{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4FSB426ZSS6QVC2D7USKOXPX2J","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":"ac0bac4b60bb366dc2fc708a03e12f821eaa8777d012bc8a2ac2b5662cfe3f63","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-27T20:08:55Z","title_canon_sha256":"081d77bbea9f175b810036e3ec86234edf0f69e9c38f6502ace66a69c6986dee"},"schema_version":"1.0","source":{"id":"2411.18729","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.18729","created_at":"2026-07-05T09:58:43Z"},{"alias_kind":"arxiv_version","alias_value":"2411.18729v2","created_at":"2026-07-05T09:58:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.18729","created_at":"2026-07-05T09:58:43Z"},{"alias_kind":"pith_short_12","alias_value":"4FSB426ZSS6Q","created_at":"2026-07-05T09:58:43Z"},{"alias_kind":"pith_short_16","alias_value":"4FSB426ZSS6QVC2D","created_at":"2026-07-05T09:58:43Z"},{"alias_kind":"pith_short_8","alias_value":"4FSB426Z","created_at":"2026-07-05T09:58:43Z"}],"graph_snapshots":[{"event_id":"sha256:be6c69f0a1f718baa774cb5e6d7496a2e8c5b0741b32d1d07ba9235f00222afb","target":"graph","created_at":"2026-07-05T09:58:43Z","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/2411.18729/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For example, in Task Arithmetic (TA), adding the fine-tuned weights of different tasks can enhance the model's performance on those tasks, while subtracting them leads to task forgetting. Although TA is highly effective, interference among task still hampers the performance of the merged model. Existing methods for handling conflicts between task generally rely on empirical selection, resulting in suboptimal performance. In ","authors_text":"Chun Yuan, Feng Xiong, Ruifeng Xu, Runxi Cheng, Wang Chen, Yiwen Guo, Zhanqiu Zhang","cross_cats":["cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-27T20:08:55Z","title":"Multi-Task Model Merging via Adaptive Weight Disentanglement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.18729","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":"sha256:701b2adc4c39c8deee1f4b53f414adde8aaf7661fb3347251f40ffc5797bf9c1","target":"record","created_at":"2026-07-05T09:58:43Z","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":"ac0bac4b60bb366dc2fc708a03e12f821eaa8777d012bc8a2ac2b5662cfe3f63","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-27T20:08:55Z","title_canon_sha256":"081d77bbea9f175b810036e3ec86234edf0f69e9c38f6502ace66a69c6986dee"},"schema_version":"1.0","source":{"id":"2411.18729","kind":"arxiv","version":2}},"canonical_sha256":"e1641e6bd994bd0a8b43fd24a75df7d27a2bce7fad66fe13d504853d4af1031e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1641e6bd994bd0a8b43fd24a75df7d27a2bce7fad66fe13d504853d4af1031e","first_computed_at":"2026-07-05T09:58:43.893715Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:43.893715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xmuos2cRhtvowitzSGjADC/cZAyazVAV9GvJKd3aP9QKAjjCWvkZuKzbfP8NdsCsWipQcgsMktwuS9P+9iZbDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:43.894150Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.18729","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:701b2adc4c39c8deee1f4b53f414adde8aaf7661fb3347251f40ffc5797bf9c1","sha256:be6c69f0a1f718baa774cb5e6d7496a2e8c5b0741b32d1d07ba9235f00222afb"],"state_sha256":"36b8f709453f34664354b505023fce27b7252ab2b4f4568626e368cdba6fc009"}