{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HVVTNHOKGU3QCOGG4WRFBTMSFP","short_pith_number":"pith:HVVTNHOK","schema_version":"1.0","canonical_sha256":"3d6b369dca35370138c6e5a250cd922bc46bb4f3561a888e1ac101235e54aa03","source":{"kind":"arxiv","id":"2409.01990","version":5},"attestation_state":"computed","paper":{"title":"Designing Large Foundation Models for Efficient Training and Inference: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Benjamin Lengerich, Dong Liu, Jing Wu, Sina Alinejad, Yanxuan Yu, Ying Nian Wu, Yite Wang, Zhongwei Wan","submitted_at":"2024-09-03T15:35:01Z","abstract_excerpt":"This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey."},"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":"2409.01990","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2024-09-03T15:35:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fbd9a3a01176af106846b6716f6e366a720682f31e6392f973f2fac901697b63","abstract_canon_sha256":"4a646907b55689cd5c43b35d5e23aaa125889b756a48e4bb1a3ae3de982e790e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:19.050470Z","signature_b64":"iFJKrD80nKPOrCLRyaDF51+5YqRID1qLvSuKxxa5g9OXUXBaKbYMOacOrbkTPlcuY9fSYSwcxfRB+fcNN2kwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d6b369dca35370138c6e5a250cd922bc46bb4f3561a888e1ac101235e54aa03","last_reissued_at":"2026-07-05T10:48:19.049894Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:19.049894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Designing Large Foundation Models for Efficient Training and Inference: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Benjamin Lengerich, Dong Liu, Jing Wu, Sina Alinejad, Yanxuan Yu, Ying Nian Wu, Yite Wang, Zhongwei Wan","submitted_at":"2024-09-03T15:35:01Z","abstract_excerpt":"This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01990","kind":"arxiv","version":5},"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/2409.01990/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":"2409.01990","created_at":"2026-07-05T10:48:19.049969+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.01990v5","created_at":"2026-07-05T10:48:19.049969+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01990","created_at":"2026-07-05T10:48:19.049969+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVVTNHOKGU3Q","created_at":"2026-07-05T10:48:19.049969+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVVTNHOKGU3QCOGG","created_at":"2026-07-05T10:48:19.049969+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVVTNHOK","created_at":"2026-07-05T10:48:19.049969+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01595","citing_title":"Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05234","citing_title":"OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP","json":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP.json","graph_json":"https://pith.science/api/pith-number/HVVTNHOKGU3QCOGG4WRFBTMSFP/graph.json","events_json":"https://pith.science/api/pith-number/HVVTNHOKGU3QCOGG4WRFBTMSFP/events.json","paper":"https://pith.science/paper/HVVTNHOK"},"agent_actions":{"view_html":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP","download_json":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP.json","view_paper":"https://pith.science/paper/HVVTNHOK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.01990&json=true","fetch_graph":"https://pith.science/api/pith-number/HVVTNHOKGU3QCOGG4WRFBTMSFP/graph.json","fetch_events":"https://pith.science/api/pith-number/HVVTNHOKGU3QCOGG4WRFBTMSFP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP/action/storage_attestation","attest_author":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP/action/author_attestation","sign_citation":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP/action/citation_signature","submit_replication":"https://pith.science/pith/HVVTNHOKGU3QCOGG4WRFBTMSFP/action/replication_record"}},"created_at":"2026-07-05T10:48:19.049969+00:00","updated_at":"2026-07-05T10:48:19.049969+00:00"}