因果反思与语言模型
Causal Reflection with Language Models
AI总结：
针对大语言模型和强化学习智能体缺乏稳健因果推理的问题，提出因果反思框架，显式建模因果动态函数并定义反思机制，使智能体能够自我修正并传达因果理解。
摘要：
尽管大语言模型展现出令人印象深刻的流畅性和事实回忆能力，但它们在稳健的因果推理方面存在困难，常常依赖于虚假的相关性和脆弱的模式。同样，传统的强化学习智能体也缺乏因果理解，它们优化奖励而不建模行动为何导致结果。我们引入了因果反思（Causal Reflection），这是一个将因果关系显式建模为状态、行动、时间和扰动上的动态函数的框架，使智能体能够推理延迟和非线性效应。此外，我们定义了一个正式的反思（Reflect）机制，用于识别预测结果与观察结果之间的不匹配，并生成因果假设以修正智能体的内部模型。在该架构中，大语言模型并非作为黑箱推理器，而是作为结构化推理引擎，将正式的因果输出转化为自然语言解释和反事实。我们的框架为因果反思智能体奠定了理论基础，这些智能体能够在不断变化的环境中适应、自我纠正并传达因果理解。
英文摘要：
While LLMs exhibit impressive fluency and factual recall, they struggle with robust causal reasoning, often relying on spurious correlations and brittle patterns. Similarly, traditional Reinforcement Learning agents also lack causal understanding, optimizing for rewards without modeling why actions lead to outcomes. We introduce Causal Reflection, a framework that explicitly models causality as a dynamic function over state, action, time, and perturbation, enabling agents to reason about delayed and nonlinear effects. Additionally, we define a formal Reflect mechanism that identifies mismatches between predicted and observed outcomes and generates causal hypotheses to revise the agent's internal model. In this architecture, LLMs serve not as black-box reasoners, but as structured inference engines translating formal causal outputs into natural language explanations and counterfactuals. Our framework lays the theoretical groundwork for Causal Reflective agents that can adapt, self-correct, and communicate causal understanding in evolving environments.
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。
