TwinRouterBench: A Step-Level Benchmark for LLM Routing in Agentic Tasks
TwinRouterBench has rolled out an innovative benchmark aimed at evaluating how LLMs manage routing for complex, long-term tasks like coding and advanced research systems. Unlike existing benchmarks that only consider single prompts and ignore the process agents go through, this one offers a more comprehensive assessment. It includes two tracks, with a static track featuring 970 router-visible prefixes from 520 instances across various datasets like SWE-bench, BFCL, mtRAG, QMSum, and PinchBench, which have been confirmed for execution targets using a downgrade-and-cascade technique. The scoring is consistent and predictable. You can find this research on arXiv.
Key facts
- TwinRouterBench is a step-level routing benchmark for LLM routing.
- It targets long-horizon applications like coding agents and deep research systems.
- Existing router benchmarks only evaluate on one-shot prompts.
- The static track includes 970 router-visible prefixes from 520 instances.
- Instances come from SWE-bench, BFCL, mtRAG, QMSum, and PinchBench.
- Each prefix has an execution-verified target tier.
- Scoring is deterministic arithmetic over tier labels and trajectory membership.
- The benchmark uses a downgrade-and-cascade protocol.
Entities
Institutions
- arXiv