{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EPU62UVUSCLW2OPHCTIC22EEAL","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":"46aad155ddd4146cb9910d8b8b7a3ee12c2b0954110c68e02dcf3289a20e9cbc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T04:09:56Z","title_canon_sha256":"9c85eda2a10acb8ec71f3f2141d53906bec8d740643a13f3f38357bd8de67077"},"schema_version":"1.0","source":{"id":"2501.15065","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15065","created_at":"2026-07-05T10:05:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15065v1","created_at":"2026-07-05T10:05:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15065","created_at":"2026-07-05T10:05:23Z"},{"alias_kind":"pith_short_12","alias_value":"EPU62UVUSCLW","created_at":"2026-07-05T10:05:23Z"},{"alias_kind":"pith_short_16","alias_value":"EPU62UVUSCLW2OPH","created_at":"2026-07-05T10:05:23Z"},{"alias_kind":"pith_short_8","alias_value":"EPU62UVU","created_at":"2026-07-05T10:05:23Z"}],"graph_snapshots":[{"event_id":"sha256:bba8a72a96790e56269bb5f36c0b57753e7b55d6e819f08863e547a7ba90632d","target":"graph","created_at":"2026-07-05T10:05:23Z","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/2501.15065/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task model merging offers an efficient solution for integrating knowledge from multiple fine-tuned models, mitigating the significant computational and storage demands associated with multi-task training. As a key technique in this field, Task Arithmetic (TA) defines task vectors by subtracting the pre-trained model ($\\theta_{\\text{pre}}$) from the fine-tuned task models in parameter space, then adjusting the weight between these task vectors and $\\theta_{\\text{pre}}$ to balance task-generalized and task-specific knowledge. Despite the promising performance of TA, conflicts can arise amo","authors_text":"Boyang Li, Qingyong Li, Wenju Sun, Wen Wang, Yangli-ao Geng","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T04:09:56Z","title":"Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15065","kind":"arxiv","version":1},"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:bea790f93379a3d775cc0b69998596de83855f95c7ef0f2ffdb2a539c2cb3a80","target":"record","created_at":"2026-07-05T10:05:23Z","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":"46aad155ddd4146cb9910d8b8b7a3ee12c2b0954110c68e02dcf3289a20e9cbc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-25T04:09:56Z","title_canon_sha256":"9c85eda2a10acb8ec71f3f2141d53906bec8d740643a13f3f38357bd8de67077"},"schema_version":"1.0","source":{"id":"2501.15065","kind":"arxiv","version":1}},"canonical_sha256":"23e9ed52b490976d39e714d02d688402f0ff6dc2a4b9a44f54e5288af874a053","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23e9ed52b490976d39e714d02d688402f0ff6dc2a4b9a44f54e5288af874a053","first_computed_at":"2026-07-05T10:05:23.831638Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:23.831638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w3nfN9ttOHFtCZ2hXE5MxsQ0gUzu2HeLw2yVxnfA8rt8Hx5yqRRNiznV3OkQSvPwMmu0b+fdPJxA70z1IJzqAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:23.832115Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.15065","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bea790f93379a3d775cc0b69998596de83855f95c7ef0f2ffdb2a539c2cb3a80","sha256:bba8a72a96790e56269bb5f36c0b57753e7b55d6e819f08863e547a7ba90632d"],"state_sha256":"0f7e8bb3822d38937f1d10c85b0eb2ef6f6a177f517d92e78a80625504842562"}