Gavel 方法从冻结 LLM 中提取技能路由信号
The Router Within: Eliciting Native Skill Routing from a Frozen LLM
朋友，这是 DeepSeek 的新方法，叫 Gavel，能从冻结的 LLM 里直接读出该用哪个技能，不用加载那么多东西，在他们的新基准测试里比其他方法好很多。
这篇论文提出了一种名为 Gavel 的方法，它通过两个线性映射从冻结的 LLM 前向传播中读取路由信号，无需将技能文本加载到上下文中。在 Qwen3-32B 模型上，该方法在 SkillTraj 基准测试中比添加 1.2B 到 16B 外部参数的渐进披露和检索重排序管道表现更好，在书面任务上提升最多 13.4 分，在技能使用中途触发时提升最多 21.9 分。
The Router Within: Eliciting Native Skill Routing from a Frozen LLM
Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by preloading every skill's metadata into the context, which disperses the agent's attention and caps the library size. Retrieval pipelines move the selection out of the context, but also out of the agent's capability. We show that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read it out with no skill text in the context. Gavel (Glance And Verdict from a frozen LLM) reads it in two steps. A glance projects the task's and each skill's mid-layer states through the two maps, the only parameters trained, and scores the full library against compact per-skill banks that one forward pass builds at installation. A verdict then resumes the shortlisted skills' forward passes and reads the model's own likelihood and yes/no judgment, fused with the glance as a product of experts. Trained once, Gavel transfers zero-shot to three public benchmarks and SkillTraj, our new benchmark of 372 simulated agent trajectories. On Qwen3-32B it outperforms progressive disclosure and retrieve-and-rerank pipelines that add 1.2B to 16B external parameters, by up to 13.4 points on written tasks and up to 21.9 when the need for a skill arises mid-rollout. Routing accuracy improves as the backbone does, and in a bash-agent harness the same 32B triggers the correct skill on Skill-Use more often than far larger frontier models running in Codex.
