让大模型学会像人类一样思考因果关系，自动修正错误推理。
Causal Reflection with Language Models
- 用动态函数建模因果关系，显式追踪状态、动作与时间的影响。
- 通过对比预测与实际结果，自动生成因果假设修正认知偏差。
- 适合需要可解释性与自我纠错能力的智能系统研发者。
尽管大语言模型在流畅性和事实记忆方面表现优异，但在稳健的因果推理上仍存在不足，常依赖虚假相关性和脆弱模式。传统强化学习智能体同样缺乏因果理解，仅优化奖励而未建模行为如何导致结果。本文提出因果反思（Causal Reflection）框架，将因果关系建模为随状态、动作、时间及扰动动态变化的函数，使智能体能够推理延迟和非线性效应。同时定义了形式化的反思机制，用于识别预测与观测结果之间的不一致，并生成因果假设以修正内部模型。在此架构中，大语言模型不再作为黑箱推理器，而是作为结构化推理引擎，将形式化的因果输出转化为自然语言解释与反事实推演。该框架为可适应、可自我修正且能沟通因果理解的智能体提供了理论基础。
原文摘要 · Abstract (English)
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.
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