Language Models Struggle to Use Representations Learned In-Context

The article examines whether large language models (LLMs) can effectively use representations they learn in-context to perform downstream tasks. Experiments with open‑weight LLMs on next‑token prediction and a new adaptive world modeling task reveal that these models struggle to deploy novel semantics defined in-context, even when such semantics are encoded in their latent representations. The study also shows that state‑of‑the‑art closed‑source reasoning models similarly fail to reliably leverage in‑context patterns, highlighting a gap in flexible representation usage.

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• llms
• benchmarks