The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents

TL;DR - This paper identifies a “handoff tax” when one coding-agent model inherits another model’s trajectory. Escalating from a weaker model to a stronger one recovers less than half the quality gap at substantial added cost, while downshifting offers a better cost-quality trade-off.

• Experiments span Claude and GPT model pairs, varying handoff direction, timing, and inherited context.
• Transfer interfaces include full trajectories, compacted trajectories, and no trajectory beyond the preserved repository state.
• Escalation improves when less of the weaker model’s trajectory is retained.
• Downshifting behaves oppositely: removing the stronger model’s trajectory reduces quality.