Can separating causal models from language models improve reasoning?

Two failure modes meet here. LLMs reason fluently but lean on spurious correlations and brittle patterns rather than robust causality; classical RL agents optimize reward without modeling why actions produce outcomes. Causal Reflection proposes a division of labor that fixes both: an explicit, formal causal model represents causality as a dynamic function over state, action, time, and perturbation — capturing delayed and nonlinear effects — and a formal Reflect mechanism detects mismatches between predicted and observed outcomes, generating causal hypotheses to revise the model. The LLM is deliberately not the reasoner; it serves as a structured inference engine that translates the formal causal outputs into natural-language explanations and counterfactuals.

The conceptual keeper is the architecture, not the (currently theoretical) implementation: keep causal reasoning in a verifiable formal substrate and use the LLM only for its genuine strength — rendering formal results in language. This sidesteps asking the model to be a causal reasoner, a role it performs unreliably.

This connects two vault threads. It builds on Why do LLMs handle causal reasoning better than temporal reasoning? — LLMs have causal fluency but not causal rigor, exactly the gap a formal model fills — and it shares the externalize-causality move with Can we extract causal belief networks from interview conversations?, which similarly keeps the causal structure outside the model and uses language only at the interface.