{"paper":{"title":"Position: Agentic AI System Is a Foreseeable Pathway to AGI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Agentic AI systems using DAG topologies achieve exponentially superior generalization and sample efficiency compared to monolithic models.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jun Wang, Junwei Liao, Muning Wen, Shuai Li, Weinan Zhang","submitted_at":"2026-05-13T04:00:43Z","abstract_excerpt":"Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial General Intelligence. Instead, we identify Agentic AI as a necessary paradigm for mastering the complex, heterogeneous distribution of real-world tasks. Through rigorous theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, progressing from simple routing mechanisms to general Directed Acyclic Graph (DAG) topologies. We demonstrate that Agentic AI achieves exponentially su"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We demonstrate that Agentic AI achieves exponentially superior generalization and sample efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The optimization constraints of monolithic learners are fundamentally more limiting than those of agentic DAG systems, without specified independent benchmarks or derivations in the abstract.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Agentic AI systems using DAG topologies achieve exponentially superior generalization and sample efficiency compared to monolithic models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bc2a088f05dc567ccfda5146152796311b62b309f5a25578b0debf2b2d3af439"},"source":{"id":"2605.12966","kind":"arxiv","version":1},"verdict":{"id":"8c236922-2292-45da-a2f4-b1e0f9ed739d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:05:18.066719Z","strongest_claim":"We demonstrate that Agentic AI achieves exponentially superior generalization and sample efficiency.","one_line_summary":"Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The optimization constraints of monolithic learners are fundamentally more limiting than those of agentic DAG systems, without specified independent benchmarks or derivations in the abstract.","pith_extraction_headline":"Agentic AI systems using DAG topologies achieve exponentially superior generalization and sample efficiency compared to monolithic models."},"references":{"count":90,"sample":[{"doi":"","year":2000,"title":"Langley , title =","work_id":"6cd283dc-0548-45e9-af07-6bc1005593ad","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1980,"title":"T. M. Mitchell. The Need for Biases in Learning Generalizations. 1980","work_id":"6b09bca6-ef5d-4a83-8c8e-219f23cbd761","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"M. J. Kearns , title =","work_id":"8efd8073-6f5d-45c5-94d5-62d366b52518","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1983,"title":"Machine Learning: An Artificial Intelligence Approach, Vol. I. 1983","work_id":"51835800-f16e-4534-8339-d3ea09147556","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2000,"title":"R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification. 2000","work_id":"a24cb892-7ac9-4509-8f5b-abbdfb15998b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":90,"snapshot_sha256":"5fb3ee69c1936b36bed6f3c6b9c30b330c3776e354397a18c5ac143be0e6d932","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b903163123799b265bb81aab6afb5b5a95e01ba83a9a7a6aafba0fe78ed7b328"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}