{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CAA5K3D2YKLTK57QGRZMDDSPET","short_pith_number":"pith:CAA5K3D2","schema_version":"1.0","canonical_sha256":"1001d56c7ac2973577f03472c18e4f24f3907852cd23dc6f0ca18b864980990d","source":{"kind":"arxiv","id":"2505.10939","version":2},"attestation_state":"computed","paper":{"title":"GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali Edalat, Mohammadtaha Bagherifard, Sahar Rajabi, Yadollah Yaghoobzadeh","submitted_at":"2025-05-16T07:23:59Z","abstract_excerpt":"Large language models often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge. Yet, we hypothesize that a key limitation remains: the entanglement of general knowledge and task-specific adaptations. To overcome this, we propose a modular framework that disentangles these components by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. By subtracting this general knowledge component from each task-specific module, we obtain residual modules that focus more exclusively on task-relevant informatio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.10939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-16T07:23:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ef0c33c89ba831b2b70b9e516f6c1f2dba6ac330b9fcdf036ef49b065a25ed55","abstract_canon_sha256":"61dfc18c0b3a11e47865cf8708a8a9329b1118f0d087bd6b67671e3d5fef991d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:03.674887Z","signature_b64":"Y6nMnZ0egswDeB4ZRv5uLkfhighdkHZ8k7xM2YCr5e6T43sZU1buzWOcrZXj4zwUU182sLS43XBGg3WvyZ4AAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1001d56c7ac2973577f03472c18e4f24f3907852cd23dc6f0ca18b864980990d","last_reissued_at":"2026-07-05T11:48:03.674304Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:03.674304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali Edalat, Mohammadtaha Bagherifard, Sahar Rajabi, Yadollah Yaghoobzadeh","submitted_at":"2025-05-16T07:23:59Z","abstract_excerpt":"Large language models often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge. Yet, we hypothesize that a key limitation remains: the entanglement of general knowledge and task-specific adaptations. To overcome this, we propose a modular framework that disentangles these components by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. By subtracting this general knowledge component from each task-specific module, we obtain residual modules that focus more exclusively on task-relevant informatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10939","kind":"arxiv","version":2},"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/2505.10939/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.10939","created_at":"2026-07-05T11:48:03.674403+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10939v2","created_at":"2026-07-05T11:48:03.674403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10939","created_at":"2026-07-05T11:48:03.674403+00:00"},{"alias_kind":"pith_short_12","alias_value":"CAA5K3D2YKLT","created_at":"2026-07-05T11:48:03.674403+00:00"},{"alias_kind":"pith_short_16","alias_value":"CAA5K3D2YKLTK57Q","created_at":"2026-07-05T11:48:03.674403+00:00"},{"alias_kind":"pith_short_8","alias_value":"CAA5K3D2","created_at":"2026-07-05T11:48:03.674403+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET","json":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET.json","graph_json":"https://pith.science/api/pith-number/CAA5K3D2YKLTK57QGRZMDDSPET/graph.json","events_json":"https://pith.science/api/pith-number/CAA5K3D2YKLTK57QGRZMDDSPET/events.json","paper":"https://pith.science/paper/CAA5K3D2"},"agent_actions":{"view_html":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET","download_json":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET.json","view_paper":"https://pith.science/paper/CAA5K3D2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10939&json=true","fetch_graph":"https://pith.science/api/pith-number/CAA5K3D2YKLTK57QGRZMDDSPET/graph.json","fetch_events":"https://pith.science/api/pith-number/CAA5K3D2YKLTK57QGRZMDDSPET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET/action/storage_attestation","attest_author":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET/action/author_attestation","sign_citation":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET/action/citation_signature","submit_replication":"https://pith.science/pith/CAA5K3D2YKLTK57QGRZMDDSPET/action/replication_record"}},"created_at":"2026-07-05T11:48:03.674403+00:00","updated_at":"2026-07-05T11:48:03.674403+00:00"}