Wei Wei

Large language model (LLM) systems frequently switch between models due to evolving capabilities, cost, and deployment constraints. However, prompts are highly model-sensitive: prompts engineered for one model often degrade substantially when reused on another. We term this phenomenon \emph{Model Drifting}, and show through extensive empirical analysis that it is common and severe across diverse LLM configurations. To address this, we propose \emph{PromptBridge}, a training-free framework for cross-model prompt transfer that preserves prompt effectiveness under model switches. It uses a set of alignment tasks to calibrate source and target models via \emph{Model-Adaptive Reflective Prompt Evolution (MAP-RPE)}, and learns a prompt mapping that translates a source-model prompt into an optimized target-model prompt without per-task or per-model re-optimization. Experiments across single- and multi-agent benchmarks demonstrate that \emph{PromptBridge} consistently improves downstream performance while reducing migration effort. For instance, when transferring prompts to \texttt{o3}, \emph{PromptBridge} improves accuracy by 27.39% on \textsc{SWE-Bench} and 39.44% on \textsc{Terminal-Bench} over direct transfer, establishing cross-model prompt transfer as a practical requirement for sustainable LLM system development.

Large-language-model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long-horizon navigation, large-scale information extraction, and reasoning under constraints. We present WebDAR...