arXiv:2605.24846v1 Announce Type: new Abstract: Large language models (LLMs) display strong comprehensive abilities, yet the internal mechanisms that support these behaviors remain insufficiently understood. In this work, we show that across a wide range of open-weight Transformers, a subset of neurons remains consistently highly activated during inference across tasks of multiple capability dimensions. By probing along the cross-task activation strength, an extremely sparse subset is isolated, whose removal causes a collapse in model behavior, which we term keystone neurons. Our analysis reve
The accelerating pace of large language model development and deployment necessitates a deeper understanding of their internal mechanisms for safety, efficiency, and reliability.
Identifying 'keystone neurons' provides a critical insight into the functional architecture of LLMs, potentially leading to more efficient models, targeted interventions, and enhanced control over AI behavior.
Our understanding of LLM interpretability and the potential for fine-grained manipulation of their capabilities shifts from broad architectural tweaks to precise neuronal targeting.
- · AI interpretability researchers
- · Developers of custom LLMs
- · AI safety organizations
- · Developers reliant on black-box LLM optimization
- · Models with inefficient architectures
Research into LLM interpretability will accelerate, focusing on identifying and stabilizing these critical neuronal pathways.
New techniques for model compression, architectural design, and targeted fine-tuning will emerge, significantly improving LLM efficiency and controllability.
The ability to 'debug' or 'edit' AI intelligence at a fundamental neural level could lead to novel AI capabilities and ethical dilemmas regarding AI consciousness or manipulation.
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Read at arXiv cs.LG