{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:I2RAJ6QTIAKVJYAHAHUCEINLZ4","short_pith_number":"pith:I2RAJ6QT","schema_version":"1.0","canonical_sha256":"46a204fa13401554e00701e82221abcf2189cce52f54726b6f84e555f13fafc1","source":{"kind":"arxiv","id":"2306.11222","version":2},"attestation_state":"computed","paper":{"title":"LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Chen Liang, Pengcheng He, Qingru Zhang, Tuo Zhao, Weizhu Chen, Yifan Yu, Yixiao Li","submitted_at":"2023-06-20T01:16:11Z","abstract_excerpt":"Transformer models have achieved remarkable results in various natural language tasks, but they are often prohibitively large, requiring massive memories and computational resources. To reduce the size and complexity of these models, we propose LoSparse (Low-Rank and Sparse approximation), a novel model compression technique that approximates a weight matrix by the sum of a low-rank matrix and a sparse matrix. Our method combines the advantages of both low-rank approximations and pruning, while avoiding their limitations. Low-rank approximation compresses the coherent and expressive parts in n"},"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":"2306.11222","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T01:16:11Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b93b8b86b4bfd73cf73ebcd2021e0767ff5d931d5d0e6a1a82763cf24b994891","abstract_canon_sha256":"d5ec95643238cfbf71e8dcad8d284dfde1bd638636690ee24da20eef7e5af3eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:31.029781Z","signature_b64":"gZI0WTZu6Gfpz6U0ujskoYNKxWK19zdE6KIUngmN9gTJ7pP7AdbtZ8gz0V8XracCDsqUyXlRzCfA8OkHIJSWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46a204fa13401554e00701e82221abcf2189cce52f54726b6f84e555f13fafc1","last_reissued_at":"2026-07-05T06:24:31.029333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:31.029333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Chen Liang, Pengcheng He, Qingru Zhang, Tuo Zhao, Weizhu Chen, Yifan Yu, Yixiao Li","submitted_at":"2023-06-20T01:16:11Z","abstract_excerpt":"Transformer models have achieved remarkable results in various natural language tasks, but they are often prohibitively large, requiring massive memories and computational resources. To reduce the size and complexity of these models, we propose LoSparse (Low-Rank and Sparse approximation), a novel model compression technique that approximates a weight matrix by the sum of a low-rank matrix and a sparse matrix. Our method combines the advantages of both low-rank approximations and pruning, while avoiding their limitations. Low-rank approximation compresses the coherent and expressive parts in n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11222","kind":"arxiv","version":2},"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/2306.11222/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":"2306.11222","created_at":"2026-07-05T06:24:31.029394+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.11222v2","created_at":"2026-07-05T06:24:31.029394+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11222","created_at":"2026-07-05T06:24:31.029394+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2RAJ6QTIAKV","created_at":"2026-07-05T06:24:31.029394+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2RAJ6QTIAKVJYAH","created_at":"2026-07-05T06:24:31.029394+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2RAJ6QT","created_at":"2026-07-05T06:24:31.029394+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28438","citing_title":"When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2401.05459","citing_title":"Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security","ref_index":261,"is_internal_anchor":false},{"citing_arxiv_id":"2404.14294","citing_title":"A Survey on Efficient Inference for Large Language Models","ref_index":145,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4","json":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4.json","graph_json":"https://pith.science/api/pith-number/I2RAJ6QTIAKVJYAHAHUCEINLZ4/graph.json","events_json":"https://pith.science/api/pith-number/I2RAJ6QTIAKVJYAHAHUCEINLZ4/events.json","paper":"https://pith.science/paper/I2RAJ6QT"},"agent_actions":{"view_html":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4","download_json":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4.json","view_paper":"https://pith.science/paper/I2RAJ6QT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.11222&json=true","fetch_graph":"https://pith.science/api/pith-number/I2RAJ6QTIAKVJYAHAHUCEINLZ4/graph.json","fetch_events":"https://pith.science/api/pith-number/I2RAJ6QTIAKVJYAHAHUCEINLZ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4/action/storage_attestation","attest_author":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4/action/author_attestation","sign_citation":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4/action/citation_signature","submit_replication":"https://pith.science/pith/I2RAJ6QTIAKVJYAHAHUCEINLZ4/action/replication_record"}},"created_at":"2026-07-05T06:24:31.029394+00:00","updated_at":"2026-07-05T06:24:31.029394+00:00"}