TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
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Overall
81
Content
85
Popularity
72
Observed public metrics from 1 member.
Merged summary
TL;DR - TRACE-Router uses a contextual bandit to assign an entire agentic task to one LLM and learns from delayed task-level rewards. This improves accuracy-latency trade-offs by aligning routing decisions with long-horizon workflow outcomes.
- Pins all LLM calls within a task to the model selected at admission.
- Optimizes routing from terminal rewards that jointly reflect accuracy and latency.
- Avoids explicit task-complexity estimation while adapting to changing workloads.
- Beats latency-matched model interpolation by 7–8 accuracy points on τ²-Bench; on Terminal-Bench, it gains 7.1 points over the strongest single model with 36% lower latency.
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TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
Public signals
Hugging Face upvotes 0 · Semantic Scholar citations 1 · Semantic Scholar influential citations 0
TL;DR - TRACE-Router uses a contextual bandit to assign an entire agentic task to one LLM and learns from delayed task-level rewards. This improves accuracy-latency trade-offs by aligning routing decisions with long-horizon workflow outcomes.
- Pins all LLM calls within a task to the model selected at admission.
- Optimizes routing from terminal rewards that jointly reflect accuracy and latency.
- Avoids explicit task-complexity estimation while adapting to changing workloads.
- Beats latency-matched model interpolation by 7–8 accuracy points on τ²-Bench; on Terminal-Bench, it gains 7.1 points over the strongest single model with 36% lower latency.