arXiv:2606.19831v1 Announce Type: new Abstract: Aligned language models gate behaviors such as refusal and language routing through sparse feed forward neurons, yet no theory predicts when a single neuron intervention controls a behavior coherently rather than collapsing the output. We develop a budget normalized control window framework for single neuron steering. A dose along one write direction reduces to one control coordinate: the alignment between the residual stream and the write, driven along a universal saturation curve in units of a coherence budget set by the residual norm divided b
This research explores a fundamental aspect of controlling large language models, driven by the rapid advancements and widespread deployment of AI and the increasing need for precise behavioral steering.
Achieving fine-grained control over specific behaviors within language models by manipulating individual neurons offers a path to more reliable, predictable, and safer AI systems, crucial for sensitive applications.
The development of a 'control-window framework' provides a theoretical and practical method for targeted, coherent intervention in large language models via single neurons, moving beyond brute-force methods.
- · AI Safety Researchers
- · Large Language Model Developers
- · AI Governance Bodies
- · Uncontrollable AI Systems
- · Adversarial Attackers (in some contexts)
More precise and reliable alignment of AI models with human intent becomes possible through targeted neural intervention.
The ability to 'steer' specific model behaviors at a neural level could lead to new forms of AI explainability and auditability.
Improved control mechanisms may accelerate the deployment of AI in highly sensitive domains, potentially impacting the development of advanced autonomous agents.
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