构建可自适应的通用世界模型，让智能体在变化环境中持续学习并可靠决策。
Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
- 用可学习的奖励模型实现目标驱动的学习
- 在线校准抽象以量化预测可靠性，支持动态验证
- 适合需要长期适应与可解释行为的自主系统研究者
下一代自主智能体不仅需高效学习，还需在开放世界中可靠行动并动态调整策略。传统方法依赖静态任务与环境，难以支持随条件变化而演化的智能体。本文提出基础世界模型的愿景：具备持久性与组合性的表征体系，整合强化学习、反应式/程序合成与抽象机制。核心框架包含四部分：(i) 从规范中学习奖励模型，提供明确优化目标；(ii) 将形式化验证融入学习全过程；(iii) 在线抽象校准，量化模型预测可靠性；(iv) 由验证器引导的测试时程序合成与世界模型生成。该框架使智能体能合成可验证程序，仅通过少量交互即生成新策略，并在面对新情况时保持正确性。基础世界模型成为学习、推理与适应的底层支撑，为既能良好执行又能解释行为的智能体奠定基础。
原文摘要 · Abstract (English)
The next generation of autonomous agents must not only learn efficiently but also act reliably and adapt their behavior in open worlds. Standard approaches typically assume fixed tasks and environments with little or no novelty, which limits world models' ability to support agents that must evolve their policies as conditions change. This paper outlines a vision for foundation world models: persistent, compositional representations that unify reinforcement learning, reactive/program synthesis, and abstraction mechanisms. We propose an agenda built around four components: (i) learnable reward models from specifications to support optimization with clear objectives; (ii) adaptive formal verification integrated throughout learning; (iii) online abstraction calibration to quantify the reliability of the model's predictions; and (iv) test-time synthesis and world-model generation guided by verifiers. Together, these components enable agents to synthesize verifiable programs, derive new policies from a small number of interactions, and maintain correctness while adapting to novelty. The resulting framework positions foundation world models as a substrate for learning, reasoning, and adaptation, laying the groundwork for agents that not only act well but can explain and justify the behavior they adopt.
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