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Add a comprehensive benchmark suite for evaluating NNsight skills across different difficulty levels (easy, medium, hard) and techniques (basics, logit-lens, activation-patching, attribution-patching, causal-tracing, model-steering). The benchmark includes: - 17 query files testing specific interpretability techniques - Schema definitions for queries and results - Structural validation for API correctness - Deprecated pattern detection (pre-0.5 NNsight) - Claude Code and API runners - Results analysis and comparison tools Filenames now use descriptive hyphen-separated titles based on the query content rather than generic numeric IDs.
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Summary
This PR adds a comprehensive benchmark suite for evaluating NNsight skills across different difficulty levels and interpretability techniques.
What's included
17 query files organized by difficulty (6 easy, 6 medium, 5 hard) covering:
nnsight-basics: Core tracing, saving, interventionslogit-lens: Layer-wise prediction decodingactivation-patching: Causal intervention via swappingattribution-patching: Gradient-based approximationcausal-tracing: Mediation analysismodel-steering: Steering vectors, persistent editsInfrastructure:
schema.py)validators/structural.py)validators/deprecated.py)runners/)analyze.py)Documentation:
Filename changes
Query files now use descriptive hyphen-separated titles based on their content (e.g.,
extract-hidden-states.yaml,position-specific-head-patching.yaml) instead of generic numeric IDs.Test plan
python skills_benchmark/runners/base.py --num-runs 1