RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing
LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built around a predefined execution paradigm, such as single-round, multi-round, or agentic routing, which fixes how model calls are organized and limits adaptation to different query needs. We propose RouteWeaver, a unified routing framew
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LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built around a predefined execution paradigm, such as single-round, multi-round, or agentic routing, which fixes how model calls are organized and limits adaptation to different query needs. We propose RouteWeaver, a unified routing framew
RouteWeaver introduces a unified framework for LLM routing that dynamically selects and coordinates heterogeneous models, integrating various execution paradigms. This approach is crucial for optimizing resource utilization and improving performance in complex, multi-task AI systems.