{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4QVIVSA67ZZJ3XBEXZRYL43KRP","short_pith_number":"pith:4QVIVSA6","canonical_record":{"source":{"id":"2410.09437","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-12T08:32:26Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"2d9841cc2b394b57fa7ab470c589ec4aa12c4f17a36988bf9132efdc484ac2c1","abstract_canon_sha256":"f0a19e69bc575e787280de7ea8e23dc970bdc2568abd29e2f422e048e9448e23"},"schema_version":"1.0"},"canonical_sha256":"e42a8ac81efe729ddc24be6385f36a8bdd3917fac2c3a1f159ec0392c660fac6","source":{"kind":"arxiv","id":"2410.09437","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09437","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09437v3","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09437","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_12","alias_value":"4QVIVSA67ZZJ","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_16","alias_value":"4QVIVSA67ZZJ3XBE","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_8","alias_value":"4QVIVSA6","created_at":"2026-07-05T10:42:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4QVIVSA67ZZJ3XBEXZRYL43KRP","target":"record","payload":{"canonical_record":{"source":{"id":"2410.09437","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-12T08:32:26Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"2d9841cc2b394b57fa7ab470c589ec4aa12c4f17a36988bf9132efdc484ac2c1","abstract_canon_sha256":"f0a19e69bc575e787280de7ea8e23dc970bdc2568abd29e2f422e048e9448e23"},"schema_version":"1.0"},"canonical_sha256":"e42a8ac81efe729ddc24be6385f36a8bdd3917fac2c3a1f159ec0392c660fac6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:37.981789Z","signature_b64":"8/ydd8RLmXSgehaEwmj0trnyDniP9/2eJREbvUlvdo5tGjNqerfYezq49H7Nm/UIEEmEP4YF+MiIueLCmZGSCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e42a8ac81efe729ddc24be6385f36a8bdd3917fac2c3a1f159ec0392c660fac6","last_reissued_at":"2026-07-05T10:42:37.981284Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:37.981284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.09437","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:42:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8CVVk3hPJXiBkIK7wey6cnR5ww4CuNfnvvCcvr4N0GOUU3NBwxhr+CAEMNtP49SOS1ee6HohZq7GXGghActfDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T02:38:00.435903Z"},"content_sha256":"d8d43a3ed14c41c3d40a2d14cb7ea5510ccc16cccb84048b31e8f9f7630d4d05","schema_version":"1.0","event_id":"sha256:d8d43a3ed14c41c3d40a2d14cb7ea5510ccc16cccb84048b31e8f9f7630d4d05"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4QVIVSA67ZZJ3XBEXZRYL43KRP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Denvy Deng, Dilxat Muhtar, Feng Sun, Hao Sun, Jianfeng Liu, Qi Zhang, Weizhu Chen, Yaming Yang, Yelong Shen, Yuefeng Zhan, Yujing Wang, Yunhai Tong","submitted_at":"2024-10-12T08:32:26Z","abstract_excerpt":"Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. However, in multi-task learning (MTL) scenarios, LoRA tends to obscure the distinction between tasks by projecting sparse high-dimensional features from different tasks into the same dense low-dimensional intrinsic space. This leads to task interference and suboptimal performance for LoRA and its variants. To tackle this challenge, we propose MTL-LoRA, which retains the advantages of low-rank adaptation while significa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09437","kind":"arxiv","version":3},"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/2410.09437/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":"2026-07-05T10:42:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2co9KwsFfEI1NJsqcfrxGg3nb5+bIt/4pbtEr/jRwHD2WFkde1vc2nW2Ynwo9X4hot0KDtd80V8NJiPovExdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T02:38:00.436526Z"},"content_sha256":"009882daa982a772b06cf4c610e81165508b8e9a412cc223118591af01304e49","schema_version":"1.0","event_id":"sha256:009882daa982a772b06cf4c610e81165508b8e9a412cc223118591af01304e49"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/bundle.json","state_url":"https://pith.science/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/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":"2026-07-07T02:38:00Z","links":{"resolver":"https://pith.science/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP","bundle":"https://pith.science/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/bundle.json","state":"https://pith.science/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4QVIVSA67ZZJ3XBEXZRYL43KRP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4QVIVSA67ZZJ3XBEXZRYL43KRP","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":"f0a19e69bc575e787280de7ea8e23dc970bdc2568abd29e2f422e048e9448e23","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-12T08:32:26Z","title_canon_sha256":"2d9841cc2b394b57fa7ab470c589ec4aa12c4f17a36988bf9132efdc484ac2c1"},"schema_version":"1.0","source":{"id":"2410.09437","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09437","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09437v3","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09437","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_12","alias_value":"4QVIVSA67ZZJ","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_16","alias_value":"4QVIVSA67ZZJ3XBE","created_at":"2026-07-05T10:42:37Z"},{"alias_kind":"pith_short_8","alias_value":"4QVIVSA6","created_at":"2026-07-05T10:42:37Z"}],"graph_snapshots":[{"event_id":"sha256:009882daa982a772b06cf4c610e81165508b8e9a412cc223118591af01304e49","target":"graph","created_at":"2026-07-05T10:42:37Z","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/2410.09437/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. However, in multi-task learning (MTL) scenarios, LoRA tends to obscure the distinction between tasks by projecting sparse high-dimensional features from different tasks into the same dense low-dimensional intrinsic space. This leads to task interference and suboptimal performance for LoRA and its variants. To tackle this challenge, we propose MTL-LoRA, which retains the advantages of low-rank adaptation while significa","authors_text":"Denvy Deng, Dilxat Muhtar, Feng Sun, Hao Sun, Jianfeng Liu, Qi Zhang, Weizhu Chen, Yaming Yang, Yelong Shen, Yuefeng Zhan, Yujing Wang, Yunhai Tong","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-12T08:32:26Z","title":"MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09437","kind":"arxiv","version":3},"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:d8d43a3ed14c41c3d40a2d14cb7ea5510ccc16cccb84048b31e8f9f7630d4d05","target":"record","created_at":"2026-07-05T10:42:37Z","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":"f0a19e69bc575e787280de7ea8e23dc970bdc2568abd29e2f422e048e9448e23","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-12T08:32:26Z","title_canon_sha256":"2d9841cc2b394b57fa7ab470c589ec4aa12c4f17a36988bf9132efdc484ac2c1"},"schema_version":"1.0","source":{"id":"2410.09437","kind":"arxiv","version":3}},"canonical_sha256":"e42a8ac81efe729ddc24be6385f36a8bdd3917fac2c3a1f159ec0392c660fac6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e42a8ac81efe729ddc24be6385f36a8bdd3917fac2c3a1f159ec0392c660fac6","first_computed_at":"2026-07-05T10:42:37.981284Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:42:37.981284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8/ydd8RLmXSgehaEwmj0trnyDniP9/2eJREbvUlvdo5tGjNqerfYezq49H7Nm/UIEEmEP4YF+MiIueLCmZGSCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:42:37.981789Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.09437","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8d43a3ed14c41c3d40a2d14cb7ea5510ccc16cccb84048b31e8f9f7630d4d05","sha256:009882daa982a772b06cf4c610e81165508b8e9a412cc223118591af01304e49"],"state_sha256":"15b5b0854e81485eeff6fafc902e365a4ed1a57c539a10dc9b2959e9a084f59b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p5evQYH2WKcgWrahDCezeuNvLiVPtZHyPHT074otyQC7L1Kw+x8CaxVKUdnrrd4k7JhiZKl5iNzOoLjC3cotCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T02:38:00.440166Z","bundle_sha256":"28f4d42c26136efbe7f0a57a3f4f281d9a74d6a15726c788c5142466f84bd6d5"}}