SkillProx 用近端梯度下降让智能体技能自进化，平均准确率比最强基线高 3 个百分点，值得搞 Agent 技能的看看。
SkillProx 提出一种受近端梯度启发的正向-反向框架，用闭环诊断与效用感知的近端细化来更新智能体的技能库。前向阶段在同一任务批次上执行诊断驱动的编辑并回滚回归，反向阶段把技能拆成知识单元，用留一效用审计估算贡献。在分布内和分布外基准上、多种骨干 LLM 下，SkillProx 比最强的基于梯度的基线平均准确率提高 3.0 个百分点。消融实验显示闭环诊断与近端细化具有互补效果。
SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent
LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills. These skills are lightweight, reusable textual artifacts that are loaded into the agent's context without weight updates. Recent methods refine skills through iterative task execution, failure diagnosis, and trajectory-guided text-space updates. However, existing frameworks lack explicit diagnosis--outcome feedback and treat deletion as a generic edit operation rather than a dedicated mechanism for consolidating accumulated knowledge. We introduce SkillProx, a proximal-gradient-inspired forward--backward framework that couples closed-loop diagnostic evolution with utility-aware proximal refinement. Motivated by a composite objective balancing task loss and skill complexity, the forward stage re-executes diagnosis-driven edits on the same task batch, rolls back regressions, and feeds measured outcomes into subsequent diagnoses. The backward stage decomposes the resulting skill into auditable knowledge units, estimates their contributions using a frozen leave-one-out utility audit, and applies validation-gated consolidation, demotion, or removal. Experiments on in-distribution and out-of-distribution benchmarks across multiple backbone LLMs show that SkillProx improves average accuracy by 3.0 percentage points over the strongest gradient-based baseline. Component ablations demonstrate the complementary effects of closed-loop diagnosis and proximal refinement.