{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EFPN6KAEDM57SMBRS5TQ45GQQV","short_pith_number":"pith:EFPN6KAE","schema_version":"1.0","canonical_sha256":"215edf28041b3bf9303197670e74d0856f5bc5a3507a5ba014d84ae28eec9bbc","source":{"kind":"arxiv","id":"2310.08915","version":3},"attestation_state":"computed","paper":{"title":"Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jared Tanner, Lirui Zhao, Mingbao Lin, Rongrong Ji, Shiwei Liu, Xingjia Han, Yiwu Yao, Yunyun Sun, Yuxin Zhang","submitted_at":"2023-10-13T07:38:52Z","abstract_excerpt":"The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre-LLMs approaches in reducing model complexity, network pruning appears to lag behind in the era of LLMs, due mostly to its costly fine-tuning (or re-training) necessity under the massive volumes of model parameter and training data. To close this industry-academia gap, we introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning app"},"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":"2310.08915","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-10-13T07:38:52Z","cross_cats_sorted":[],"title_canon_sha256":"69171c27dbbef52a62aba7ac59b6c02f2bc616648bc15455e67407df459e9d16","abstract_canon_sha256":"cf752d8c6840373e9a7c8019ef4106aa61f7fbad1ceb128260fa8a2a972e66d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:15.183086Z","signature_b64":"A/kHl7frCGSN1Z2TgGHswGasxZzNhrHpeXRRdRb6RjVqbNTXUIrvdi7gNP58/6xR+Lcvb6J67CJuxw5WvIxkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"215edf28041b3bf9303197670e74d0856f5bc5a3507a5ba014d84ae28eec9bbc","last_reissued_at":"2026-07-05T07:49:15.182647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:15.182647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jared Tanner, Lirui Zhao, Mingbao Lin, Rongrong Ji, Shiwei Liu, Xingjia Han, Yiwu Yao, Yunyun Sun, Yuxin Zhang","submitted_at":"2023-10-13T07:38:52Z","abstract_excerpt":"The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre-LLMs approaches in reducing model complexity, network pruning appears to lag behind in the era of LLMs, due mostly to its costly fine-tuning (or re-training) necessity under the massive volumes of model parameter and training data. To close this industry-academia gap, we introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08915","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/2310.08915/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":"2310.08915","created_at":"2026-07-05T07:49:15.182705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08915v3","created_at":"2026-07-05T07:49:15.182705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08915","created_at":"2026-07-05T07:49:15.182705+00:00"},{"alias_kind":"pith_short_12","alias_value":"EFPN6KAEDM57","created_at":"2026-07-05T07:49:15.182705+00:00"},{"alias_kind":"pith_short_16","alias_value":"EFPN6KAEDM57SMBR","created_at":"2026-07-05T07:49:15.182705+00:00"},{"alias_kind":"pith_short_8","alias_value":"EFPN6KAE","created_at":"2026-07-05T07:49:15.182705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01544","citing_title":"CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26632","citing_title":"RT-Lynx: Putting GEMM Sparsity in the Right Place for Diffusion Models","ref_index":80,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04493","citing_title":"SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV","json":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV.json","graph_json":"https://pith.science/api/pith-number/EFPN6KAEDM57SMBRS5TQ45GQQV/graph.json","events_json":"https://pith.science/api/pith-number/EFPN6KAEDM57SMBRS5TQ45GQQV/events.json","paper":"https://pith.science/paper/EFPN6KAE"},"agent_actions":{"view_html":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV","download_json":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV.json","view_paper":"https://pith.science/paper/EFPN6KAE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08915&json=true","fetch_graph":"https://pith.science/api/pith-number/EFPN6KAEDM57SMBRS5TQ45GQQV/graph.json","fetch_events":"https://pith.science/api/pith-number/EFPN6KAEDM57SMBRS5TQ45GQQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV/action/storage_attestation","attest_author":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV/action/author_attestation","sign_citation":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV/action/citation_signature","submit_replication":"https://pith.science/pith/EFPN6KAEDM57SMBRS5TQ45GQQV/action/replication_record"}},"created_at":"2026-07-05T07:49:15.182705+00:00","updated_at":"2026-07-05T07:49:15.182705+00:00"}