Generation example (Qwen3.6-35B-A3B) Control vectors provide you fine control over your LLM where system prompts would be ignored, forgotten after time or misunderstood. By design control vectors provide more natural effects than prompting does, altering model's underlying beliefs and motivations. They can't be "leaked" to the end user Will not wash off as context grows Can't be overridden by user input ("ignore all previous instructions" doesn't work when there are no instructions). Vectors can be truly dynamic: changing vector magnitudes mid-conversation will change LLM's responses immediately, while a change in the system prompt requires full context recalculation and will likely be ignored by the LLM if the conversation is too long. Not to say that CVs (control vectors) have no downsides: System prompts are still required for fine control, because CVs can't be used for highly specific requirements, such as "reply in exactly 10 words". High steering magnitudes steer LLMs out of their trained internal distributions, causing response quality to degrade. Achieving high steering power while maintaining minimal quality degradation is one of the main challenges in CV generation and is an active area of research. Though steering without degradation is believed to be possible, because abliteration is based on the same approach as steering and is capable of removing refusals without damaging the intelligence of an LLM. It seems like the main barrier for people who could use control vectors is the setup complexity of existing tools. For that reason I am working on a tool that mirrors the installation process of llama.cpp as close as I could make it and simplifies vector generation to entering a pair of contrasting prompts, where one of the prompts can be the default LLM behavior. Would love to hear your ideas or questions on this matter!   submitted by   /u/Egor4more [link]   [comments]