arXivPaperNeeds Review
TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
TRACE-Router selects one model per agent task, keeps every call on that backend, and learns from the final outcome. Its benchmarks suggest task-level routing can improve accuracy and latency together.
arXiv · Jul 24, 2026
Source Summary
TRACE-Router chooses a model when a task arrives, pins every subsequent call to that backend, then updates a contextual-bandit policy from the terminal reward. The reward accounts for accuracy and latency, without explicit task-complexity estimates.
Practical Implication
For long-running coding agents, route at the workflow boundary rather than treating each model call independently. The reported gains include **7–8 accuracy points on tau2-Bench** and **7.1 accuracy points with 36% lower latency on Terminal-Bench**.
Agent-Ready Context
TRACE-Router chooses a model when a task arrives, pins every subsequent call to that backend, then updates a contextual-bandit policy from the terminal reward. The reward accounts for accuracy and latency, without explicit task-complexity estimates. For long-running coding agents, route at the workflow boundary rather than treating each model call independently. The reported gains include **7–8 accuracy points on tau2-Bench** and **7.1 accuracy points with 36% lower latency on Terminal-Bench**. The evidence covers **three agentic benchmarks** and reports Pareto improvements, but the material does not describe deployment overhead, model-switching constraints, or behavior on real coding workloads.
Context Map
infracoding#gateways#agent-reliability#model-selectionUncertainty
The evidence covers **three agentic benchmarks** and reports Pareto improvements, but the material does not describe deployment overhead, model-switching constraints, or behavior on real coding workloads.