Language Models Struggle to Use Representations Learned In-Context

Though language models (LMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligence research: creating an artificial system that can adapt its behavior to radically new contexts upon deployment (Shi et al., 2024) . One important step towards this goal is to create systems that can induce rich representations of data that are seen in-context, and then flexibly deploy these representations to accomplish goals (Lampinen et al., 2024) . Recently, Park et al. (2025a) demonstrated that current LMs are indeed capable of inducing such representation from context (i.e., in-context representation learning). The present study investigates whether LMs can use these representations to complete simple downstream tasks. We first assess whether open-weights LMs can use in-context representations for next-token prediction, and then probe models using a novel task, adaptive world modeling. In both tasks, we find evidence that open-weights LMs struggle to deploy representations of novel semantics that are defined in-context, even if they encode these semantics in their latent representations. Furthermore, we assess closed-source, state-of-the-art reasoning models on the adaptive world modeling task, and demonstrate that even the most performant LMs cannot reliably leverage novel patterns presented in-context. Overall, this work seeks to inspire novel methods for encouraging models to not only encode information presented in-context, but to do so in a manner that supports flexible deployment of this information.