Finds a direction (or an orthonormal basis of several directions) by gradient-based optimization of a custom loss with explicit ablation/addition/retain terms, rather than a closed-form statistic (diff-in-means) or a generic classifier fit (linear-probing).
Used in (3 observations)
structure: Linear Separability · models: Ovis2.5-2B, InternVL3.5-2B, VST-3B · paper: Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
structure: Cone · models: Gemma-2-2B-it, Gemma-2-9B-it, Qwen2.5-1.5B-Instruct, Qwen2.5-14B-Instruct, Llama-3-8B-Instruct · paper: The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
structure: Cone · models: Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct, Gemma-2-2B-it, Gemma-2-9B-it · paper: From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs