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About this paper
ZhiYuan Feng, Zhuoshi Pan, Zhaolu Kang, Zhongxia Chen, Shuo Yang, Sen Ye, Heng Wang, Han Li
EnactOpt adapts embodied agents by jointly revising reusable skills and context-construction code while keeping models and runtime fixed. It diagnoses how feedback appears in the next complete model input and relates to the subsequent action, then selects harnesses using validation success. On ALFWorld’s 274 test instances, success was 92.70% versus 66.06% for H₀; evidence is limited to benchmark settings.
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