As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge. Human platform governance mechanisms do not transfer directly: agent statelessness undermines deterrence-based sanctions, model homogeneity violates independence assumptions underlying crowd wisdom, and sycophancy collapses deliberative consensus.
We propose a deliberative curation protocol combining three governance layers: (1) a knowledge artifact lifecycle formalized as a labeled transition system; (2) reputation-weighted deliberative voting integrating Beta Reputation with EigenTrust amplification; and (3) graduated sanctions adapted for stateless agents, including broken agent handling distinguishing malfunction from adversarial behavior.
We evaluate the protocol through agent-based simulation with 100 agents across seven behavioral archetypes under two adversity scenarios (30 seeds, paired t-tests). The protocol trades modest precision under benign conditions for substantially better resilience under adversity: 0.826 vs 0.791 for majority vote under moderate adversity (p<0.001), widening to 0.807 vs 0.740 under stress (p<0.001). The protocol degrades roughly three times more slowly than majority vote. Ablation analysis identifies commit-reveal vote concealment as the most impactful single component (8.2-8.6pp precision improvement, p<0.001), outperforming reputation weighting and deliberation combined. Graduated sanctions were not exercised in simulation and remain empirically unvalidated.
AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems
Department of Information Engineering, Computer Science, and Mathematics, Center of Excellence for Research DEWS, University of L’Aquila（信息工程、计算机科学和数学系，DEWS研究中心，拉奎拉大学）
Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Wuhan University of Science and Technology（武汉科技大学）；National University of Singapore（新加坡国立大学）；Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；BOSS Zhipin（BOSS直聘）
Deliberative and Conceptual Inference in Service Robots
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas（应用数学与系统研究所）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Facultad de Estudios Superiores Aragón（阿拉贡高等研究学院）
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）
Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
University of Cambridge（剑桥大学）；OATML, University of Oxford（牛津大学OATML实验室）；University of Toronto（多伦多大学）；Shanghai Jiao Tong University（上海交通大学）；Samsung AI Center（三星人工智能中心）
ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge
Virginia Tech（弗吉尼亚理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of California, Berkeley（加州大学伯克利分校）
CurateGPT: A flexible language-model assisted biocuration tool
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Queen Mary University of London（伦敦玛丽女王大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所（欧洲分子生物学实验室-欧洲生物信息研究所））；Berlin Institute of Health - Charité, Universitätsmedizin（柏林健康研究所-夏里特医科大学）
