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This paper proposes CURL, a plug-in adapter that uses estimator uncertainty to allocate pretrained LLM semantic capacity for improving heterogeneous treatment effect (CATE) estimation. It introduces two role-conditioned prompts to construct assignment- and heterogeneity-oriented representations, improving ten host learners on four benchmarks.