论文提出 SAT：让强模型先诊断再教学弱模型 Agent
Teaching the Harness: Student-Aware Skill Transfer for LLM Agents
这篇论文有点意思：Claude 教 DeepSeek 时不是直接写攻略，而是先跑诊断探针找出弱模型卡在哪，再针对性写技能，成功率从 75% 拉到 86%。给做 Agent 框架和多人协作系统的开发者提供了可复用的技能准入机制思路。
arXiv 论文提出 Student-Aware Teaching（SAT）框架，让教师 LLM 在给固定学生 Agent 写技能前，先建模学生的局限。
教师用诊断探针验证假设，写出的技能只有在匹配开发任务上提升成功率才准入库。以 Claude-Sonnet-5 教 DeepSeek-V4-Flash，SAT 在四次主要运行中达到 86.
46% 任务成功率，高于无画像教师（81.04%）和未受教的基线（75.
00%）。方法的核心是把 Theory of Mind 用于工程：教师区分学生的经验画像与假设，并在技能写入库前逐一测试。
消融显示去掉分析型学习者画像会同时降低预测和成功率；48 个候选技能在误导性画像下无一通过准入。
学生模型互换技能库时各自用自己生成的库表现更好，教师离开后库仍保留最多 11.1 点平均增益。
前提是学生能遵循指令且部署渠道可传递这些技能。
Teaching the Harness: Student-Aware Skill Transfer for LLM Agents
Effective teaching requires reasoning about the learner as well as the task, a capacity that Theory of Mind describes as inferring another agent's latent state from its behavior. We ask whether a stronger LLM teaches a fixed student better when it applies this principle. The student is an LLM agent acting through a harness that fixes its tools, context, and action budget, so a procedure that helps one model can exhaust the budget of another. We introduce Student-Aware Teaching (SAT). The teacher builds a learner model that pairs an empirical profile with hypotheses about the student's limitations, tests these hypotheses with diagnostic probes, and writes reusable skills for the diagnosed failures. A skill enters the library only after it raises success on matched development tasks. With Claude-Sonnet-5 teaching DeepSeek-V4-Flash, SAT reaches 86.46% task success across four primary runs, leading both a profile-free teacher with the same framework (81.04%) and the untaught student (75.00%) in each run. Removing the analytical learner model lowers both prediction and success, and under misleading learner profiles none of the 48 candidate skills passes admission. When DeepSeek-V4-Flash and Gemini-3.8-Flash exchange libraries, each performs better with its own. Saved libraries keep average gains of up to 11.1 points after the teacher leaves. The benefit depends on a student that can follow instructions and a deployment route that delivers them.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Student-Aware Teaching、Claude、DeepSeek、智能体，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始信息发布自一手权威信源「arXiv: DeepSeek」（Tier-1 一手发布），具备高确定性参考价值。