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This paper proposes compressing instruction prompts into a single activation vector via learned weighted sums of intermediate layer activations, achieving under 2% accuracy drop and revealing insights into LLM activation space structure.
UniSteer introduces a text-guided activation flow matching method to learn a universal conditional velocity field in activation space, enabling versatile LLM behavior control and classification tasks without task-specific intervention modules.