arXiv:2605.14084v2 Announce Type: replace-cross Abstract: Code agents must both reason over long-horizon repository state and obey strict tool-use protocols. In paired Instruct/Thinking checkpoints, these capabilities are complementary but misaligned. The Instruct model is concise and tool-disciplined, whereas the Thinking model offers stronger planning and recovery behavior but often over-deliberates and degrades agent performance. We present CRANE (Constrained Reasoning Injection for Code Agents via Nullspace Editing), a training-free parameter-editing method that treats the Thinking-Instruc
The rapid advancement in AI models necessitates methods to refine agentic behavior, and this research addresses a critical challenge in reconciling conflicting model strengths.
Improving the efficiency and reliability of AI agents directly impacts their deployment in complex tasks, potentially collapsing white-collar workflows and enhancing productivity.
This method offers a training-free approach to enhance AI agent performance by combining the strengths of different model checkpoints, making agents more practical for real-world applications.
- · AI developers
- · Businesses adopting AI agents
- · Software sectors
- · Inefficient AI agent development methods
- · Companies slow to adopt advanced AI
More robust and efficient AI agents become available for various applications.
Increased automation of complex tasks across industries, leading to productivity gains and potential job displacement.
The acceleration of AI agent capabilities could reduce the timeline for artificial general intelligence development.
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