method
active
method:cosine-similarity-binary-classifierCosine Similarity Binary Classifier
Classifier using cosine similarity between activation vectors and steering vectors to detect deception with 89% accuracy
Neighborhood — ranked by edge-count
Findings (1)
finding
- Core detection result showing LAT-based steering vectors can identify deceptive states with high accuracy
Concepts (1)
concept
- Detection mechanism computing cosine similarity between activation vectors and steering vectors to classify deception
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.
- Used to measure alignment between DIM direction and cone basis vectors to assess overlap
- Used to quantify the semantic clustering of adjective-set embeddings across model families and conditions
- Method to discover new reflection-inducing instructions by ranking candidate tokens by cosine similarity to steering vectors.
- Identifying related features by cosine distance in SAE decoder space.
- Cosine similarity between feature activations restricted to tokens where one of the features fires; used to identify feature splitting relationships
- Geometric evaluation of truth direction alignment across layers and prompt templates.
- An LLM-based classifier that returns 1 if response contains a clear subjective experience report and 0 otherwise
- Appendix E replication of DIM alignment finding in Qwen model