{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YSHYR3XGFAOUKSF74ERLOKLN4W","short_pith_number":"pith:YSHYR3XG","canonical_record":{"source":{"id":"2408.14470","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-26T17:58:53Z","cross_cats_sorted":[],"title_canon_sha256":"d58d7742a8e876d695e99e3e13cb19aa9f39d8e19b985e55db854bdc1053c91c","abstract_canon_sha256":"f85a02e6c33bcd54d3fab653714cdd2507ad5f43a293fad6e8303a64ccaa2b10"},"schema_version":"1.0"},"canonical_sha256":"c48f88eee6281d4548bfe122b7296de5b8cd6b9a4626da1708d598ffe4e72346","source":{"kind":"arxiv","id":"2408.14470","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.14470","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"arxiv_version","alias_value":"2408.14470v3","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.14470","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_12","alias_value":"YSHYR3XGFAOU","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_16","alias_value":"YSHYR3XGFAOUKSF7","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_8","alias_value":"YSHYR3XG","created_at":"2026-07-05T11:25:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YSHYR3XGFAOUKSF74ERLOKLN4W","target":"record","payload":{"canonical_record":{"source":{"id":"2408.14470","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-26T17:58:53Z","cross_cats_sorted":[],"title_canon_sha256":"d58d7742a8e876d695e99e3e13cb19aa9f39d8e19b985e55db854bdc1053c91c","abstract_canon_sha256":"f85a02e6c33bcd54d3fab653714cdd2507ad5f43a293fad6e8303a64ccaa2b10"},"schema_version":"1.0"},"canonical_sha256":"c48f88eee6281d4548bfe122b7296de5b8cd6b9a4626da1708d598ffe4e72346","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:39.148200Z","signature_b64":"7Pcwkca+i66j1SC4vTNSWXjoIK6Tsnw5irocm1sCJ8gB+s+lteYzR7+gx5y3wrij0gYGMoTWTeLgD/5nBux4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c48f88eee6281d4548bfe122b7296de5b8cd6b9a4626da1708d598ffe4e72346","last_reissued_at":"2026-07-05T11:25:39.147716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:39.147716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.14470","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/CiEVGFxHuHNpzUfqDUiqaFGJiCeQlafmuen8Mya3x18Snb9M79KoPYSa5TNTPiBymtzBspDU1n58MUivZL1AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:52:45.572472Z"},"content_sha256":"0f813652df15019ba28d21311f55d760568299002bed9e069a2390073145593e","schema_version":"1.0","event_id":"sha256:0f813652df15019ba28d21311f55d760568299002bed9e069a2390073145593e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YSHYR3XGFAOUKSF74ERLOKLN4W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aradhye Agarwal, Ayan Sengupta, Suhas K Ramesh, Tanmoy Chakraborty","submitted_at":"2024-08-26T17:58:53Z","abstract_excerpt":"Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. Selective PEFT, a class of parameter-efficient fine-tuning (PEFT) methodologies, aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although parameter-efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective PEFT techniques use a fixed set of parameters selected using different importance heu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.14470","kind":"arxiv","version":3},"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/2408.14470/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":"2026-07-05T11:25:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aym34eWrKNVCACJDA4MDAUVTx2ag0VNiorFcDXpz9uOeEAEW6/mFHkeNjA0xDBW9hNgcZk7acsi25uKGNXDWCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:52:45.573066Z"},"content_sha256":"9d3dd8bbf72291bfd21f68c6aac2e40addf9a1d6c6f744e2bccf2820906f9b61","schema_version":"1.0","event_id":"sha256:9d3dd8bbf72291bfd21f68c6aac2e40addf9a1d6c6f744e2bccf2820906f9b61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/bundle.json","state_url":"https://pith.science/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/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":"2026-08-06T01:52:45Z","links":{"resolver":"https://pith.science/pith/YSHYR3XGFAOUKSF74ERLOKLN4W","bundle":"https://pith.science/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/bundle.json","state":"https://pith.science/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YSHYR3XGFAOUKSF74ERLOKLN4W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YSHYR3XGFAOUKSF74ERLOKLN4W","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":"f85a02e6c33bcd54d3fab653714cdd2507ad5f43a293fad6e8303a64ccaa2b10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-26T17:58:53Z","title_canon_sha256":"d58d7742a8e876d695e99e3e13cb19aa9f39d8e19b985e55db854bdc1053c91c"},"schema_version":"1.0","source":{"id":"2408.14470","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.14470","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"arxiv_version","alias_value":"2408.14470v3","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.14470","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_12","alias_value":"YSHYR3XGFAOU","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_16","alias_value":"YSHYR3XGFAOUKSF7","created_at":"2026-07-05T11:25:39Z"},{"alias_kind":"pith_short_8","alias_value":"YSHYR3XG","created_at":"2026-07-05T11:25:39Z"}],"graph_snapshots":[{"event_id":"sha256:9d3dd8bbf72291bfd21f68c6aac2e40addf9a1d6c6f744e2bccf2820906f9b61","target":"graph","created_at":"2026-07-05T11:25:39Z","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/2408.14470/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. Selective PEFT, a class of parameter-efficient fine-tuning (PEFT) methodologies, aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although parameter-efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective PEFT techniques use a fixed set of parameters selected using different importance heu","authors_text":"Aradhye Agarwal, Ayan Sengupta, Suhas K Ramesh, Tanmoy Chakraborty","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-26T17:58:53Z","title":"Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.14470","kind":"arxiv","version":3},"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":"sha256:0f813652df15019ba28d21311f55d760568299002bed9e069a2390073145593e","target":"record","created_at":"2026-07-05T11:25:39Z","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":"f85a02e6c33bcd54d3fab653714cdd2507ad5f43a293fad6e8303a64ccaa2b10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-26T17:58:53Z","title_canon_sha256":"d58d7742a8e876d695e99e3e13cb19aa9f39d8e19b985e55db854bdc1053c91c"},"schema_version":"1.0","source":{"id":"2408.14470","kind":"arxiv","version":3}},"canonical_sha256":"c48f88eee6281d4548bfe122b7296de5b8cd6b9a4626da1708d598ffe4e72346","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c48f88eee6281d4548bfe122b7296de5b8cd6b9a4626da1708d598ffe4e72346","first_computed_at":"2026-07-05T11:25:39.147716Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:39.147716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7Pcwkca+i66j1SC4vTNSWXjoIK6Tsnw5irocm1sCJ8gB+s+lteYzR7+gx5y3wrij0gYGMoTWTeLgD/5nBux4Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:39.148200Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.14470","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f813652df15019ba28d21311f55d760568299002bed9e069a2390073145593e","sha256:9d3dd8bbf72291bfd21f68c6aac2e40addf9a1d6c6f744e2bccf2820906f9b61"],"state_sha256":"7a62583e978f7527e80c3cc3dd4f7fdd713636744b6e00f1b43416565f47ab87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uMEUoPZN9bGvMxf4kKdqqCyMIozaL2bSzdAolgRiwGrs+EvjR8Yc4TeMLQ6CcigmoNU1r30zwWdTBBfvIHSmCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T01:52:45.576589Z","bundle_sha256":"e7048c722718fd5d75efbfc5bddb24115076082d598f693c800a8a546ab9cd1c"}}