Tests whether a hypothesized high-level causal model (a symbolic algorithm with named variables) is a faithful simplification of a neural network's actual computation, by learning — via gradient descent — an orthogonal rotation of a representation so that one rotated-basis subspace aligns with a chosen causal variable, then checking via interchange interventions whether swapping that subspace's value between two inputs reproduces the high-level model's predicted counterfactual output. USES an assumed linear-subspace structure as an alignment-testing tool; does not itself claim to discover that any real-world concept is linearly encoded.
Used in (5 observations)
structure: Linear Subspace · models: Alpaca-7B · paper: Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
structure: Linear Subspace · models: 3-layer ReLU feedforward network (hierarchical equality task), BERT-base-uncased · paper: Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
structure: Linear Subspace, Linear Direction · models: Qwen3-4B, Qwen3-8B, OLMo 3 7B · paper: Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics
structure: Generalized Helix, Circle · models: Llama-3.1-8B · paper: Arithmetic in the Wild: Llama Uses Base-10 Addition to Reason About Cyclic Concepts
structure: Linear Direction · models: GPT-2 Small, Pythia-1.4B, Pythia-2.8B · paper: Linear Representations of Sentiment in Large Language Models