{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NADBFOOLYGJNGQAEI2IQGK5OVG","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":"9c13e3561321c32109d949b0b59f29adf95ef4e19e4d8ac0bb4d89d56379209a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-17T15:12:47Z","title_canon_sha256":"2f24bfa8859bf9ca1ba1da9d02922bfe6fb99de5582c2c075d732c8a3a222e8e"},"schema_version":"1.0","source":{"id":"2505.12043","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.12043","created_at":"2026-07-05T11:05:45Z"},{"alias_kind":"arxiv_version","alias_value":"2505.12043v2","created_at":"2026-07-05T11:05:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12043","created_at":"2026-07-05T11:05:45Z"},{"alias_kind":"pith_short_12","alias_value":"NADBFOOLYGJN","created_at":"2026-07-05T11:05:45Z"},{"alias_kind":"pith_short_16","alias_value":"NADBFOOLYGJNGQAE","created_at":"2026-07-05T11:05:45Z"},{"alias_kind":"pith_short_8","alias_value":"NADBFOOL","created_at":"2026-07-05T11:05:45Z"}],"graph_snapshots":[{"event_id":"sha256:cd185b6884e4bf9b451d68ab09f62bbbf5d9e8a8087a894b1a337c48dc237710","target":"graph","created_at":"2026-07-05T11:05:45Z","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/2505.12043/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although large language models (LLMs) perform well in general tasks, domain-specific applications suffer from hallucinations and accuracy limitations. Continual Pre-Training (CPT) approaches encounter two key issues: (1) domain-biased data degrades general language skills, and (2) improper corpus-mixture ratios limit effective adaptation. To address these, we propose a novel framework, Mixture of Losses (MoL), which decouples optimization objectives for domain-specific and general corpora. Specifically, cross-entropy (CE) loss is applied to domain-corpus to ensure knowledge acquisition, while ","authors_text":"Jingxue Chen, Qianchun Lu, Qingkun Tang, Siyuan Fang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-17T15:12:47Z","title":"MoL for LLMs: Dual-Loss Optimization to Enhance Domain Expertise While Preserving General Capabilities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12043","kind":"arxiv","version":2},"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":"sha256:5b1795d294f07587a5b81ae099587a8b14c893945da94afee2b75649e6fcd967","target":"record","created_at":"2026-07-05T11:05:45Z","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":"9c13e3561321c32109d949b0b59f29adf95ef4e19e4d8ac0bb4d89d56379209a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-17T15:12:47Z","title_canon_sha256":"2f24bfa8859bf9ca1ba1da9d02922bfe6fb99de5582c2c075d732c8a3a222e8e"},"schema_version":"1.0","source":{"id":"2505.12043","kind":"arxiv","version":2}},"canonical_sha256":"680612b9cbc192d340044691032baea991a71dd336d9ad2210dce4f4916f574c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"680612b9cbc192d340044691032baea991a71dd336d9ad2210dce4f4916f574c","first_computed_at":"2026-07-05T11:05:45.159783Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:45.159783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qmXq6+EWur6F8Vnjz+Qqlldv43L1p+0/cCyxYDEEmgiRGJmPQcnCwtMfyn0v2B8IjjELlR7wUSzpet2Y3YdcCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:45.160247Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.12043","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b1795d294f07587a5b81ae099587a8b14c893945da94afee2b75649e6fcd967","sha256:cd185b6884e4bf9b451d68ab09f62bbbf5d9e8a8087a894b1a337c48dc237710"],"state_sha256":"2380e8d9dee7484667e07aa0fa02210dfbe4956041ae89a5601cf9c350a914f1"}