DRIFT: Breaking Domain Entanglement in Multi-Source Out-of-Distribution In-Context Learning

In this paper, we study multi-source out-of-distribution in-context learning (OOD ICL), where demonstrations from heterogeneous source domains are used to support inference on an unseen target distribution for Large Language Models (LLMs). We reveal that increasing source diversity can unexpectedly degrade ICL performance. To explain this phenomenon, we establish a Bayesian framework that decouples demonstration retrieval from LLM inference, and theoretically characterize how domain information entangled in retrieval representations propagates source-dependent bias into the predictive distribution. Our analysis further shows that suppressing such domain information yields a tighter bound on the divergence from the target predictive distribution. Motivated by this result, we propose a domain-disentangled retrieval framework that preserves transferable semantics while minimizing domain-specific information during demonstration selection. Experiments on multiple LLMs and OOD benchmarks consistently validate our theoretical findings and demonstrate improved robustness under multi-source shifts, establishing a new perspective for multi-source OOD ICL.