Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
arXiv:2510.13554v2 Announce Type: replace-cross Abstract: The reasoning pattern of Large language models (LLMs) remains opaque, and reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps. This work positions attention as a privileged substrate that renders the internal logic of LLMs legible, not merely as a byproduct of computation, but as a mechanistic blueprint of reasoning itself. We first distinguish attention heads between locally and globally focused information processing and reveal that locall
The increasing complexity and opacity of large language models necessitate advanced interpretability techniques to understand their internal workings, especially as they become more integrated into critical applications.
Understanding how LLMs reason, beyond mere input-output observation, is crucial for improving their reliability, trustworthiness, and for designing more efficient and capable AI systems in the future.
This research provides a mechanistic blueprint for deciphering LLM reasoning via attention mechanisms, moving beyond 'black box' interpretations towards explainable AI policy optimization.
- · AI researchers
- · LLM developers
- · AI safety and ethics organizations
- · Reinforcement learning applications
- · Opaque LLM systems
- · Trial-and-error AI optimization methods
Improved debugging and fine-tuning capabilities for advanced AI models are enabled by this granular understanding of internal processes.
The ability to 'see' LLM reasoning could accelerate the development of more robust AI agents, leading to faster automation of complex tasks.
Deeper insights into AI 'thought' processes could inform new cognitive architectures, blurring the lines between artificial and natural intelligence research.
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Read at arXiv cs.LG