Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing

Key points

• We compare several router architectures using handoff curves, which record task success and cost as the handoff step changes.1
• Our dataset contains 34,204 complete executions of three Qwen models across BFCL, MultiWOZ, and $τ$-retail, providing offline supervision for routing decisions.1
• In our primary comparison, with Qwen3.5 35B-A3B as the starting model, allowing a later handoff does not establish an improvement in utility over initial selection on BFCL or MultiWOZ, and increases cost on BFCL across a range of cost weights.1
• Changing the supervision targets improves router performance on both our dataset and TwinRouterBench.1

Sources (1)

• [1]Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 11:45 PM

  We study how initial model selection, later handoff, and training targets affect success and cost in LLM tasks that span multiple turns.

  We compare several router architectures using handoff curves, which record task success and cost as the handoff step changes.

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