{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KVN4GKPZUH2D3B3TZKMI6QIPDD","short_pith_number":"pith:KVN4GKPZ","schema_version":"1.0","canonical_sha256":"555bc329f9a1f43d8773ca988f410f18f8d788ecf31e68c87ca26e4d4af35272","source":{"kind":"arxiv","id":"2409.07487","version":2},"attestation_state":"computed","paper":{"title":"MoA is All You Need: Building LLM Research Team using Mixture of Agents","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Abhinav Raghunathan, Flora Huang, Leqi Zeng, Sandy Chen, Terrence C. Kim","submitted_at":"2024-09-04T19:00:59Z","abstract_excerpt":"Large Language Models (LLMs) research in the financial domain is particularly complex due to the sheer number of approaches proposed in literature. Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods in the sector due to its inherent groundedness and data source variability. In this work, we introduce a RAG framework called Mixture of Agents (MoA) and demonstrate its viability as a practical, customizable, and highly effective approach for scaling RAG applications. MoA is essentially a layered network of individually customized small language models (Hoffmann et al.,"},"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":"2409.07487","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-09-04T19:00:59Z","cross_cats_sorted":[],"title_canon_sha256":"7ba8a54c09e7ead52dfd5f708a3d3a6d16abebae9a1d19f46c6db44964dc90f2","abstract_canon_sha256":"b92eae72ce46eb899b919cf1db28ccaf898c8f8d9666244f78d83c0b2cddb9c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:03.689043Z","signature_b64":"F4UgNv/TezRCcN3wgaGRRfZ2b/2DNo1kod9zF5lMzMoqPIy9knYjGD1vF4c6RXc0+FbYlWLGRXfx4PvAy5A5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"555bc329f9a1f43d8773ca988f410f18f8d788ecf31e68c87ca26e4d4af35272","last_reissued_at":"2026-07-05T09:07:03.688604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:03.688604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MoA is All You Need: Building LLM Research Team using Mixture of Agents","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Abhinav Raghunathan, Flora Huang, Leqi Zeng, Sandy Chen, Terrence C. Kim","submitted_at":"2024-09-04T19:00:59Z","abstract_excerpt":"Large Language Models (LLMs) research in the financial domain is particularly complex due to the sheer number of approaches proposed in literature. Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods in the sector due to its inherent groundedness and data source variability. In this work, we introduce a RAG framework called Mixture of Agents (MoA) and demonstrate its viability as a practical, customizable, and highly effective approach for scaling RAG applications. MoA is essentially a layered network of individually customized small language models (Hoffmann et al.,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07487","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/2409.07487/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":"2409.07487","created_at":"2026-07-05T09:07:03.688665+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.07487v2","created_at":"2026-07-05T09:07:03.688665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07487","created_at":"2026-07-05T09:07:03.688665+00:00"},{"alias_kind":"pith_short_12","alias_value":"KVN4GKPZUH2D","created_at":"2026-07-05T09:07:03.688665+00:00"},{"alias_kind":"pith_short_16","alias_value":"KVN4GKPZUH2D3B3T","created_at":"2026-07-05T09:07:03.688665+00:00"},{"alias_kind":"pith_short_8","alias_value":"KVN4GKPZ","created_at":"2026-07-05T09:07:03.688665+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25030","citing_title":"MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2512.22579","citing_title":"SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD","json":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD.json","graph_json":"https://pith.science/api/pith-number/KVN4GKPZUH2D3B3TZKMI6QIPDD/graph.json","events_json":"https://pith.science/api/pith-number/KVN4GKPZUH2D3B3TZKMI6QIPDD/events.json","paper":"https://pith.science/paper/KVN4GKPZ"},"agent_actions":{"view_html":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD","download_json":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD.json","view_paper":"https://pith.science/paper/KVN4GKPZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.07487&json=true","fetch_graph":"https://pith.science/api/pith-number/KVN4GKPZUH2D3B3TZKMI6QIPDD/graph.json","fetch_events":"https://pith.science/api/pith-number/KVN4GKPZUH2D3B3TZKMI6QIPDD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD/action/storage_attestation","attest_author":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD/action/author_attestation","sign_citation":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD/action/citation_signature","submit_replication":"https://pith.science/pith/KVN4GKPZUH2D3B3TZKMI6QIPDD/action/replication_record"}},"created_at":"2026-07-05T09:07:03.688665+00:00","updated_at":"2026-07-05T09:07:03.688665+00:00"}