The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
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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.
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The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
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Hugging Face upvotes 15
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.