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[2602.15293] The Information Geometry of Softmax: Probing and Steering
Summary
This paper explores the information geometry of softmax distributions, focusing on how AI systems encode semantic structures and the development of a method called 'dual steering' for concept manipulation.
Why It Matters
Understanding the geometric representation of AI models is crucial for enhancing their interpretability and controllability. This research provides insights into how semantic structures can be manipulated effectively, which is vital for developing more robust AI systems.
Key Takeaways
- The paper discusses the role of information geometry in AI representation spaces.
- It introduces 'dual steering' as a method for robustly steering representations towards specific concepts.
- Empirical findings suggest dual steering improves controllability and stability in concept manipulation.
Computer Science > Machine Learning arXiv:2602.15293 (cs) [Submitted on 17 Feb 2026] Title:The Information Geometry of Softmax: Probing and Steering Authors:Kiho Park, Todd Nief, Yo Joong Choe, Victor Veitch View a PDF of the paper titled The Information Geometry of Softmax: Probing and Steering, by Kiho Park and 3 other authors View PDF HTML (experimental) Abstract:This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. The motivating observation of this paper is that the natural geometry of these representation spaces should reflect the way models use representations to produce behavior. We focus on the important special case of representations that define softmax distributions. In this case, we argue that the natural geometry is information geometry. Our focus is on the role of information geometry on semantic encoding and the linear representation hypothesis. As an illustrative application, we develop "dual steering", a method for robustly steering representations to exhibit a particular concept using linear probes. We prove that dual steering optimally modifies the target concept while minimizing changes to off-target concepts. Empirically, we find that dual steering enhances the controllability and stability of concept manipulation. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML) Cite as: arXiv:2...
