{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7EOKS72JTLFBPQBFG6YTE5LWBT","short_pith_number":"pith:7EOKS72J","canonical_record":{"source":{"id":"2405.02596","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T07:44:18Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"680ea485fb42121199a7470979b6368a591149cee351583d458232b922fa9fa8","abstract_canon_sha256":"6ce4332e5d8fd6a1ab354e3c9e84a598deba9553e6ead706adea0d48e7d06ada"},"schema_version":"1.0"},"canonical_sha256":"f91ca97f499aca17c02537b13275760cf5d45a9be34057f27d5822a3cefcba44","source":{"kind":"arxiv","id":"2405.02596","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.02596","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2405.02596v1","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02596","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"7EOKS72JTLFB","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"7EOKS72JTLFBPQBF","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"7EOKS72J","created_at":"2026-07-05T08:15:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7EOKS72JTLFBPQBFG6YTE5LWBT","target":"record","payload":{"canonical_record":{"source":{"id":"2405.02596","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T07:44:18Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"680ea485fb42121199a7470979b6368a591149cee351583d458232b922fa9fa8","abstract_canon_sha256":"6ce4332e5d8fd6a1ab354e3c9e84a598deba9553e6ead706adea0d48e7d06ada"},"schema_version":"1.0"},"canonical_sha256":"f91ca97f499aca17c02537b13275760cf5d45a9be34057f27d5822a3cefcba44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:15:17.867560Z","signature_b64":"az/Njt4tJnHu6QhABLH7iNuAQjaKdCrjsZKQB05I0bP4Zt/BNP9t53dRNzp3IF+ZJsELeCORZxoQe3sP+ArxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f91ca97f499aca17c02537b13275760cf5d45a9be34057f27d5822a3cefcba44","last_reissued_at":"2026-07-05T08:15:17.867182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:15:17.867182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.02596","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":"2026-07-05T08:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eRBfQY2NmQx0Wsx9p5WYqv+egALWWOect/jm0LyygAt3Rfgj45UaWQTHbcd9SSo6PqO3PYrZB117wUrfvHKECA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:35:57.645903Z"},"content_sha256":"add374f30a47c7c91dd63cbdd20194212720d77f5108e9bbc1529e8cd932936a","schema_version":"1.0","event_id":"sha256:add374f30a47c7c91dd63cbdd20194212720d77f5108e9bbc1529e8cd932936a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7EOKS72JTLFBPQBFG6YTE5LWBT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Jing Xu, Jingzhao Zhang","submitted_at":"2024-05-04T07:44:18Z","abstract_excerpt":"Fine-tuning large language models (LLM) can be costly. Parameter-efficient fine-tuning (PEFT) addresses the problems by training a fraction of the parameters, whose success reveals the expressiveness and flexibility of pretrained models. This paper studies the limit of PEFT, by further simplifying its design and reducing the number of trainable parameters beyond standard setups. To this end, we use Random Masking to fine-tune the pretrained model. Despite its simplicity, we show that Random Masking is surprisingly effective: with a larger-than-expected learning rate, Random Masking can match t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02596","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/2405.02596/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":"2026-07-05T08:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"61aIZwleWD2usAXS77sK/vScCh3/ZtfSYXydpa9ikBIspJrT2o4ldSH5RMWNwFzBapw6ryblqzGsIjwEuPfuBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:35:57.646428Z"},"content_sha256":"f45ff74d0e01ba37919a858832ebffa9e12b57d08f50da3cfe389f489e258800","schema_version":"1.0","event_id":"sha256:f45ff74d0e01ba37919a858832ebffa9e12b57d08f50da3cfe389f489e258800"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/bundle.json","state_url":"https://pith.science/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/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":"2026-08-05T18:35:57Z","links":{"resolver":"https://pith.science/pith/7EOKS72JTLFBPQBFG6YTE5LWBT","bundle":"https://pith.science/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/bundle.json","state":"https://pith.science/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7EOKS72JTLFBPQBFG6YTE5LWBT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7EOKS72JTLFBPQBFG6YTE5LWBT","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":"6ce4332e5d8fd6a1ab354e3c9e84a598deba9553e6ead706adea0d48e7d06ada","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T07:44:18Z","title_canon_sha256":"680ea485fb42121199a7470979b6368a591149cee351583d458232b922fa9fa8"},"schema_version":"1.0","source":{"id":"2405.02596","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.02596","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2405.02596v1","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.02596","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"7EOKS72JTLFB","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"7EOKS72JTLFBPQBF","created_at":"2026-07-05T08:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"7EOKS72J","created_at":"2026-07-05T08:15:17Z"}],"graph_snapshots":[{"event_id":"sha256:f45ff74d0e01ba37919a858832ebffa9e12b57d08f50da3cfe389f489e258800","target":"graph","created_at":"2026-07-05T08:15:17Z","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/2405.02596/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large language models (LLM) can be costly. Parameter-efficient fine-tuning (PEFT) addresses the problems by training a fraction of the parameters, whose success reveals the expressiveness and flexibility of pretrained models. This paper studies the limit of PEFT, by further simplifying its design and reducing the number of trainable parameters beyond standard setups. To this end, we use Random Masking to fine-tune the pretrained model. Despite its simplicity, we show that Random Masking is surprisingly effective: with a larger-than-expected learning rate, Random Masking can match t","authors_text":"Jing Xu, Jingzhao Zhang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T07:44:18Z","title":"Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.02596","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:add374f30a47c7c91dd63cbdd20194212720d77f5108e9bbc1529e8cd932936a","target":"record","created_at":"2026-07-05T08:15:17Z","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":"6ce4332e5d8fd6a1ab354e3c9e84a598deba9553e6ead706adea0d48e7d06ada","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-04T07:44:18Z","title_canon_sha256":"680ea485fb42121199a7470979b6368a591149cee351583d458232b922fa9fa8"},"schema_version":"1.0","source":{"id":"2405.02596","kind":"arxiv","version":1}},"canonical_sha256":"f91ca97f499aca17c02537b13275760cf5d45a9be34057f27d5822a3cefcba44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f91ca97f499aca17c02537b13275760cf5d45a9be34057f27d5822a3cefcba44","first_computed_at":"2026-07-05T08:15:17.867182Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:15:17.867182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"az/Njt4tJnHu6QhABLH7iNuAQjaKdCrjsZKQB05I0bP4Zt/BNP9t53dRNzp3IF+ZJsELeCORZxoQe3sP+ArxCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:15:17.867560Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.02596","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:add374f30a47c7c91dd63cbdd20194212720d77f5108e9bbc1529e8cd932936a","sha256:f45ff74d0e01ba37919a858832ebffa9e12b57d08f50da3cfe389f489e258800"],"state_sha256":"6e14390cc823039a97b383e973828e4cb52072cd6483e3ad325bf14b7ef63176"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qmZI25jvLn4iO7kWhgHuWi6j4kwmMAp7izUUvWda1swAr4XjfLZQdiyjH8UsY6wfuulcZsS3IaiKxOlnRQ42Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T18:35:57.652747Z","bundle_sha256":"3ede25123e735327dd5b205d661926b42e06a852ec0bfbece839252691671363"}}