arXiv:2605.01844v2 Announce Type: replace Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical accounts are largely based on the Linear Representation Hypothesis (LRH). While LRH assumes that concepts can be orthogonalized for lossless control, this idealized mapping fails in real representations and cannot account for the observed unpredictability of steering. By relaxing LRH's orthogonality assumption while preserving linear representations, we show that overlapping concept contributi
This research provides a theoretical advancement in understanding and controlling large language models, addressing current limitations in steering mechanisms.
Improved steering predictability and stability for large language models could unlock more reliable and effective AI applications across various industries.
The theoretical framework for language model steering shifts from an overly simplistic orthogonal assumption to a more realistic model incorporating overlapping concept contributions.
- · AI developers
- · Companies using large language models
- · AI-driven product sectors
- · Developers reliant on unstable steering methods
- · Companies with less sophisticated AI control mechanisms
More precise and reliable control over large language model behavior becomes possible.
This leads to the development of more robust and trustworthy AI applications and agentic systems.
Increased reliability and predictability of AI could accelerate the adoption of autonomous AI agents in critical workflows, collapsing more white-collar tasks.
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