arXiv: cs.AI preprintsInternational5 November 2026
Tools Need Not Speak in Text: Latent Context Compression for Agents
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arXiv:2611.06500v1 Announce Type: new Abstract: Large language model (LLM) agents typically append raw tool outputs to their context. Over multi-step interactions, long and noisy observations incur substantial context overhead and dilute the agent's attention to task-relevant information. Existing token pruning and summarization methods remain constrained by discrete text representations, limiting the achievable compression ratios. In this work, we challenge the prevailing assumption that agents must consume tool observations as explicit text tokens. To this end, we propose Coca-Cola, a contin
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