New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents

Abstract:Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet their effects on the recipient remain underexplored. Through controlled experiments with multi-session tasks, we uncover behavior transfer: recipients tend to adopt reasoning and action patterns from the models that generated their histories. This effect is most evident with raw-history transfer but is substantially reduced by summarization. Based on these findings, we propose Direction- and Authorship-aware Summarization of History (DASH) that preserves stronger-model behavior while minimizing weak-to-strong transfer via summarization. Experiments with repeated swaps show that DASH outperforms raw-history transfer and naive summarization baselines. These results suggest that effective multi-model orchestration requires considering not only which model to use, but also which behavioral patterns to carry forward.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.19712 [cs.LG]

(or arXiv:2610.19712v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.19712 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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