Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems

2026-01-26

AI Summary

This work addresses the high coordination costs and low efficiency commonly encountered by multi-agent vision systems under information asymmetry, problems exacerbated by existing approaches that overlook the structural nature of uncertainty and lack economic sustainability. To this end, we propose Agora, a novel framework that formalizes epistemic uncertainty as a tradable asset and establishes a decentralized uncertainty market, wherein agents are incentivized through economic mechanisms to exchange uncertainty at the levels of perception, semantics, and reasoning. Agora integrates vision-language models with multi-armed bandits and Thompson Sampling to devise market-aware brokerage strategies. Experiments demonstrate that Agora significantly outperforms current methods across five multimodal benchmarks, achieving an 8.5% accuracy gain on MMMU while reducing coordination costs by more than threefold.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Vision-Language Models (VLMs) enable powerful multi-agent systems, but scaling them is economically unsustainable: coordinating heterogeneous agents under information asymmetry often spirals costs. Existing paradigms, such as Mixture-of-Agents and knowledge-based routers, rely on heuristic proxies that ignore costs and collapse uncertainty structure, leading to provably suboptimal coordination. We introduce Agora, a framework that reframes coordination as a decentralized market for uncertainty. Agora formalizes epistemic uncertainty into a structured, tradable asset (perceptual, semantic, inferential), and enforces profitability-driven trading among agents based on rational economic rules. A market-aware broker, extending Thompson Sampling, initiates collaboration and guides the system toward cost-efficient equilibria. Experiments on five multimodal benchmarks (MMMU, MMBench, MathVision, InfoVQA, CC-OCR) show that Agora outperforms strong VLMs and heuristic multi-agent strategies, e.g., achieving +8.5% accuracy over the best baseline on MMMU while reducing cost by over 3x. These results establish market-based coordination as a principled and scalable paradigm for building economically viable multi-agent visual intelligence systems.

Problem

• multi-agent systems
• visual uncertainty
• information asymmetry
• cost-efficient coordination
• vision-language models

Innovation

• uncertainty trading
• multi-agent coordination
• vision-language models
• market-based AI
• epistemic uncertainty