发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SHIFT通过本地LLM架构师学习策略和价值函数，利用蒙特卡洛树搜索按查询构建智能体框架，在六个基准上以约80%的平均准确率超越17个基线，并降低执行成本。
AI 中文摘要
智能体框架指定了用于解决任务的角色、指令、工具和通信结构，而正确的框架取决于查询。由于每个设计选择的价值只能通过执行来观察，为每个查询定制框架需要在推理时执行备选方案或进行昂贵的人工设计。我们引入了SHIFT，它将执行移出逐查询搜索循环。一个本地LLM架构师从搜索中学习关于框架构建动作的策略，以及一个价值函数，该函数根据测量的执行预测一个平衡准确性与执行成本的效用。对于每个查询，蒙特卡洛树搜索使用这些预测来构建框架。在六个基准的9,193个任务中，从数学到文档和通用助手任务，使用Gemini 3.5 Flash执行器，SHIFT达到了最高平均准确率，约为80%，优于涵盖提示、提示优化和工作流搜索的17个基线，并超过最强基线7.2个百分点。SHIFT的一种更便宜模式也达到了比所有基线更高的平均准确率，同时使用的执行令牌比最强基线少32%。我们进一步表明，联合选择结构、指令和工具比仅选择指令或仅选择工具最多高出9.1个百分点，并且学习到的价值选择从候选池中识别出更准确且执行成本更低的框架。
英文摘要
Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Because the value of each design choice is observable only through execution, tailoring a harness to each query has required either executing alternatives at inference time or costly manual design. We introduce SHIFT, which moves execution out of the per-query search loop. A local LLM architect learns a policy over harness-building actions from search, and a value function that predicts, from measured executions, a utility balancing accuracy against execution cost. For each query, Monte Carlo tree search uses these predictions to construct a harness. Across 9,193 tasks in six benchmarks, from math to document and general-assistant tasks, with a Gemini 3.5 Flash executor, SHIFT attains the highest mean accuracy, about 80%, outperforming 17 baselines that span prompting, prompt optimization, and workflow search, and exceeding the strongest baseline by 7.2 percentage points. A cheaper mode of SHIFT also attains a higher mean accuracy than every baseline while using 32% fewer execution tokens than the strongest baseline. We further show that choosing structure, instructions, and tools jointly beats choosing only instructions or only tools by up to 9.1 percentage points, and that learned value selection identifies more accurate harnesses with lower execution cost from candidate pools.
Comments27 pages, 18 figures
