{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MTTKZMIE3ASAOER2BZMFS2FYHW","short_pith_number":"pith:MTTKZMIE","schema_version":"1.0","canonical_sha256":"64e6acb104d82407123a0e585968b83dbd953b64ccddacee3027ca4ac0fbd55b","source":{"kind":"arxiv","id":"2505.21191","version":1},"attestation_state":"computed","paper":{"title":"Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jungang Li, Junyan Zhang, Junzhuo Li, Shuliang Liu, Sicheng Tao, Song Dai, Xuming Hu, Yibo Yan, Yonghua Hei, Yubo Gao, Zhaorui Hou","submitted_at":"2025-05-27T13:40:28Z","abstract_excerpt":"The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improvements remain poorly understood. This study systematically examines how fine-tuning reconfigures LLM computations by isolating and analyzing instruction-specific sparse components, i.e., neurons in dense models and both neurons and experts in Mixture-of-Experts (MoE) architectures. In particular, we introduce HexaInst, a carefully curated and balanced instructional dataset spanning six distinct categories, and propos"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.21191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T13:40:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"47cf58ee6e6a3a752ef4640398783f030a953a5478325a7b6db0cbfc173fc694","abstract_canon_sha256":"ce64e229e73302586cecb9c65a317cc1260ae1049a9505e8680d168406bb03fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:38.540400Z","signature_b64":"wJ1mamErT0xd1fRK4+9RWyTmSVdfw+89vQKKfmX3+DohUTODE5alo/8Miph/FHUM3GkoOZFIsy0G1lG1X7C6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64e6acb104d82407123a0e585968b83dbd953b64ccddacee3027ca4ac0fbd55b","last_reissued_at":"2026-07-05T11:10:38.539887Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:38.539887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jungang Li, Junyan Zhang, Junzhuo Li, Shuliang Liu, Sicheng Tao, Song Dai, Xuming Hu, Yibo Yan, Yonghua Hei, Yubo Gao, Zhaorui Hou","submitted_at":"2025-05-27T13:40:28Z","abstract_excerpt":"The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improvements remain poorly understood. This study systematically examines how fine-tuning reconfigures LLM computations by isolating and analyzing instruction-specific sparse components, i.e., neurons in dense models and both neurons and experts in Mixture-of-Experts (MoE) architectures. In particular, we introduce HexaInst, a carefully curated and balanced instructional dataset spanning six distinct categories, and propos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21191","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/2505.21191/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.21191","created_at":"2026-07-05T11:10:38.539940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.21191v1","created_at":"2026-07-05T11:10:38.539940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21191","created_at":"2026-07-05T11:10:38.539940+00:00"},{"alias_kind":"pith_short_12","alias_value":"MTTKZMIE3ASA","created_at":"2026-07-05T11:10:38.539940+00:00"},{"alias_kind":"pith_short_16","alias_value":"MTTKZMIE3ASAOER2","created_at":"2026-07-05T11:10:38.539940+00:00"},{"alias_kind":"pith_short_8","alias_value":"MTTKZMIE","created_at":"2026-07-05T11:10:38.539940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW","json":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW.json","graph_json":"https://pith.science/api/pith-number/MTTKZMIE3ASAOER2BZMFS2FYHW/graph.json","events_json":"https://pith.science/api/pith-number/MTTKZMIE3ASAOER2BZMFS2FYHW/events.json","paper":"https://pith.science/paper/MTTKZMIE"},"agent_actions":{"view_html":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW","download_json":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW.json","view_paper":"https://pith.science/paper/MTTKZMIE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.21191&json=true","fetch_graph":"https://pith.science/api/pith-number/MTTKZMIE3ASAOER2BZMFS2FYHW/graph.json","fetch_events":"https://pith.science/api/pith-number/MTTKZMIE3ASAOER2BZMFS2FYHW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW/action/storage_attestation","attest_author":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW/action/author_attestation","sign_citation":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW/action/citation_signature","submit_replication":"https://pith.science/pith/MTTKZMIE3ASAOER2BZMFS2FYHW/action/replication_record"}},"created_at":"2026-07-05T11:10:38.539940+00:00","updated_at":"2026-07-05T11:10:38.539940+00:00"}