Anchored Thinking: Mapping the Inner Compass of Reasoning LLMs
In the world of large language models (LLMs), answers often emerge from an intricate internal dialogue. But what if we could locate the few sentences within that stream of thoughts that disproportionately steer the outcome—like anchors stabilizing a drifting ship? That’s exactly what Paul Bogdan, Uzay Macar, Neel Nanda, and Arthur Conmy aim to do in their new work, “Thought Anchors: Which LLM Reasoning Steps Matter?”. This study presents an ambitious trifecta of methods to trace the true influencers of LLM reasoning. ...