYiCZOzY3XScmTPNURlCo0hOStFq1JR1JXx9vQ336JRSl9cVSKeElSYKiXA17fBolAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6465cb675c3c7845aea13c3b50a3e945af24ab8164ce94cfb84edf8eb5d6a424","last_reissued_at":"2026-07-05T09:57:26.370337Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:26.370337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.02869","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":"2026-07-05T09:57:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jm6552ou2BqXiOCNihkrpJxmDB2j8Ef13TRb/oiU7LBeNDEUhXKpmlWF9YzGtP5TCVMH8/3YyQbMfMeml9PhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T11:29:56.772191Z"},"content_sha256":"6b67b12289d48db08d00f3fb66683181ce060b6a26621a8df536c3d73b3a688a","schema_version":"1.0","event_id":"sha256:6b67b12289d48db08d00f3fb66683181ce060b6a26621a8df536c3d73b3a688a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MRS4WZ24HR4ELLVBHQ5VBI7JIW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kang Yuan, MengXing Ren, Wentao Cai, Yiming Zhang, Yining Sun, Zenghui Ding, Zheng Chang","submitted_at":"2025-01-06T09:22:36Z","abstract_excerpt":"Recent researches of large language models(LLM), which is pre-trained on massive general-purpose corpora, have achieved breakthroughs in responding human queries. However, these methods face challenges including limited data insufficiency to support extensive pre-training and can not align responses with users' instructions. To address these issues, we introduce a medical instruction dataset, CMedINS, containing six medical instructions derived from actual medical tasks, which effectively fine-tunes LLM in conjunction with other data. Subsequently, We launch our medical model, IIMedGPT, employ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02869","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/2501.02869/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":"2026-07-05T09:57:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uZ7CtZ48pj+o3Ltwd0kyXxjboGK3sV+na/Zsj5vBwDWIbOoMj5WbBPsr3qu+vlnNcLaS1P7UxeotuqZNGqhiCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T11:29:56.773767Z"},"content_sha256":"c8e3db557a9068d519eb684df55fa5fc2fb1231cf4c2b4262af405181133f532","schema_version":"1.0","event_id":"sha256:c8e3db557a9068d519eb684df55fa5fc2fb1231cf4c2b4262af405181133f532"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/bundle.json","state_url":"https://pith.science/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/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":"2026-08-11T11:29:56Z","links":{"resolver":"https://pith.science/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW","bundle":"https://pith.science/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/bundle.json","state":"https://pith.science/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MRS4WZ24HR4ELLVBHQ5VBI7JIW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MRS4WZ24HR4ELLVBHQ5VBI7JIW","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":"39a743b512195de839d657b208979e0b0007391ddbb26feba09f7bd60b05f218","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-06T09:22:36Z","title_canon_sha256":"aba38614a74d772b48ab1971dfb03588c515bfb8e1abdb50dc953602894f0c2c"},"schema_version":"1.0","source":{"id":"2501.02869","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.02869","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"arxiv_version","alias_value":"2501.02869v1","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02869","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_12","alias_value":"MRS4WZ24HR4E","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_16","alias_value":"MRS4WZ24HR4ELLVB","created_at":"2026-07-05T09:57:26Z"},{"alias_kind":"pith_short_8","alias_value":"MRS4WZ24","created_at":"2026-07-05T09:57:26Z"}],"graph_snapshots":[{"event_id":"sha256:c8e3db557a9068d519eb684df55fa5fc2fb1231cf4c2b4262af405181133f532","target":"graph","created_at":"2026-07-05T09:57:26Z","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/2501.02869/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent researches of large language models(LLM), which is pre-trained on massive general-purpose corpora, have achieved breakthroughs in responding human queries. However, these methods face challenges including limited data insufficiency to support extensive pre-training and can not align responses with users' instructions. To address these issues, we introduce a medical instruction dataset, CMedINS, containing six medical instructions derived from actual medical tasks, which effectively fine-tunes LLM in conjunction with other data. Subsequently, We launch our medical model, IIMedGPT, employ","authors_text":"Kang Yuan, MengXing Ren, Wentao Cai, Yiming Zhang, Yining Sun, Zenghui Ding, Zheng Chang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-06T09:22:36Z","title":"IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02869","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":"sha256:6b67b12289d48db08d00f3fb66683181ce060b6a26621a8df536c3d73b3a688a","target":"record","created_at":"2026-07-05T09:57:26Z","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":"39a743b512195de839d657b208979e0b0007391ddbb26feba09f7bd60b05f218","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-06T09:22:36Z","title_canon_sha256":"aba38614a74d772b48ab1971dfb03588c515bfb8e1abdb50dc953602894f0c2c"},"schema_version":"1.0","source":{"id":"2501.02869","kind":"arxiv","version":1}},"canonical_sha256":"6465cb675c3c7845aea13c3b50a3e945af24ab8164ce94cfb84edf8eb5d6a424","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6465cb675c3c7845aea13c3b50a3e945af24ab8164ce94cfb84edf8eb5d6a424","first_computed_at":"2026-07-05T09:57:26.370337Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:26.370337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8tv3sNyo2/z/sH8lYrEnYiCZOzY3XScmTPNURlCo0hOStFq1JR1JXx9vQ336JRSl9cVSKeElSYKiXA17fBolAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:26.370881Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.02869","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b67b12289d48db08d00f3fb66683181ce060b6a26621a8df536c3d73b3a688a","sha256:c8e3db557a9068d519eb684df55fa5fc2fb1231cf4c2b4262af405181133f532"],"state_sha256":"a1b60f0ffcf16c3d8aee734c575539d154148387d2c9b616c7f3973f8556289c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5zlvGGaaxWLehKXyuGSUrs9i3tz7Hu4GSV0jLL52dw1KeMhTpQZJbiZH5REtTNoavG65JBSmv9hT0Dy369PxAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T11:29:56.787216Z","bundle_sha256":"23b317a27a41da4cd58144c4950d48cce75e14142a6bbe66266a53ee59b01abc"}}