这篇论文提出了SkillReranker，在智能体任务中通过任务分解引导技能重排序，比传统方法更准更省token，具体在ALFWorld和ScienceWorld上验证了效果。
SkillReranker通过任务和技能两侧的语义分解，生成子任务描述和执行状态描述，并构建有向无环执行图。该框架利用交叉编码器对每个任务区间的候选技能进行综合评分，选择最合适的技能集。在ALFWorld和ScienceWorld两个基准上的实验表明，SkillReranker有效提升了任务完成率，减少了环境交互步骤和token消耗。
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks. However, the growing scale of skill libraries makes accurate skill selection increasingly challenging. In real-world scenarios, ambiguous semantic matching often arises between a specific task requirement and multiple generic yet semantically similar candidate skills. Moreover, existing methods tend to overlook the dynamic influence of task difficulty and skill applicability when selecting the optimal target skill set. To address these issues, we propose SkillReranker, an inference-time reranking framework for adaptive skill selection. Specifically, we first perform semantic decomposition on both the task and skill sides, yielding informative subtask and execution-state descriptions as well as transition-state descriptions that characterize each skill's functionality. These descriptions are then used to construct a directed acyclic execution graph, where intermediate task states are modeled as nodes and candidate skills as edges, thereby establishing a structured task-skill correspondence. On this basis, SkillReranker determines whether each state node satisfies the split condition to identify subtask intervals. For each task interval, we employ a cross-encoder to perform comprehensive scoring over candidate skills and select the most suitable ones to form the final target skill set. Experiments on ALFWorld and ScienceWorld with three backbone LLMs show that SkillReranker effectively improves task performance, reduces environment interaction steps, and lowers token consumption compared with existing skill selection baselines.