Improving Environment-Agnostic Coding Agents: The Role of Scaffolding· 提升环境无关编码代理:脚手架的作用
Stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We investigate why this advantage persists and whether an environment-agnostic agent can be improved without environment-specific engineering. We evaluate a Codex-based agent in two nearly identical prompt variants on ARC-AGI-3, AGI Maze, and Terminal-Bench. Each variant is deployed with unchanged code, prompts, and general-purpose tools across environments, receiving environment-specific information as input. Changing only an instruction to encourage reporting difficulties and returning control for automatic continuation improves mean scores on both ARC-AGI-3 and AGI Maze, while Terminal-Bench shows small, mixed changes. Yet the general agent rema
通过指令优化提升环境无关编码代理性能
- 核心方法
- 基于Codex的代理在不同环境中的表现对比,通过修改指令鼓励报告困难并自动继续执行任务
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 探究环境无关编码代理如何克服环境特定信息的缺乏,以提高性能
- 关键实验
- 在ARC-AGI-3、AGI Maze和Terminal-Bench三个环境中进行了实验,展示了平均得分的改善情况
- 主要贡献
- 证实了通过简单指令调整可以提升环境无关编码代理的性能
- 意义与局限
- 该研究对于开发更加灵活、适应性更强的AI代理具有重要指导意义,但也指出了在特定任务上可能存在的局限性