Hawkeye introduces a collaborative model approach that significantly improves reasoning efficiency in large language models by reducing unnecessary intermediate reasoning steps.

• Addresses the semantic redundancy problem in Chain-of-Thought (CoT) reasoning
• Reduces computational costs and latency through more efficient token generation
• Maintains or improves reasoning accuracy while requiring fewer resources
• Creates potential for faster, more responsive AI tutoring systems in educational settings

For education, this advancement means more cost-effective AI tutoring tools that can provide quicker responses to student questions without sacrificing quality—enabling broader access to AI-powered educational support.

Hawkeye: Efficient Reasoning with Model Collaboration