为能够学习、验证和在动态环境中适应的智能体构建基础世界模型
Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
AI总结：
本文提出基础世界模型，通过可学习奖励模型、自适应形式验证、在线抽象校准和测试时合成，使智能体在动态环境中可靠学习、验证和适应。
摘要：
下一代自主智能体不仅需要高效学习，还必须可靠地行动，并在开放世界中适应其行为。标准方法通常假设固定任务和环境，缺乏或几乎没有新颖性，这限制了世界模型支持必须在条件变化时进化策略的智能体的能力。本文提出了基础世界模型的愿景：持久且可组合的表示，统一强化学习、反应/程序合成和抽象机制。我们提出了一项围绕四个组件的议程：(i) 从规范学习奖励模型以支持有明确目标的优化；(ii) 集成在整个学习过程中的自适应形式验证；(iii) 在线抽象校准以量化模型预测的可靠性；(iv) 由验证器引导的测试时合成和世界模型生成。这些组件共同使智能体能够合成可验证的程序，从少量交互中推导出新策略，并在适应新颖性时保持正确性。所得到的框架将基础世界模型定位为学习、推理和适应的基质，为不仅行动良好而且能解释和证明其行为的智能体奠定基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
