Causal Reflection with Language Models

TL;DR

This paper introduces Causal Reflection, a framework enabling language models and agents to explicitly model, reason about, and revise their understanding of causality, improving robustness and adaptability in complex environments.

Contribution

It presents a novel causal modeling framework and a Reflect mechanism allowing agents to identify and correct causal reasoning errors, advancing AI's causal understanding capabilities.

Findings

Framework enables explicit causal reasoning over state, action, and time.

Reflect mechanism identifies mismatches and generates hypotheses for model revision.

Lays theoretical groundwork for adaptive, self-correcting causal agents.

Abstract

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.