SG-XICL: Coordinating Evidence and Reasoning Scopes in Large-Label In-Context Learning

In-context learning (ICL) enables large language models (LLMs) to perform new tasks from a small set of demonstrations without task-specific parameter updates. With a large label space, however, a separate design question is which labels should receive dedicated demonstration evidence and explicit reasoning for a given query. We propose Subspace-Guided X-ICL (SG-XICL), which constructs a query-conditioned candidate label subspace to coordinate label-aligned demonstration evidence and candidate-wise reasoning while retaining the full task label space for final prediction. Across six language models and eight classification datasets, SG-XICL achieves higher average Accuracy than the four evaluated ICL baselines on their supported comparisons. Compared with full-label X²-ICL, SG-XICL keeps dedicated reasoning local and incurs substantially lower online token burden over common-supported conditions. These results support separating the scope of dedicated explicit processing from the full prediction space in large-label ICL.