Attention-Aligned Reasoning for Large Language Models
ATAR is a novel reasoning method that steers Large Language Model attention by interleaving planning and action steps to balance global goals with local objectives. It introduces dynamic attention steering based on model confidence, allowing it to amplify focus during uncertain steps. ATAR consistently outperforms existing methods across six benchmarks by up to 15.39% absolute improvement, showing a path toward more reliable LLM reasoning.