# TRACER：面向LLM分类的基于轨迹的自适应成本效益路由

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-16 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo2m34or03a2slbalstw1yi8
- 原文链接：https://arxiv.org/abs/2604.14531

## AI 摘要

开源系统TRACER利用LLM生产日志训练轻量级替代模型，通过设置一致性阈值α作为部署门控，仅在替代模型与教师模型（如Sonnet 4.6）一致率达标时激活，实现近零边际成本的推理。系统生成可解释性产物明确能力边界。在77类意图识别基准测试中，替代模型覆盖率达83%-100%；在150类任务中完全替代教师模型；而在自然语言推理任务中，系统正确识别嵌入表示不足并拒绝部署。

## 正文

Every call to an LLM classification endpoint produces a labeled input-output pair already retained in production logs. These pairs constitute a free, growing training set: a lightweight surrogate trained on them can absorb a significant portion of future traffic at near-zero marginal inference cost. The open questions are when the surrogate is reliable enough to deploy, what it handles versus defers, and how that boundary evolves as data accumulates. We introduce TRACER (Trace-based Adaptive Cost-Efficient Routing), an open-source system that trains ML surrogates on an LLM's own production traces and governs deployment through a parity gate: the surrogate is activated only when its agreement with the LLM exceeds a user-specified threshold α. To make the routing boundary transparent, TRACER generates interpretability artifacts describing which input regions the surrogate handles, where it plateaus, and why it defers. On a 77-class intent benchmark with a Sonnet 4.6 teacher, TRACER achieves 83-100% surrogate coverage depending on the quality target α; on a 150-class benchmark, the surrogate fully replaces the teacher. On a natural language inference task, the parity gate correctly refuses deployment because the embedding representation cannot support reliable separation. The system is available as open-source software.