{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:M6AVXTTV5VBAZ7TBRQ2WHS5A35","short_pith_number":"pith:M6AVXTTV","canonical_record":{"source":{"id":"2502.00832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:05:23Z","cross_cats_sorted":[],"title_canon_sha256":"8ce7cc967da51a70c13a7e5b186bd0d2d217c6f756945fb83d134b1fecab1190","abstract_canon_sha256":"e03dbb26fe2c679b31ff94337907dd17028cc723d63c400649dd56e0e6b674b0"},"schema_version":"1.0"},"canonical_sha256":"67815bce75ed420cfe618c3563cba0df7386b6df030df45485ce75f44554814e","source":{"kind":"arxiv","id":"2502.00832","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00832","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00832v1","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00832","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_12","alias_value":"M6AVXTTV5VBA","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_16","alias_value":"M6AVXTTV5VBAZ7TB","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_8","alias_value":"M6AVXTTV","created_at":"2026-07-05T10:08:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:M6AVXTTV5VBAZ7TBRQ2WHS5A35","target":"record","payload":{"canonical_record":{"source":{"id":"2502.00832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:05:23Z","cross_cats_sorted":[],"title_canon_sha256":"8ce7cc967da51a70c13a7e5b186bd0d2d217c6f756945fb83d134b1fecab1190","abstract_canon_sha256":"e03dbb26fe2c679b31ff94337907dd17028cc723d63c400649dd56e0e6b674b0"},"schema_version":"1.0"},"canonical_sha256":"67815bce75ed420cfe618c3563cba0df7386b6df030df45485ce75f44554814e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:39.039499Z","signature_b64":"6KAOjbzYQHbOtd2SdArSnems8SvwP0GLoBQXfIYieyu3QayjVzLSV+n2PatQqKDVsrSchbKEZszGu7KKOuH2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67815bce75ed420cfe618c3563cba0df7386b6df030df45485ce75f44554814e","last_reissued_at":"2026-07-05T10:08:39.039066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:39.039066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.00832","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":"2026-07-05T10:08:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/0/ytclxX9Nmsbje+Qm4u1IJO+jkQ6M7VLsP6tzjBbm047xpmiY6u0/YnhfH4m11LyFyKwCzB7gDi272NdCcAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:34:20.071462Z"},"content_sha256":"0b2bcaab7198ecc0eb95d9d95dd8f66e94d67842ee335b059bf347007444b21c","schema_version":"1.0","event_id":"sha256:0b2bcaab7198ecc0eb95d9d95dd8f66e94d67842ee335b059bf347007444b21c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:M6AVXTTV5VBAZ7TBRQ2WHS5A35","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalization of Medical Large Language Models through Cross-Domain Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eric Gonzalez, Harrison Fuller, Robert Long","submitted_at":"2025-02-02T16:05:23Z","abstract_excerpt":"The advancement of large language models (LLMs) has opened new frontiers in natural language processing, particularly in specialized domains like healthcare. In this paper, we propose the Incremental Curriculum-Based Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical large language models (MLLMs). ICFT combines curriculum-based learning, dual-stage memory coordination, and parameter-efficient fine-tuning to enable a progressive transition from general linguistic knowledge to strong domain-specific expertise. Experimental results across diverse medical NLP tasks, inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00832","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/2502.00832/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":"2026-07-05T10:08:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g68AArq++djWJem255H0a6EIHnBOLGWLxN1C3O7KbQR8DHvd7SJbfcqZiDgOioP1gtOMsTIBkoKJhqiAY1ZwBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:34:20.071951Z"},"content_sha256":"d48be981db8275f6df5015013c249410e760492b26964a94421b21a2c3002652","schema_version":"1.0","event_id":"sha256:d48be981db8275f6df5015013c249410e760492b26964a94421b21a2c3002652"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/bundle.json","state_url":"https://pith.science/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/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":"2026-08-08T21:34:20Z","links":{"resolver":"https://pith.science/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35","bundle":"https://pith.science/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/bundle.json","state":"https://pith.science/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M6AVXTTV5VBAZ7TBRQ2WHS5A35/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:M6AVXTTV5VBAZ7TBRQ2WHS5A35","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":"e03dbb26fe2c679b31ff94337907dd17028cc723d63c400649dd56e0e6b674b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:05:23Z","title_canon_sha256":"8ce7cc967da51a70c13a7e5b186bd0d2d217c6f756945fb83d134b1fecab1190"},"schema_version":"1.0","source":{"id":"2502.00832","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00832","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00832v1","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00832","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_12","alias_value":"M6AVXTTV5VBA","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_16","alias_value":"M6AVXTTV5VBAZ7TB","created_at":"2026-07-05T10:08:39Z"},{"alias_kind":"pith_short_8","alias_value":"M6AVXTTV","created_at":"2026-07-05T10:08:39Z"}],"graph_snapshots":[{"event_id":"sha256:d48be981db8275f6df5015013c249410e760492b26964a94421b21a2c3002652","target":"graph","created_at":"2026-07-05T10:08:39Z","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/2502.00832/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advancement of large language models (LLMs) has opened new frontiers in natural language processing, particularly in specialized domains like healthcare. In this paper, we propose the Incremental Curriculum-Based Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical large language models (MLLMs). ICFT combines curriculum-based learning, dual-stage memory coordination, and parameter-efficient fine-tuning to enable a progressive transition from general linguistic knowledge to strong domain-specific expertise. Experimental results across diverse medical NLP tasks, inc","authors_text":"Eric Gonzalez, Harrison Fuller, Robert Long","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:05:23Z","title":"Generalization of Medical Large Language Models through Cross-Domain Weak Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00832","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":"sha256:0b2bcaab7198ecc0eb95d9d95dd8f66e94d67842ee335b059bf347007444b21c","target":"record","created_at":"2026-07-05T10:08:39Z","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":"e03dbb26fe2c679b31ff94337907dd17028cc723d63c400649dd56e0e6b674b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:05:23Z","title_canon_sha256":"8ce7cc967da51a70c13a7e5b186bd0d2d217c6f756945fb83d134b1fecab1190"},"schema_version":"1.0","source":{"id":"2502.00832","kind":"arxiv","version":1}},"canonical_sha256":"67815bce75ed420cfe618c3563cba0df7386b6df030df45485ce75f44554814e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67815bce75ed420cfe618c3563cba0df7386b6df030df45485ce75f44554814e","first_computed_at":"2026-07-05T10:08:39.039066Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:08:39.039066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6KAOjbzYQHbOtd2SdArSnems8SvwP0GLoBQXfIYieyu3QayjVzLSV+n2PatQqKDVsrSchbKEZszGu7KKOuH2DA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:08:39.039499Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.00832","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b2bcaab7198ecc0eb95d9d95dd8f66e94d67842ee335b059bf347007444b21c","sha256:d48be981db8275f6df5015013c249410e760492b26964a94421b21a2c3002652"],"state_sha256":"5ebc775603213c1c0c428f55d20626b05fc30cdf79f53d6a1cdd137f88a9db9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m3coapGEC3a5yiARFzdZ8FwF2r4A7n79zBaNlIxRODjXHK5fQewgBL5CNycjbEotawhMBxnfO114NqE59rQiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T21:34:20.076975Z","bundle_sha256":"b4a4ab4fa4ed21e9903921f828771a1610364ef8368c08a70f3779fb48071a9f"}}