{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RWZ2P3RZOQWXC7C7Z4EMTYMR2B","short_pith_number":"pith:RWZ2P3RZ","canonical_record":{"source":{"id":"2508.06163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T09:33:08Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"cabcbf4eda948644d7b6791248559e52118f973a54bf5896fcef4af4dbd49eb6","abstract_canon_sha256":"6de8647113d17af3e8caa9ba7583430a1e66ec562cb05fae216e31c8325bc32e"},"schema_version":"1.0"},"canonical_sha256":"8db3a7ee39742d717c5fcf08c9e191d05f27ecc66ceccdebc15589e3fa091de9","source":{"kind":"arxiv","id":"2508.06163","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.06163","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"arxiv_version","alias_value":"2508.06163v1","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.06163","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_12","alias_value":"RWZ2P3RZOQWX","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_16","alias_value":"RWZ2P3RZOQWXC7C7","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_8","alias_value":"RWZ2P3RZ","created_at":"2026-07-05T11:50:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RWZ2P3RZOQWXC7C7Z4EMTYMR2B","target":"record","payload":{"canonical_record":{"source":{"id":"2508.06163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T09:33:08Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"cabcbf4eda948644d7b6791248559e52118f973a54bf5896fcef4af4dbd49eb6","abstract_canon_sha256":"6de8647113d17af3e8caa9ba7583430a1e66ec562cb05fae216e31c8325bc32e"},"schema_version":"1.0"},"canonical_sha256":"8db3a7ee39742d717c5fcf08c9e191d05f27ecc66ceccdebc15589e3fa091de9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:51.086744Z","signature_b64":"0t/Us4+wCeW1boIMDXdogFTgCVvhaGOhUaDUNI1Hdi/oOFxW7/n00WEDZ5zOnAwWZKSaeNY7HWPvUlxMjc0pAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8db3a7ee39742d717c5fcf08c9e191d05f27ecc66ceccdebc15589e3fa091de9","last_reissued_at":"2026-07-05T11:50:51.086229Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:51.086229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.06163","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:50:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U1WXKodObZ0+saWlWXH8IScn6IWERfHGwNPt40rNwLAsHNEoUL4qCyDNRh5luMARic/hsIEIO2FGSrTnVgRpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T06:06:15.219523Z"},"content_sha256":"e17201214a4950fea4fb5556478b6f085415847c49277a260e83aa564e282157","schema_version":"1.0","event_id":"sha256:e17201214a4950fea4fb5556478b6f085415847c49277a260e83aa564e282157"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RWZ2P3RZOQWXC7C7Z4EMTYMR2B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Anxiang Ma, Bei Li, Dingyang Lin, Jingbo Zhu, Junxin Wang, Kaiyan Chang, Tong Xiao, Tong Zheng, yingfeng luo, Zhengtao Yu, Ziqiang Xu","submitted_at":"2025-08-08T09:33:08Z","abstract_excerpt":"Model merging has emerged as a compelling data-free paradigm for multi-task learning, enabling the fusion of multiple fine-tuned models into a single, powerful entity. A key technique in merging methods is sparsification, which prunes redundant parameters from task vectors to mitigate interference. However, prevailing approaches employ a ``one-size-fits-all'' strategy, applying a uniform sparsity ratio that overlooks the inherent structural and statistical heterogeneity of model parameters. This often leads to a suboptimal trade-off, where critical parameters are inadvertently pruned while les"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.06163","kind":"arxiv","version":1},"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/2508.06163/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":"2026-07-05T11:50:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fx25VaFgQZrFZQ+vmMmNNnQ2RCyRFXhU8SC9ou78Q6iVPH3sIXvWzEdqytMkPYCM4vPVyku1z0kIM7IfKAsfDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T06:06:15.220071Z"},"content_sha256":"b276cd49a4946edead78ca27c4af9f2a2a3fc94b59710db0438792dc4b05a188","schema_version":"1.0","event_id":"sha256:b276cd49a4946edead78ca27c4af9f2a2a3fc94b59710db0438792dc4b05a188"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/bundle.json","state_url":"https://pith.science/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/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":"2026-08-05T06:06:15Z","links":{"resolver":"https://pith.science/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B","bundle":"https://pith.science/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/bundle.json","state":"https://pith.science/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RWZ2P3RZOQWXC7C7Z4EMTYMR2B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RWZ2P3RZOQWXC7C7Z4EMTYMR2B","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":"6de8647113d17af3e8caa9ba7583430a1e66ec562cb05fae216e31c8325bc32e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T09:33:08Z","title_canon_sha256":"cabcbf4eda948644d7b6791248559e52118f973a54bf5896fcef4af4dbd49eb6"},"schema_version":"1.0","source":{"id":"2508.06163","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.06163","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"arxiv_version","alias_value":"2508.06163v1","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.06163","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_12","alias_value":"RWZ2P3RZOQWX","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_16","alias_value":"RWZ2P3RZOQWXC7C7","created_at":"2026-07-05T11:50:51Z"},{"alias_kind":"pith_short_8","alias_value":"RWZ2P3RZ","created_at":"2026-07-05T11:50:51Z"}],"graph_snapshots":[{"event_id":"sha256:b276cd49a4946edead78ca27c4af9f2a2a3fc94b59710db0438792dc4b05a188","target":"graph","created_at":"2026-07-05T11:50:51Z","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/2508.06163/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model merging has emerged as a compelling data-free paradigm for multi-task learning, enabling the fusion of multiple fine-tuned models into a single, powerful entity. A key technique in merging methods is sparsification, which prunes redundant parameters from task vectors to mitigate interference. However, prevailing approaches employ a ``one-size-fits-all'' strategy, applying a uniform sparsity ratio that overlooks the inherent structural and statistical heterogeneity of model parameters. This often leads to a suboptimal trade-off, where critical parameters are inadvertently pruned while les","authors_text":"Anxiang Ma, Bei Li, Dingyang Lin, Jingbo Zhu, Junxin Wang, Kaiyan Chang, Tong Xiao, Tong Zheng, yingfeng luo, Zhengtao Yu, Ziqiang Xu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T09:33:08Z","title":"One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.06163","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:e17201214a4950fea4fb5556478b6f085415847c49277a260e83aa564e282157","target":"record","created_at":"2026-07-05T11:50:51Z","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":"6de8647113d17af3e8caa9ba7583430a1e66ec562cb05fae216e31c8325bc32e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-08-08T09:33:08Z","title_canon_sha256":"cabcbf4eda948644d7b6791248559e52118f973a54bf5896fcef4af4dbd49eb6"},"schema_version":"1.0","source":{"id":"2508.06163","kind":"arxiv","version":1}},"canonical_sha256":"8db3a7ee39742d717c5fcf08c9e191d05f27ecc66ceccdebc15589e3fa091de9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8db3a7ee39742d717c5fcf08c9e191d05f27ecc66ceccdebc15589e3fa091de9","first_computed_at":"2026-07-05T11:50:51.086229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:51.086229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0t/Us4+wCeW1boIMDXdogFTgCVvhaGOhUaDUNI1Hdi/oOFxW7/n00WEDZ5zOnAwWZKSaeNY7HWPvUlxMjc0pAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:51.086744Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.06163","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e17201214a4950fea4fb5556478b6f085415847c49277a260e83aa564e282157","sha256:b276cd49a4946edead78ca27c4af9f2a2a3fc94b59710db0438792dc4b05a188"],"state_sha256":"5d5f951e52a6b1b175b29c111e45b3e501e40f6ec66e0c898fe1225f22bf64b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a1/qRIKAnpcn/Y2mu32vXFPTYHHzOZVa/YJF3mpNz+wcWeXIwNvZ2XNpEecQyEOiSJN79BOI9Meo9VZGCDOvCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T06:06:15.227050Z","bundle_sha256":"16291813a6548344e66aea745a5550f7704ec5cf0baddef24b9a6a2a8b1d9bcc"}}