{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NIYX32CX7KPOOA3WHUR4WKKKGF","short_pith_number":"pith:NIYX32CX","schema_version":"1.0","canonical_sha256":"6a317de857fa9ee703763d23cb294a314833e7821f8d7489d4cf9a0c88c85f6a","source":{"kind":"arxiv","id":"2304.04370","version":6},"attestation_state":"computed","paper":{"title":"OpenAGI: When LLM Meets Domain Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jianchao Ji, Juntao Tan, Kai Mei, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Yongfeng Zhang, Zelong Li","submitted_at":"2023-04-10T03:55:35Z","abstract_excerpt":"Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools, plugins, or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research and development platform designed for solving multi-step, real-w"},"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":"2304.04370","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-04-10T03:55:35Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"54885f670f119dcd402d34c9beb385a3b9b7d7d1bd3174888962aede6c8f0c36","abstract_canon_sha256":"2fe75ce10a3592432df52c5511df99f366ac642c3ffccf3913c2d3c2cb56f475"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:41.260195Z","signature_b64":"dCIeHJoX/li9/I4+0xKzLINTAJ185WgiWXAZW0dKn2StBAfk981Z2z46dW52hkflsm8EZSGWfrVTb0lZjjBhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a317de857fa9ee703763d23cb294a314833e7821f8d7489d4cf9a0c88c85f6a","last_reissued_at":"2026-07-05T07:08:41.259564Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:41.259564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenAGI: When LLM Meets Domain Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jianchao Ji, Juntao Tan, Kai Mei, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Yongfeng Zhang, Zelong Li","submitted_at":"2023-04-10T03:55:35Z","abstract_excerpt":"Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools, plugins, or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research and development platform designed for solving multi-step, real-w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.04370","kind":"arxiv","version":6},"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/2304.04370/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":"2304.04370","created_at":"2026-07-05T07:08:41.259633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.04370v6","created_at":"2026-07-05T07:08:41.259633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.04370","created_at":"2026-07-05T07:08:41.259633+00:00"},{"alias_kind":"pith_short_12","alias_value":"NIYX32CX7KPO","created_at":"2026-07-05T07:08:41.259633+00:00"},{"alias_kind":"pith_short_16","alias_value":"NIYX32CX7KPOOA3W","created_at":"2026-07-05T07:08:41.259633+00:00"},{"alias_kind":"pith_short_8","alias_value":"NIYX32CX","created_at":"2026-07-05T07:08:41.259633+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.08332","citing_title":"ORFS-agent: Tool-Using Agents for Chip Design Optimization","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2306.06070","citing_title":"Mind2Web: Towards a Generalist Agent for the Web","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2304.15010","citing_title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2311.16502","citing_title":"MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08904","citing_title":"OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces","ref_index":98,"is_internal_anchor":false},{"citing_arxiv_id":"2309.07864","citing_title":"The Rise and Potential of Large Language Model Based Agents: A Survey","ref_index":212,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08033","citing_title":"IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF","json":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF.json","graph_json":"https://pith.science/api/pith-number/NIYX32CX7KPOOA3WHUR4WKKKGF/graph.json","events_json":"https://pith.science/api/pith-number/NIYX32CX7KPOOA3WHUR4WKKKGF/events.json","paper":"https://pith.science/paper/NIYX32CX"},"agent_actions":{"view_html":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF","download_json":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF.json","view_paper":"https://pith.science/paper/NIYX32CX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.04370&json=true","fetch_graph":"https://pith.science/api/pith-number/NIYX32CX7KPOOA3WHUR4WKKKGF/graph.json","fetch_events":"https://pith.science/api/pith-number/NIYX32CX7KPOOA3WHUR4WKKKGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF/action/storage_attestation","attest_author":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF/action/author_attestation","sign_citation":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF/action/citation_signature","submit_replication":"https://pith.science/pith/NIYX32CX7KPOOA3WHUR4WKKKGF/action/replication_record"}},"created_at":"2026-07-05T07:08:41.259633+00:00","updated_at":"2026-07-05T07:08:41.259633+00:00"}