Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs

Researchers introduce LATTE, a framework that enables teams of large language models to coordinate work dynamically through shared task graphs rather than fixed hierarchies or fully unstructured approaches. The system reduces token usage, execution time, and coordination failures while maintaining or improving accuracy compared to existing multi-agent LLM coordination methods.

LATTE addresses a fundamental challenge in deploying LLM teams: balancing structure with adaptability. Existing approaches force a difficult choice between rigid, pre-defined task pipelines that lack flexibility and chaotic unstructured collaboration that wastes resources through redundant work and communication overhead. The framework draws inspiration from distributed computing systems, applying proven principles of partial observability and constrained communication to LLM coordination.

This work emerges from the broader trend of moving beyond single-model inference toward collaborative multi-agent systems. As LLMs become more capable, researchers recognize that ensemble approaches—where agents specialize in different functions—can outperform single models on complex tasks. However, managing such teams efficiently remains unsolved, with hidden costs in token consumption and wall-clock time offsetting accuracy gains.

The practical impact centers on operational efficiency for organizations deploying LLM-based systems at scale. Token usage directly translates to computational costs; wall-clock time affects user experience; coordination failures introduce both technical debt and reliability issues. LATTE's demonstrated improvements across these dimensions suggest meaningful cost reductions for production systems, particularly for complex reasoning tasks requiring multi-step collaboration.

The results across multiple base models and tasks indicate the framework's robustness. Developers building AI applications should monitor LATTE's evolution and eventual implementation, as it could become standard infrastructure for multi-agent systems. The competitive comparison against MetaGPT and hierarchical designs positions this as a genuine advancement rather than marginal improvement.

• →LATTE dynamically constructs shared task graphs enabling LLM teams to adapt coordination without rigid pre-planning or chaotic unstructured collaboration
• →Framework reduces token consumption, execution time, and coordination failures like file conflicts while matching or exceeding accuracy of fixed-structure approaches
• →Design inspired by distributed systems principles allows agents to operate under partial observability and communication constraints
• →Tested across multiple LLM base models, suggesting broad applicability rather than single-model optimization
• →Addresses scalability challenges for production LLM applications where token costs and wall-clock time directly impact operational expenses