When Do Causal World Models Help Modular LLM Agents
arXiv:2610.00012v1 introduces FedCausalCompose, a causal world-model framework for modular LLM agents that uses local intervention–response evidence to recover cross-module interfaces. The paper shows observational world models can incur irreducible interventional error, that interface recovery improves with intervention-response coverage, and that causal composition can outperform non-causal bounds when coverage and local mechanism error are controlled; experiments show causal interfaces mainly help in structured tool/API environments and less so in dialogue or narrative settings unless the causal info is made action-relevant.