When Demonstrations Fail: Representation-Resource Matching in In-Context Adaptation

In-context learning lets a language model acquire a new task from a handful of demonstrations, yet some mapping tasks are learned and others of near-identical surface form are not. We ask a causal question that existing work, which describes the representation in-context learning forms, does not: which representation source makes a demonstration usable? Grounded in low-resource multilingual translation, we find that correct source–target correspondence is a graded adaptation signal, and that its benefit is gated by representation accessibility: injectively corrupting the source—holding content, demonstrations, and decoding fixed—collapses it, and this gating is selective: a surface bijection survives the corruption that collapses a semantic mapping. In controlled tasks, a computed intermediate constraint separates via direct-intermediate probes and an explicit rule control; a held-out-query control shows the numeric task’s apparent rule induction is largely inflated by support overlap, while a relational task, whose query pair is held out by construction, reveals a genuine computation failure, and supplying the correct intermediate rescues both. Finally, a cross-model check shows the selective dissociation and the intermediate rescue are positive across five model families (two weaker; Appendix), though their magnitudes do not rank with the tested scale range. Together, these intervention-based results identify a representation-resource constraint on in-context adaptation—an intervention-based behavioral taxonomy of whether the source a task demands is available during adaptation—that scalar accuracy does not capture.