Quo Vadis, World Modeling?

2026-08-03

AI Summary

This work addresses the limitations of traditional world models, which are confined to predicting physical states and thus fail to provide the diverse, actionable feedback necessary for agents’ continual learning. To overcome this, the paper proposes Agent-Centric Interactive World Proxies that extend world modeling beyond state transitions to multimodal information transfer, encompassing execution outcomes, experience retrieval, skill invocation, and validation signals. The framework innovatively decomposes the world proxy into six functional modules—dynamics, spatial, execution, memory, skill, and reward—and integrates them within a hierarchical augmentation architecture. This design operates across three levels: inference guidance, training optimization, and co-evolution of proxies and agents. Empirical results demonstrate that this paradigm substantially enhances agents’ planning capabilities, learning efficiency, and potential for sustained evolution.

Abstract

Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more controllable feedback before committing to real actions. Classical world models instantiate this proxy primarily through future physical-state prediction, a formulation useful yet narrow for agents that require actionable feedback beyond raw state transitions. In this work, we conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile feedback for continually improving agents. To systematically map this design space, we organize world proxies into six functional forms based on their feedback modalities: dynamics, spatial, execution, memory/experience, skill, and reward/verification proxies, which together characterize the primary ways world modeling serves agent improvement. We further analyze how these proxies empower agents across three progressive levels: L.1 Inference-Time Guidance, where proxy outputs enrich in-context information for superior decisions; L.2 Training-Time Optimization, where proxy outputs yield rewards, critiques, or synthetic rollouts for policy learning; and L.3 Agent-Proxy Co-Evolution, where real-environment evidence continuously updates both the proxy and the agent for co-evolution. Ultimately, this work recasts world modeling into an agent-centric paradigm, establishing a roadmap for building world proxies that empower agents to plan better, learn faster, and evolve continually.

Problem

• world modeling
• agent-centric
• interactive feedback
• information transitions
• continual improvement

Innovation

• world modeling
• agent-centric proxies
• interactive feedback
• co-evolution
• continual learning