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This paper introduces LLAMIA-Bench and a method called latent state internalization to enhance collaboration between language models and non-language agents, showing that internalizing continuous representations outperforms verbalization and matches frontier models like GPT-5.1.
The paper introduces LLAMIA-Bench, a benchmark for collaborative chess tasks between language models and non-language agents, and proposes latent state internalization to outperform text-based verbalization, with a 14B model matching or exceeding frontier models like GPT-5.1.