{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:M3BA6X3L7VIMNT6PJKXMBRPMV2","short_pith_number":"pith:M3BA6X3L","canonical_record":{"source":{"id":"2403.11621","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T09:55:01Z","cross_cats_sorted":[],"title_canon_sha256":"aa4d54d78b7b43d7dc7add03f487c318bc27ab7514c0ac72503deab4ba3bfdf7","abstract_canon_sha256":"ea4ff857c65e76411a063f6a7b4198f042f60603acdcf1d26f31e92433e8bfd1"},"schema_version":"1.0"},"canonical_sha256":"66c20f5f6bfd50c6cfcf4aaec0c5ecae9f23c362a4cc46e559974c13c162babb","source":{"kind":"arxiv","id":"2403.11621","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.11621","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"arxiv_version","alias_value":"2403.11621v1","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11621","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_12","alias_value":"M3BA6X3L7VIM","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_16","alias_value":"M3BA6X3L7VIMNT6P","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_8","alias_value":"M3BA6X3L","created_at":"2026-07-05T07:57:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:M3BA6X3L7VIMNT6PJKXMBRPMV2","target":"record","payload":{"canonical_record":{"source":{"id":"2403.11621","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T09:55:01Z","cross_cats_sorted":[],"title_canon_sha256":"aa4d54d78b7b43d7dc7add03f487c318bc27ab7514c0ac72503deab4ba3bfdf7","abstract_canon_sha256":"ea4ff857c65e76411a063f6a7b4198f042f60603acdcf1d26f31e92433e8bfd1"},"schema_version":"1.0"},"canonical_sha256":"66c20f5f6bfd50c6cfcf4aaec0c5ecae9f23c362a4cc46e559974c13c162babb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:19.109660Z","signature_b64":"siKSfDeO+/GIXdd93qV864YXJgrH1rKQiLv8sJRTjt8qnngAu53Spv9ZuQhvfXA+NOT3Tqyv7Nlsu9bjmquXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66c20f5f6bfd50c6cfcf4aaec0c5ecae9f23c362a4cc46e559974c13c162babb","last_reissued_at":"2026-07-05T07:57:19.109127Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:19.109127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.11621","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":"2026-07-05T07:57:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9nIiPZ/BoR1XbTJs0Z4yaCsnOFvAHyz89xLrlDNauAX/veGMqIWL54lvRv+LEMZsy+DTClqX88tNo4/HdHhLCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:52:15.640559Z"},"content_sha256":"49cf7101275e1ad12941ed6badcf0efa92f91721f7c061bd02706a15e414d3d0","schema_version":"1.0","event_id":"sha256:49cf7101275e1ad12941ed6badcf0efa92f91721f7c061bd02706a15e414d3d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:M3BA6X3L7VIMNT6PJKXMBRPMV2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Haoyun Xu, Lidia S. Chao, Runzhe Zhan","submitted_at":"2024-03-18T09:55:01Z","abstract_excerpt":"Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11621","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/2403.11621/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":"2026-07-05T07:57:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nuGjfvP56vONi5uepXe7ifFvLV1dwlS3W0Ew9ZyE05tnfOBK92sciqbr2MUpYJg8sF/lZ/VV3N0WKWqSMsKoCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:52:15.641077Z"},"content_sha256":"72e65aa0f180c0737b1c6fee316de79cfdcd3ea2c9f13ea7f0d5d04994a8e9d8","schema_version":"1.0","event_id":"sha256:72e65aa0f180c0737b1c6fee316de79cfdcd3ea2c9f13ea7f0d5d04994a8e9d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/bundle.json","state_url":"https://pith.science/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/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":"2026-08-05T02:52:15Z","links":{"resolver":"https://pith.science/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2","bundle":"https://pith.science/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/bundle.json","state":"https://pith.science/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M3BA6X3L7VIMNT6PJKXMBRPMV2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:M3BA6X3L7VIMNT6PJKXMBRPMV2","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":"ea4ff857c65e76411a063f6a7b4198f042f60603acdcf1d26f31e92433e8bfd1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T09:55:01Z","title_canon_sha256":"aa4d54d78b7b43d7dc7add03f487c318bc27ab7514c0ac72503deab4ba3bfdf7"},"schema_version":"1.0","source":{"id":"2403.11621","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.11621","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"arxiv_version","alias_value":"2403.11621v1","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11621","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_12","alias_value":"M3BA6X3L7VIM","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_16","alias_value":"M3BA6X3L7VIMNT6P","created_at":"2026-07-05T07:57:19Z"},{"alias_kind":"pith_short_8","alias_value":"M3BA6X3L","created_at":"2026-07-05T07:57:19Z"}],"graph_snapshots":[{"event_id":"sha256:72e65aa0f180c0737b1c6fee316de79cfdcd3ea2c9f13ea7f0d5d04994a8e9d8","target":"graph","created_at":"2026-07-05T07:57:19Z","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/2403.11621/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainab","authors_text":"Derek F. Wong, Haoyun Xu, Lidia S. Chao, Runzhe Zhan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T09:55:01Z","title":"Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11621","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":"sha256:49cf7101275e1ad12941ed6badcf0efa92f91721f7c061bd02706a15e414d3d0","target":"record","created_at":"2026-07-05T07:57:19Z","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":"ea4ff857c65e76411a063f6a7b4198f042f60603acdcf1d26f31e92433e8bfd1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T09:55:01Z","title_canon_sha256":"aa4d54d78b7b43d7dc7add03f487c318bc27ab7514c0ac72503deab4ba3bfdf7"},"schema_version":"1.0","source":{"id":"2403.11621","kind":"arxiv","version":1}},"canonical_sha256":"66c20f5f6bfd50c6cfcf4aaec0c5ecae9f23c362a4cc46e559974c13c162babb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"66c20f5f6bfd50c6cfcf4aaec0c5ecae9f23c362a4cc46e559974c13c162babb","first_computed_at":"2026-07-05T07:57:19.109127Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:57:19.109127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"siKSfDeO+/GIXdd93qV864YXJgrH1rKQiLv8sJRTjt8qnngAu53Spv9ZuQhvfXA+NOT3Tqyv7Nlsu9bjmquXDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:57:19.109660Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.11621","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49cf7101275e1ad12941ed6badcf0efa92f91721f7c061bd02706a15e414d3d0","sha256:72e65aa0f180c0737b1c6fee316de79cfdcd3ea2c9f13ea7f0d5d04994a8e9d8"],"state_sha256":"b3b26f3173493983d8cd20e75bca58aaf5e6d1c8ba05ca7bda3dd6357faa1966"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BrO1GZ3sLpEkEKBymME2xRaYHw9XyFtJhI1vCadsMKesu+1jxi2+ZpfCIJPhQZBIq0SYrMY6uHSDv2/JguWECg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T02:52:15.645327Z","bundle_sha256":"d36cb1f1432141627dd318f849825466a65791ec335ab51b88352fd8936f357e"}}