动态调整大模型选择，提升智能工作流的精度与效率。
VineLM: Trie-Based Fine-Grained Control for Agentic Workflows
- 按请求实时选择每一步的模型，而非预先固定
- 在相同预算下准确率最高提升18%，节省98%-99.8%预分析成本
- 适合需要高精度、低延迟的复杂推理任务
智能工作流将可配置的大语言模型阶段与工具阶段交替执行，常包含重试或优化循环。现有工作流管理器在离线阶段对完整配置进行评估，为每个请求分配静态的工作流计划，即每个可配置的LLM阶段绑定单一模型，且在循环中重复使用该模型，运行时不再重新选择。我们提出VineLM，一种支持细粒度控制的工作流管理器：在执行过程中根据请求级目标（如在成本或延迟预算内最大化准确率）动态选择每一步的模型。VineLM将可行执行路径表示为带标注的模型选择前缀树，利用检查点和级联剖析技术，在不穷举所有请求路径的情况下估算路径的准确率、成本和延迟。运行时，每完成一个阶段后，系统会基于实际执行前缀和剩余延迟预算重新根植前缀树并重新规划剩余子树。在NL2SQL和数学推理工作流上，VineLM相较粗粒度基线显著改善了成本-延迟-准确率权衡，相同每请求预算下准确率最高提升18%，其稀疏剖析使离线剖析成本相比全量剖析降低98%-99.8%。
原文摘要 · Abstract (English)
Agentic workflows interleave configurable LLM stages with tool stages and often include retries or refinement loops. Existing workflow managers profile full workflow configurations offline and assign each request a static workflow-level plan that binds each configurable LLM stage to a single model, reuses that model across repeated loop iterations, and does not revisit those choices at runtime. We present VineLM, a workflow manager that enables fine-grained control by choosing the model for each stage invocation as execution unfolds under request-level objectives such as maximizing accuracy under cost or latency budgets. VineLM represents feasible executions as an annotated trie of model-choice prefixes and uses checkpointing and cascade profiling to estimate path accuracy, cost, and latency without exhaustively profiling every request on every path. At runtime, VineLM re-roots the trie after each stage invocation and replans over the remaining subtrie using the realized execution prefix and remaining latency budget. On NL2SQL and math reasoning workflows, VineLM improves the cost-latency-accuracy frontier over coarse workflow-level baselines, achieving up to 18% higher accuracy at the same per-request budget with its sparse profiling reducing offline profiling cost by 98-99.8% when compared to exhaustive profiling.
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