Exploring Collaboration between a language and a non-language agent
LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in many important domains like game playing and robotics, the strongest available agents are not language models. Integrating non-language agents with LLMs would require \emph{verbalization}: compressing their rich continuous representations into sparse textual summaries at each interaction step. To study whether verbalization constitutes a bottleneck, we introduce \textsc{LLAMIA-Bench}, a suite of six diverse collaborative chess tasks spanning three facets: behavioral imitation, state assessment, and natural-language explanation. Each task instan...
| Authors | Harini S, Somesh Singh, Yaman K Singla et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 August 2026 |
| Last updated at source | 2 September 2026 |
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