Causal Reflection with Language Models
This addresses the issue of spurious correlations and lack of causal modeling in AI systems, offering a theoretical foundation for more adaptive agents, though it appears incremental as it builds on existing causal reasoning concepts.
The paper tackles the problem of robust causal reasoning in language models and reinforcement learning agents by introducing Causal Reflection, a framework that models causality dynamically and includes a Reflect mechanism for self-correction, resulting in agents that can adapt and explain causal understanding.
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.