外观
论文arXiv 2610.1497718 天前
EnactOpt：面向具身智能体的诊断引导式执行框架演化
原文链接：EnactOpt: Diagnosis-Guided Harness Evolution for Embodied Agents（arXiv 2610.14977 · 发布日期：2026-10-10）
EnactOpt: Diagnosis-Guided Harness Evolution for Embodied Agents
针对执行框架影响具身智能体决策的问题，EnactOpt用有限执行反馈联合演化文本技能与上下文构建代码，并在VLABench、ALFWorld和EAI上验证。
核心要点
- 失败可能源于模型外执行框架如何呈现证据并引导使用，而不只是模型能力不足。
- EnactOpt在不更新模型和运行时的情况下，联合演化可复用文本技能与上下文构建代码。
- 其将动作反馈关联到模型实际输入和后续动作，用结构化回合摘要驱动提示级编辑。
- 在VLABench、ALFWorld和EAI上，任务成功率较初始框架提升约10-27个百分点，并优于所评基线。
验证范围与局限：验证范围为VLABench、ALFWorld和EAI，且包括未用于框架演化或候选选择的任务条件。
英文摘要 (Abstract)
Embodied agents built on foundation models rely on an execution harness to organize observations, guide actions, and incorporate execution feedback into subsequent decisions. Failures can arise not only from limited model capabilities, but also from how the harness presents evidence and guides its use. These model-external mechanisms provide an adaptation target without updating model parameters, but task-specific interaction experience can be costly to collect. Task-level outcomes alone do not reveal whether relevant evidence was missing from the model input or was available but inadequately used. Moreover, these two aspects are coupled: effective action guidance may require evidence that the current context omits, while exposing that evidence alone does not ensure an appropriate response. We introduce EnactOpt, which uses limited execution feedback to jointly evolve reusable textual skills and context-construction code while keeping the underlying models and runtime fixed. It diagnoses adjacent decisions by relating action feedback to the actual model input and subsequent action. Structured episode summaries guide joint revisions through prompt-level edit operators, and candidate harnesses undergo compatibility checks and validation-based retention. Across VLABench, ALFWorld, and Embodied Agent Interface (EAI), EnactOpt improves task success by approximately 10-27 percentage points over the initial harnesses and outperforms the evaluated GEPA, TextGrad, and Trace2Skill baselines. These gains extend to task conditions not used for harness evolution or candidate selection. The results support a complementary route to embodied adaptation: turning limited execution experience into reusable behavioral improvements by jointly refining how evidence is presented and acted upon.
作者：ZhiYuan Feng, Zhuoshi Pan, Zhaolu Kang, Zhongxia Chen, Shuo Yang, Sen Ye, Heng Wang, Han Li
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