concept
active
concept:neural-behavior-geometryneural behavior geometry
The manifold structure of model outputs, modelled by M_y.
Neighborhood — ranked by edge-count
Concepts (2)
concept
- Neural Representation Geometryrelated_toThe broader conceptual framework that neural activations exhibit non-Euclidean geometric structure causally linked to behavior.
- behavior manifold M_yassociated_withManifold fitted to output probability distributions (behavior).
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Conceptual scheme introduced in this paper: neural networks develop internal geometric representations that mirror real-world geometry, providing the right level of description for interpretability and control.
- Neural representation geometry causally shapes behavior; interventions respecting that geometry will yield natural trajectories.hypothesis0.813Central hypothesis tested via manifold steering experiments across language models and video world models.
- Central empirical claim of the paper, demonstrated across tasks and modalities
- Cognition in nervous systems, used as a modelling target
- What is the right geometry for enabling principled steering of neural network behavior?question0.776The reframed steering problem the paper introduces
- Michael Johnson's prior work on how neural networks (and brains) can be 'annealed' to find optimal states.