Route Anything, 범용 AI 시스템의 모델·정책 라우팅 프레임워크
Route Anything: A Study of Model and Policy Routing for General-Purpose AI Systems
- 매체
- Hugging Face
- 게시
- 중요도
- 3/5
- 화제 지수
- 22
한국어 요약
연구팀이 범용 AI 시스템에서 모델과 정책 라우팅을 연구하는 통합 프레임워크 Route Anything을 제시했다. 언어 이해, 이미지·영상·음성 생성, 코딩, 에이전트, 로봇 조작 등 25개 벤치마크에서 오라클 라우팅은 최고 고정 후보 대비 5.4점, 학습된 라우터는 0.3점(영상 생성 2.1점, 로봇 조작 1.1점) 향상을 보였으며 정책 시간은 24% 감소했다.
원문 발췌 · English
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.