Decentralised Consensus Learning Networks: SME Rotation Without Centralised Reward
2026-07-27 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors propose a new way for AI agents to learn together without relying on a single central reward system. Instead, agents gain trust and expertise by agreeing with their peers, with expert status assigned dynamically based on who is most consistent with the group. They tested this system in many simulations, finding that in simpler settings expert roles rotate often, but in high-dimensional belief spaces, expertise tends to concentrate in one agent. This shows that in complex scenarios, consensus and leadership can naturally emerge without external labels. The study highlights how decentralized consensus can create flexible and scalable learning dynamics.
decentralized learningmulti-agent systemsconsensus dynamicspeer validationexpertise emergencegraph topologybelief representationdimensionalityphase transitionstrust allocation
Authors
Florin Neagu
Abstract
Centralised reward signals dominate modern AI learning systems, but they impose a single external definition of correct or valuable knowledge. We present a decentralised, consensus-based multi-agent learning framework in which expertise emerges through peer validation rather than prescribed reward. Agents update beliefs via weighted social consensus, while trust is allocated according to competence inferred from peer consistency instead of ground truth. Subject-matter expert (SME) status is assigned dynamically as a top-percentile competence rank rather than a fixed label. We evaluate the framework across 84 simulation runs spanning 30 to 10,000 agents, multiple graph topologies, sparse large-scale networks, scalar and vector belief representations, dimensionality sweeps (D=1-500), multi-seed robustness tests, and parameter sensitivity analyses. Phase 1 shows that SME rotation is robust, persistent, topology-invariant, and scale-invariant: 90-100% of agents attain SME status, with most expertise turnover occurring after belief convergence and increasing with network size. Phases 2 and 3 show that vector beliefs introduce heterogeneous convergence with cascade dynamics and reveal five distinct dynamical regimes as belief dimensionality increases. At high dimensionality (D=150-200), the network reaches stable partial consensus while expertise becomes increasingly concentrated in a single agent. ETA sensitivity analysis demonstrates that this concentration is driven by belief dimensionality rather than stochastic noise. We interpret this behaviour as an emergent property of decentralised learning: in complex high-dimensional consensus spaces, the agent most consistently aligned with the collective belief naturally emerges as the recognised expert.