{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:G6CH3GWBNAE67V6VA2YWVTVYS6","short_pith_number":"pith:G6CH3GWB","canonical_record":{"source":{"id":"2406.11617","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T15:02:45Z","cross_cats_sorted":[],"title_canon_sha256":"303fa0cdb693015fa7240ff853ea1e92e7e2bbed09908b24a2756e6299a8f290","abstract_canon_sha256":"a9cc9d90ffed8241729e0d6c60bbcbdb40825d2f2750c3db5bbce08e7df9d136"},"schema_version":"1.0"},"canonical_sha256":"37847d9ac16809efd7d506b16aceb8979ad1e3580b999a9fdca08d873c9db4b9","source":{"kind":"arxiv","id":"2406.11617","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11617","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11617v1","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11617","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_12","alias_value":"G6CH3GWBNAE6","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_16","alias_value":"G6CH3GWBNAE67V6V","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_8","alias_value":"G6CH3GWB","created_at":"2026-07-05T08:32:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:G6CH3GWBNAE67V6VA2YWVTVYS6","target":"record","payload":{"canonical_record":{"source":{"id":"2406.11617","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T15:02:45Z","cross_cats_sorted":[],"title_canon_sha256":"303fa0cdb693015fa7240ff853ea1e92e7e2bbed09908b24a2756e6299a8f290","abstract_canon_sha256":"a9cc9d90ffed8241729e0d6c60bbcbdb40825d2f2750c3db5bbce08e7df9d136"},"schema_version":"1.0"},"canonical_sha256":"37847d9ac16809efd7d506b16aceb8979ad1e3580b999a9fdca08d873c9db4b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:55.604122Z","signature_b64":"rpUmD4cuU9l75w3pbLsrr8JoAn9fXsxne8GtsUSBdxp6g5eytxs58BYlaXK2qjW+e5ETns0JsxxAQetniT3QBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37847d9ac16809efd7d506b16aceb8979ad1e3580b999a9fdca08d873c9db4b9","last_reissued_at":"2026-07-05T08:32:55.603473Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:55.603473Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.11617","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":"2026-07-05T08:32:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RQStAIBfxyinqjvUiYhj3qbMVSVh7M/+IEpVrNdvbKJLEwD2YKTVFE22PtLy1Tz9W1cnqgrNaAGb1pqe/W0lBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:05:27.553841Z"},"content_sha256":"25785a4a55697b2b81109722ea345ed297318ce8f6358896a1a765297c5c7268","schema_version":"1.0","event_id":"sha256:25785a4a55697b2b81109722ea345ed297318ce8f6358896a1a765297c5c7268"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:G6CH3GWBNAE67V6VA2YWVTVYS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Pala Tej Deep, Rishabh Bhardwaj, Soujanya Poria","submitted_at":"2024-06-17T15:02:45Z","abstract_excerpt":"With the proliferation of domain-specific models, model merging has emerged as a set of techniques that combine the capabilities of multiple models into one that can multitask without the cost of additional training. In this paper, we propose a new model merging technique, Drop and rEscaLe via sampLing with mAgnitude (DELLA-Merging), that employs a novel pruning technique, MAGPRUNE, which shows significant advantages over DARE and TIES. MAGPRUNE first ranks the parameters in order of their magnitude and assigns higher dropout probabilities (p) to parameters with lower ranks corresponding to lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11617","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/2406.11617/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":"2026-07-05T08:32:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MK35eN2nkOJEmcU5GFw5NYIZKL/rzc5j9On6iniKgQUJdBih1gLe+EEmg2l9gOmMRme2xuhSLGaf2aZUwwhaBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:05:27.554224Z"},"content_sha256":"3150a627ff3ba478b12fb51edb0a111b9ba370b62ff591ff046bb15918906451","schema_version":"1.0","event_id":"sha256:3150a627ff3ba478b12fb51edb0a111b9ba370b62ff591ff046bb15918906451"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/bundle.json","state_url":"https://pith.science/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/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":"2026-08-19T23:05:27Z","links":{"resolver":"https://pith.science/pith/G6CH3GWBNAE67V6VA2YWVTVYS6","bundle":"https://pith.science/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/bundle.json","state":"https://pith.science/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G6CH3GWBNAE67V6VA2YWVTVYS6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:G6CH3GWBNAE67V6VA2YWVTVYS6","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":"a9cc9d90ffed8241729e0d6c60bbcbdb40825d2f2750c3db5bbce08e7df9d136","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T15:02:45Z","title_canon_sha256":"303fa0cdb693015fa7240ff853ea1e92e7e2bbed09908b24a2756e6299a8f290"},"schema_version":"1.0","source":{"id":"2406.11617","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11617","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11617v1","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11617","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_12","alias_value":"G6CH3GWBNAE6","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_16","alias_value":"G6CH3GWBNAE67V6V","created_at":"2026-07-05T08:32:55Z"},{"alias_kind":"pith_short_8","alias_value":"G6CH3GWB","created_at":"2026-07-05T08:32:55Z"}],"graph_snapshots":[{"event_id":"sha256:3150a627ff3ba478b12fb51edb0a111b9ba370b62ff591ff046bb15918906451","target":"graph","created_at":"2026-07-05T08:32:55Z","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/2406.11617/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the proliferation of domain-specific models, model merging has emerged as a set of techniques that combine the capabilities of multiple models into one that can multitask without the cost of additional training. In this paper, we propose a new model merging technique, Drop and rEscaLe via sampLing with mAgnitude (DELLA-Merging), that employs a novel pruning technique, MAGPRUNE, which shows significant advantages over DARE and TIES. MAGPRUNE first ranks the parameters in order of their magnitude and assigns higher dropout probabilities (p) to parameters with lower ranks corresponding to lo","authors_text":"Pala Tej Deep, Rishabh Bhardwaj, Soujanya Poria","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T15:02:45Z","title":"DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11617","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":"sha256:25785a4a55697b2b81109722ea345ed297318ce8f6358896a1a765297c5c7268","target":"record","created_at":"2026-07-05T08:32:55Z","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":"a9cc9d90ffed8241729e0d6c60bbcbdb40825d2f2750c3db5bbce08e7df9d136","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-17T15:02:45Z","title_canon_sha256":"303fa0cdb693015fa7240ff853ea1e92e7e2bbed09908b24a2756e6299a8f290"},"schema_version":"1.0","source":{"id":"2406.11617","kind":"arxiv","version":1}},"canonical_sha256":"37847d9ac16809efd7d506b16aceb8979ad1e3580b999a9fdca08d873c9db4b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37847d9ac16809efd7d506b16aceb8979ad1e3580b999a9fdca08d873c9db4b9","first_computed_at":"2026-07-05T08:32:55.603473Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:32:55.603473Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rpUmD4cuU9l75w3pbLsrr8JoAn9fXsxne8GtsUSBdxp6g5eytxs58BYlaXK2qjW+e5ETns0JsxxAQetniT3QBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:32:55.604122Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.11617","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25785a4a55697b2b81109722ea345ed297318ce8f6358896a1a765297c5c7268","sha256:3150a627ff3ba478b12fb51edb0a111b9ba370b62ff591ff046bb15918906451"],"state_sha256":"3baa779c441d3e9f9552926a2292be3755263cf87ef1938b9d691b448ac29d70"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"riUZhlixUhaoxcqm5gDH50U3pnp43kjoXnvZEpNeTGrUN8CpblNI21zpx4gwWKQFXgLMuWuxbma72/GSXW44BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T23:05:27.556589Z","bundle_sha256":"b8320589cf8d35703f98ef0796939338ed852e38ff113240bac7bbc1a7a7f2f7"}}