developer tools
One Instruction Changes How the Agent Returns
An environment-agnostic coding agent improves when it reports difficulty and yields control for automatic continuation.
Summary
An environment-agnostic coding agent improves when it reports difficulty and yields control for automatic continuation.
Access to tools and instructions does not guarantee that an agent will use them effectively. Across ARC-AGI-3, AGI Maze and Terminal-Bench, one prompt change—reporting difficulty and returning control for continuation—improved mean scores on the first two environments while producing small, mixed changes on the third. Moving reset decisions into a simple scripted controller recovered part of a specialized agent's advantage, while extra tutorial experience could hurt. The experiments cannot separate better procedure use from additional or differently allocated inference, and gains cost more tokens. They nevertheless show that control scaffolding remains consequential for the tested models.
Why it matters
An environment-agnostic coding agent improves when it reports difficulty and yields control for automatic continuation.
Limits and context
- Access to tools and instructions does not guarantee that an agent will use them effectively.
- The experiments cannot separate better procedure use from additional or differently allocated inference, and gains cost more tokens.
Key claims
An environment-agnostic coding agent improves when it reports difficulty and yields control for automatic continuation.
Qualification: Access to tools and instructions does not guarantee that an agent will use them effectively.
Evidence: source-2026-10-17-011
Sources
- arXiv preprint 2610.22289, original v1arXiv · primary research
Corrections
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