这篇论文给出了一个很实用的思路：用任务级反馈来动态路由LLM，不用每次调用都做独立决策。在多个基准上同时提升了准确率和速度。
TRACE-ROUTER 提出任务级路由框架，将每个任务在受理时通过上下文赌博机分配一次模型，后续所有LLM调用均固定到所选后端，并使用最终奖励更新策略。在 tau2-Bench 上，该方法比延迟匹配的单个模型插值高出7-8个准确率点。在 Terminal-Bench 上，相比最强单模型基线，TRACE-ROUTER 以36%更低延迟实现7.1个准确率点提升。该框架无需显式任务复杂度估计，即可学习适应工作负载的路由策略。
TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-horizon workflows whose quality is determined only by a delayed, task-level outcome. This mismatch prevents per-call routers from correctly attributing feedback to individual routing decisions. Towards mitigating this, we present TRACE-Router, a task-level routing framework that aligns routing with the unit of supervision. TRACE-Router assigns each task to a model once at admission using a contextual bandit, pins all subsequent LLM calls to the selected backend, and updates its policy using the task's terminal reward, jointly accounting for accuracy and latency. By leveraging delayed task feedback, TRACE-Router learns routing policies that adapt to the workload while avoiding explicit task-complexity estimation. Across three agentic benchmarks, TRACE-Router consistently improves the accuracy-latency trade-off, achieving non-dominated Pareto frontier points. On tau2-Bench, it outperforms latency-matched interpolation between individual models by 7-8 accuracy points, while on Terminal-Bench it achieves 7.1 higher accuracy points than the strongest single model baseline with 36% lower latency.