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Paper · arXiv

Improving Environment-Agnostic Coding Agents: The Role of Scaffolding

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 b...

AuthorsSergey Rodionov
Primary categorycs.AI
Published15 October 2026
Last updated at source15 October 2026
cs.AI
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