The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
Read the original on arXiv AI →The paper investigates the cost–quality trade-offs involved when coding agents switch between low‑cost, low‑capability (LC) and high‑cost, high‑capability (HC) language models during long‑running tasks. By experimenting with different handoff directions, timings, and interfaces—full‑trajectory transfer, compaction, and trajectory removal—the authors find that full‑trajectory escalation recovers less than half of the LC‑to‑HC quality gap while adding significant cost, a penalty they call the handoff tax. Conversely, downshifting from HC to LC yields a more favorable cost‑quality balance, and the optimal interface depends on the direction of the handoff. whyItMatters":"The study quantifies how model handoffs impact both performance and expense, offering guidance for designing more efficient coding agents that balance cost and quality."
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