Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes. Highly structured methods rely on fixed roles, pipelines, or task decompositions assigned a priori. In contrast, fully unstructured teams enable adaptability and exploration but suffer from inefficiencies such as error propagation, inter-agent conflicts, and wasted resources (measured in time, tokens, or file operations). We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems, where processors must operate under partial observability and communication constraints. In LATTE, a team of a...
| Authors | Elizabeth Mieczkowski, Alexander Ku, Tiwalayo Eisape et al. |
|---|---|
| Primary category | cs.MA |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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