{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TTVNHS6QGZYFRNW7FNEEL7R636","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":"edff6ce5496a8fa056300c4121ec03e8cb4779dd6b5ebb2c1f5da6264543877f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-05T02:37:10Z","title_canon_sha256":"9538d2ac05bad3f99cd65eb1e4675e2dca927d261f861d7490fad8bd6541ce10"},"schema_version":"1.0","source":{"id":"2506.04567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04567","created_at":"2026-07-05T11:16:16Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04567v1","created_at":"2026-07-05T11:16:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04567","created_at":"2026-07-05T11:16:16Z"},{"alias_kind":"pith_short_12","alias_value":"TTVNHS6QGZYF","created_at":"2026-07-05T11:16:16Z"},{"alias_kind":"pith_short_16","alias_value":"TTVNHS6QGZYFRNW7","created_at":"2026-07-05T11:16:16Z"},{"alias_kind":"pith_short_8","alias_value":"TTVNHS6Q","created_at":"2026-07-05T11:16:16Z"}],"graph_snapshots":[{"event_id":"sha256:4eb1f21fd47d346a9882e4a9a01315af33df5f0981c2dff8db8d2dc41b1a4382","target":"graph","created_at":"2026-07-05T11:16:16Z","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/2506.04567/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model merging has emerged as a promising solution to accommodate multiple large models within constrained memory budgets. We present StatsMerging, a novel lightweight learning-based model merging method guided by weight distribution statistics without requiring ground truth labels or test samples. StatsMerging offers three key advantages: (1) It uniquely leverages singular values from singular value decomposition (SVD) to capture task-specific weight distributions, serving as a proxy for task importance to guide task coefficient prediction; (2) It employs a lightweight learner StatsMergeLearne","authors_text":"Bryan Bo Cao, Ranjith Merugu, Shubham Jain","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-05T02:37:10Z","title":"StatsMerging: Statistics-Guided Model Merging via Task-Specific Teacher Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04567","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":"sha256:6c0719a029d470e36b95599239cd2b3dd5a4ec6312486c06af2c776f0fb5d386","target":"record","created_at":"2026-07-05T11:16:16Z","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":"edff6ce5496a8fa056300c4121ec03e8cb4779dd6b5ebb2c1f5da6264543877f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-05T02:37:10Z","title_canon_sha256":"9538d2ac05bad3f99cd65eb1e4675e2dca927d261f861d7490fad8bd6541ce10"},"schema_version":"1.0","source":{"id":"2506.04567","kind":"arxiv","version":1}},"canonical_sha256":"9cead3cbd0367058b6df2b4845fe3edf9e2d8a89a01d41f4a6d42959c21eb7ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9cead3cbd0367058b6df2b4845fe3edf9e2d8a89a01d41f4a6d42959c21eb7ea","first_computed_at":"2026-07-05T11:16:16.714150Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:16.714150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2uVxdAGHj0kEZakX7yaJg91ROQHJPZs8s5zrlQ2o5sghuLHaTBJx6QuFTgFLUwV2UgnJxMggHoSBI0EphrcOCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:16.714609Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.04567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c0719a029d470e36b95599239cd2b3dd5a4ec6312486c06af2c776f0fb5d386","sha256:4eb1f21fd47d346a9882e4a9a01315af33df5f0981c2dff8db8d2dc41b1a4382"],"state_sha256":"c9ef3587a86fef1da6c967c508201fbff2c236379031d7dcb5e08e7beeed76ea"}