Can We Break Llms Out Of Self Loops Fine Grained R

# Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

Derived from arXiv:2607.18100 - Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

## Core Concept
Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable. Existing methods for shaping how a model reasons are prompt based approaches and operate at the input level, offering no fine-grained control over the reasoning process itself. Related work analyzes and discovers latent transition dynamics in the reasoning traces from Large Language Models. Building on this, we statistically characterize these...

## Key Insights
- Derived from arXiv:2607.18100
- Published: 2026-07-20
- Utility Score: 1.00
- Authors: Sheldon Yu, Tong Yu, Xunyi Jiang et al.

## Activation
can-we-break-llms-out-of-self-loops-fine-grained-r, 2607.18100

## References
- arXiv: https://arxiv.org/abs/2607.18100