{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AFG64KWH74RMDD3LUXJXSELAUV","short_pith_number":"pith:AFG64KWH","schema_version":"1.0","canonical_sha256":"014dee2ac7ff22c18f6ba5d3791160a571b933293f474264d256f1ab77e68791","source":{"kind":"arxiv","id":"2409.02877","version":1},"attestation_state":"computed","paper":{"title":"Configurable Foundation Models: Building LLMs from a Modular Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Ao Zhang, Chaojun Xiao, Chenglei Si, Chenyang Song, Chenyang Zhao, Dazhi Jiang, Feng Yao, Guanyu Lin, Huimin Chen, Jingbo Shang, Khai Hao Moo, Maosong Sun, Shuo Wang, Weilin Zhao, Xiaozhi Wang, Xu Han, Yankai Lin, Yingfa Chen, Yufei Huang, Yuge Tu, Zexuan Zhong, Zhengyan Zhang, Zhiyuan Liu","submitted_at":"2024-09-04T17:01:02Z","abstract_excerpt":"Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation resources and scenarios requiring various abilities increasingly cumbersome. Inspired by modularity within the human brain, there is a growing tendency to decompose LLMs into numerous functional modules, allowing for inference with part of modules and dynamic assembly of modules to tackle complex tasks, such as mixture-of-experts. To highlight the "},"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":"2409.02877","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-09-04T17:01:02Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"b7bf2eaaf0842a01835893647d1560e6582548d07211f83075acb8d6f0df1830","abstract_canon_sha256":"b2330b193f01a51eda8064bd1e649415b7c684b1518d9882de0a49d2f0ec2954"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:11.835059Z","signature_b64":"mFTqi3wjKfmmushYHP3YLHUulc9IwstmlazC4Nugppi0bDHO6R9gQ7mDu22BHguSlYFSXk0Kggu5S+c9TvTIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"014dee2ac7ff22c18f6ba5d3791160a571b933293f474264d256f1ab77e68791","last_reissued_at":"2026-07-05T09:03:11.834649Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:11.834649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Configurable Foundation Models: Building LLMs from a Modular Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Ao Zhang, Chaojun Xiao, Chenglei Si, Chenyang Song, Chenyang Zhao, Dazhi Jiang, Feng Yao, Guanyu Lin, Huimin Chen, Jingbo Shang, Khai Hao Moo, Maosong Sun, Shuo Wang, Weilin Zhao, Xiaozhi Wang, Xu Han, Yankai Lin, Yingfa Chen, Yufei Huang, Yuge Tu, Zexuan Zhong, Zhengyan Zhang, Zhiyuan Liu","submitted_at":"2024-09-04T17:01:02Z","abstract_excerpt":"Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation resources and scenarios requiring various abilities increasingly cumbersome. Inspired by modularity within the human brain, there is a growing tendency to decompose LLMs into numerous functional modules, allowing for inference with part of modules and dynamic assembly of modules to tackle complex tasks, such as mixture-of-experts. To highlight the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02877","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/2409.02877/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":"2409.02877","created_at":"2026-07-05T09:03:11.834705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.02877v1","created_at":"2026-07-05T09:03:11.834705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02877","created_at":"2026-07-05T09:03:11.834705+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFG64KWH74RM","created_at":"2026-07-05T09:03:11.834705+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFG64KWH74RMDD3L","created_at":"2026-07-05T09:03:11.834705+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFG64KWH","created_at":"2026-07-05T09:03:11.834705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03979","citing_title":"Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories","ref_index":100,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18146","citing_title":"Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV","json":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV.json","graph_json":"https://pith.science/api/pith-number/AFG64KWH74RMDD3LUXJXSELAUV/graph.json","events_json":"https://pith.science/api/pith-number/AFG64KWH74RMDD3LUXJXSELAUV/events.json","paper":"https://pith.science/paper/AFG64KWH"},"agent_actions":{"view_html":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV","download_json":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV.json","view_paper":"https://pith.science/paper/AFG64KWH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.02877&json=true","fetch_graph":"https://pith.science/api/pith-number/AFG64KWH74RMDD3LUXJXSELAUV/graph.json","fetch_events":"https://pith.science/api/pith-number/AFG64KWH74RMDD3LUXJXSELAUV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV/action/storage_attestation","attest_author":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV/action/author_attestation","sign_citation":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV/action/citation_signature","submit_replication":"https://pith.science/pith/AFG64KWH74RMDD3LUXJXSELAUV/action/replication_record"}},"created_at":"2026-07-05T09:03:11.834705+00:00","updated_at":"2026-07-05T09:03:11.834705+00:00"}