Route Anything：通用 AI 系统中的模型与策略路由研究框架
Route Anything: A Study of Model and Policy Routing for General-Purpose AI Systems
Route Anything 框架通过动态选择和协调不同 AI 模型与策略，提升通用 AI 系统性能与效率。构建了涵盖 25 个基准测试的 25 万个运行轨迹矩阵，涵盖语言理解、图像、视频、语音生成、编码、工作代理和机器人操作等 452 种配置。
Published on Nov 3
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Abstract
Route Anything is a framework for dynamically selecting and coordinating specialized AI models and policies across diverse tasks to improve performance and efficiency.
Artificial general intelligence (AGI) requires a method that can understand, generate, reason, and act across diverse domains and modalities. Rather than relying on a single model to master every capability, a promising path toward AGI now is to orchestrate a heterogeneous ecosystem of specialized models, tools, and policies (candidates) into a unified AI system. At the heart of this vision lies a fundamental question: how can an AI system dynamically select and coordinate the most capable candidate for each task while balancing performance and computational cost? In this work, we present Route Anything, a unified framework for studying model and policy routing across general-purpose AI systems. We develop a unified theoretical foundation that characterizes optimal routing under quality--cost trade-offs and establishes how the information available to a router determines its attainable performance. To systematically investigate routing across diverse capabilities, we first construct an omni-modal trajectory matrix spanning 25 benchmarks across language understanding, image, video, and speech generation, coding and workplace agents, and robotic manipulation, comprising 374,401 scored runs from 452 candidate configurations. Building on this framework, we train and test different routers to investigate the potential of omni-modal model routing, the capabilities of learned routers, and the conditions under which routing improves efficiency. Our results reveal substantial opportunities for capability orchestration: per-input oracle routing outperforms the best fixed candidate by 5.4 points, and learned routers outperform it by 0.3 points across 16 benchmarks, by 2.1 points on video generation, and by 1.1 points on robot manipulation while using 24% less policy time. More importantly, routing raises efficiency by 8.82x on average compared with the reference model.
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来源: Hugging Face Daily Papers · huggingface.co