{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SAC44E7JJD7IIHBNFSEWBLJBSZ","short_pith_number":"pith:SAC44E7J","schema_version":"1.0","canonical_sha256":"9005ce13e948fe841c2d2c8960ad21965119c7c3cd71545be8d2c219bfbc8cb9","source":{"kind":"arxiv","id":"2502.01015","version":5},"attestation_state":"computed","paper":{"title":"Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Han Zhao, Makoto Yamada, Meitong Liu, Siqi Zeng, Weiqiu You, Yao-Hung Hubert Tsai, Yifan Hao, Yifei He","submitted_at":"2025-02-03T03:18:26Z","abstract_excerpt":"Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse settings. However, maintaining a large collection of task vectors introduces scalability challenges in both storage and computation. We propose Task Vector Bases, a framework compressing $T$ task vectors into $M < T$ basis vectors while preserving the functionality of task arithmetic. By representing each task vector as a structured linear combination of basis atoms, our approach supports standard operations such as ad"},"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":"2502.01015","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-03T03:18:26Z","cross_cats_sorted":[],"title_canon_sha256":"cb36645f8f42a78c3844456957b88e0a0106e1c8e8896caeace9c2039216d6ee","abstract_canon_sha256":"67f0ef62066034bcff8ecd7b34d763bf3eb6c226d869f70647838889ce6e1edb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:19.707204Z","signature_b64":"n+R89+VcGCuQusDP45t9lYQvJn22o7AW178revFKjvfxARq3A7uToCpcw/DBfBFEoDIs+zDH72eGKU0Q7vFCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9005ce13e948fe841c2d2c8960ad21965119c7c3cd71545be8d2c219bfbc8cb9","last_reissued_at":"2026-06-24T01:14:19.706705Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:19.706705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Han Zhao, Makoto Yamada, Meitong Liu, Siqi Zeng, Weiqiu You, Yao-Hung Hubert Tsai, Yifan Hao, Yifei He","submitted_at":"2025-02-03T03:18:26Z","abstract_excerpt":"Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse settings. However, maintaining a large collection of task vectors introduces scalability challenges in both storage and computation. We propose Task Vector Bases, a framework compressing $T$ task vectors into $M < T$ basis vectors while preserving the functionality of task arithmetic. By representing each task vector as a structured linear combination of basis atoms, our approach supports standard operations such as ad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01015","kind":"arxiv","version":5},"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/2502.01015/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":"2502.01015","created_at":"2026-06-24T01:14:19.706769+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01015v5","created_at":"2026-06-24T01:14:19.706769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01015","created_at":"2026-06-24T01:14:19.706769+00:00"},{"alias_kind":"pith_short_12","alias_value":"SAC44E7JJD7I","created_at":"2026-06-24T01:14:19.706769+00:00"},{"alias_kind":"pith_short_16","alias_value":"SAC44E7JJD7IIHBN","created_at":"2026-06-24T01:14:19.706769+00:00"},{"alias_kind":"pith_short_8","alias_value":"SAC44E7J","created_at":"2026-06-24T01:14:19.706769+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.25846","citing_title":"On the Limits of Model Merging for Multilinguality in Pre-Training","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2604.17078","citing_title":"Understanding and Enforcing Weight Disentanglement in Task Arithmetic","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ","json":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ.json","graph_json":"https://pith.science/api/pith-number/SAC44E7JJD7IIHBNFSEWBLJBSZ/graph.json","events_json":"https://pith.science/api/pith-number/SAC44E7JJD7IIHBNFSEWBLJBSZ/events.json","paper":"https://pith.science/paper/SAC44E7J"},"agent_actions":{"view_html":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ","download_json":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ.json","view_paper":"https://pith.science/paper/SAC44E7J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01015&json=true","fetch_graph":"https://pith.science/api/pith-number/SAC44E7JJD7IIHBNFSEWBLJBSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/SAC44E7JJD7IIHBNFSEWBLJBSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ/action/storage_attestation","attest_author":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ/action/author_attestation","sign_citation":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ/action/citation_signature","submit_replication":"https://pith.science/pith/SAC44E7JJD7IIHBNFSEWBLJBSZ/action/replication_record"}},"created_at":"2026-06-24T01:14:19.706769+00:00","updated_at":"2026-06-24T01:14:19.706769+00:00"}