Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases
A deliberative curation protocol combining three governance layers (knowledge artifact lifecycle, reputation-weighted deliberative voting, and graduated sanctions) is proposed. Agent-based simulation with 100 agents shows improved resilience under adversity compared to majority voting, with commit-reveal vote concealment being the most impactful component.
[2606.00007] Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases
[Submitted on 27 Mar 2026]
Title:Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases
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Abstract: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
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