arXiv:2510.08734v3 Announce Type: replace Abstract: A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly modifying their internal states, either through vector additions to their activations or through updates to their weight matrices. These techniques, while powerful, are often guided by empirical heuristics, such as deriving ``steering vectors'' from the average activations of contrastive prompts. Building on the foundational work of Dherin et al. (2025), who discovered that a prompt's influence mat
Building on recent research demonstrating control over LLM behavior via internal state modification, this work directly addresses the empirical heuristics previously guiding prompt-based steering.
This research provides a more principled and potentially generalized method for controlling Large Language Models, moving beyond specific prompts to directly modifying their underlying weights.
The ability to transmute prompts into weight modifications offers a more fundamental approach to AI control and customization, potentially simplifying and generalizing model steering techniques.
- · AI researchers
- · LLM developers
- · Companies seeking fine-grained AI control
- · Developers reliant solely on prompt engineering
- · Inferior prompt optimization tools
More robust and predictable control over large language model behavior becomes achievable.
This could lead to highly customized and specialized AI agents with embedded, context-specific behaviors.
The development of 'personality' or 'task' weights could enable more sophisticated and reliable AI agents for complex applications.
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Read at arXiv cs.LG