Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior Author's Explanation: x.com/GoodfireAI/sta… Overview: Manifold steering investigates the causal link between internal representations and behavioral outcomes by intervening along activation manifolds rather than assuming Euclidean geometry. This approach demonstrates that steering along the activation manifold produces behavioral trajectories that align with output geometry, whereas linear interventions fail to recover natural dynamics. By verifying this bidirectional relationship across LLMs and video world models, this work identifies geometric structure as the primary mechanism for principled control over neural model behavior. Paper: arxiv.org/abs/2605.05115