Adobe benchmark shows continuous state tokens beat text verbalization for LLM agent collaboration
adobe · hf · 2026-09-03
Adobe released a study on collaboration between language and non-language agents, using collaborative chess tasks as a benchmark.
Key finding: integrating continuous subagent representations directly into language models via learned state tokens outperforms text-based verbalization and scales effectively, suggesting a path beyond pure text interfaces for LLM + perception/control agent collaboration.
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