SkillSeg: Self-Evolving Agent Skills via Feedback Attribution

Language agents can improve without updating model parameters by revising an external library of reusable skills. However, when multiple skills are invoked within an episode, its terminal outcome does not specify whether the behavior associated with each skill provides evidence for retaining or revising that skill. We propose SkillSeg, a framework for skill-level feedback attribution over trajectory segments, each consisting of consecutive actions executed under the same active skill. For each segment, SkillSeg compares the likelihood of the observed actions under matched prompts with and without the active skill, then combines this contrast with the episode outcome relative to a historical baseline. The resulting sign routes the segment to positive or negative reflection for the corresponding skill, and a likelihood-ratio gate screens the proposed library update. We evaluate SkillSeg with frozen Qwen3.5-9B agents on ALFWorld, WebShop, and AppWorld. SkillSeg achieves 85.07 ± 1.49% success on ALFWorld, compared with 79.10 ± 1.49% for a matched segment-level baseline that directly inherits episode labels. It also achieves 36.51 ± 2.48% success on AppWorld and a score of 55.48 ± 3.64 with 20.00 ± 1.00% success on WebShop, attaining the highest means among the completed same-backbone methods across these benchmarks. These results support skill-level feedback attribution as a useful mechanism for guiding reusable skill evolution with frozen language models.