Causal Reflection with Language Models
Introduction
Traditional reinforcement learning (RL) maximizes rewards without modeling cause-effect relationships, limiting the ability to adapt or generalize. Similarly, large language models (LLMs) lack an inherent grasp of causality in dynamic contexts, often failing in novel scenarios. While causal reinforcement learning (CRL) offers a promising direction by incorporating causal models, existing methods often assume fixed causal structures, failing to capture the evolving nature of real-world systems where relationships can change over time. These shortcomings create critical performance bottlenecks, limiting agents' ability to generalize beyond their training environments.
Abi Aryan
Zac Yung-Chun Liu
Abide AI
Causal Reflection Framework
We introduce Causal Reflection, a novel framework for modeling causality in dynamic environments, which addresses current limitations through 3 key contributions: 1. A temporal, action-based causal function capable of modeling both linear and nonlinear causal relationships in dynamic systems. 2. A Reflect mechanism that enables agents to self-correct their internal causal models by generating and testing hypotheses about prediction errors. 3. A principled integration schema as Causal Reasoning where LLMs act as structured interpreters, translating the formal outputs of our causal model into natural language explanations and counterfactuals.
Causal Function
State (St): a vector representing the environment’s configuration.
Action (At): an intervention on the system.
Time (Tt): temporal ordering.
Perturbation (δ): small, unobserved influences that trigger nonlinear, chaotic effects.
The explicit modeling of δ allows the framework to account for non- stationarity and structural breaks. We formulate a causal function, C, that maps these components to a future state. The function progressively incorporates complexity, starting with direct effects, then temporal delays (k), and finally the perturbation factor δ.
This function models systems where causality is not only delayed but also nonlinear and time- varying. The inclusion of δ allows the function to model how small causes can propagate to produce disproportionate effects.
Reflection Mechanism
The Reflect function that enables an agent to learn from discrepancies between predicted and observed outcomes. When the prediction error exceeds a threshold, a causal mismatch is detected. The Reflect mechanism is invoked to generate a set of causal hypotheses Ht that could explain the error. H represents a candidate hypothesis, such as a misestimated perturbation factor δ or an unmodeled confounder. This elevates reflection from a heuristic process to structured causal inference.
Existing self-reflection paradigms, such as Reflexion framework, improve agent behavior through verbal reinforcement and meta-cognitive feedback, however, its reflection process remains heuristic and unstructured. In contrast, Causal Reflection elevates reflection from intuition to structured causal inference, prompting the agent to generate specific, falsifiable hypotheses about its world model.
LLM Integration & Causal Reasoning
Our framework utilizes LLMs as generative inference engines that translate the formal, symbolic outputs of causal model into explanations. The LLM receives the causal tuple (St, At, Tt, δ) and model’s prediction, and generates natural language outputs like causal explanations or counterfactuals. This grounds the LLM’s output in a verifiable causal structure, mitigating the risk of ungrounded, ”hallucinated” reasoning. With the causal contexts, explanations, and counterfactuals, we produce reasoning steps as “causal reasoning”.
Implementation & Benchmarking
We implemented and benchmarked our causal reflection framework in DoorKey task of the mini-grid environment. The causal reflective agents have 20-25% higher success rate than the baseline PPO agents. The full benchmark results will be published in our next paper.
Limitations
Causal graph (DoorKey)
Scalability: Computational challenges of modeling high-dimensional, dynamic causal systems where state representations grow large and causal inference becomes difficult.
LLM fidelity: The framework depends on LLMs faithfully translating formal causal outputs into natural language, future work will address hallucinations and other translation errors by developing methods to quantify and mitigate them.
Key components
Env: Dynamic env framework (AbideGym, will be open source)
Adapter: Centralize env logic for planner.
Planner: Select actions by searching over action sequences while consulting the learned causal graph via the adapter.
Causal graph: facilitate causal learning.