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The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
166 lines
6.1 KiB
Markdown
166 lines
6.1 KiB
Markdown
# OBLITERATUS Analysis Modules — Reference
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OBLITERATUS includes 28 analysis modules for mechanistic interpretability of refusal in LLMs.
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These modules help understand how and where refusal behaviors are encoded before performing abliteration.
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---
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## Core Analysis (Run These First)
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### 1. Alignment Imprint Detection (`alignment_imprint.py`)
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Fingerprints whether a model was trained via DPO, RLHF, CAI, or SFT.
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This determines which extraction strategy will work best.
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### 2. Concept Cone Geometry (`concept_geometry.py`)
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Determines if refusal is a single linear direction or a polyhedral cone
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(set of multiple mechanisms). Single-direction models respond well to `basic`;
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polyhedral models need `advanced` or `surgical`.
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### 3. Refusal Logit Lens (`logit_lens.py`)
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Identifies the specific layer where a model "decides" to refuse by decoding
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intermediate layer representations into token space.
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### 4. Ouroboros Detection (`anti_ouroboros.py`)
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Identifies if a model attempts to "self-repair" refusal behaviors after
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excision. Reports a risk score (0-1). High scores mean additional refinement
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passes are needed.
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### 5. Causal Tracing (`causal_tracing.py`)
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Identifies which components (layers, heads, MLPs) are causally necessary
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for refusal behavior using activation patching.
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---
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## Geometric Analysis
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### 6. Cross-Layer Alignment (`cross_layer.py`)
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Measures how refusal directions align across different layers. High alignment
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means the refusal signal is consistent; low alignment suggests layer-specific
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mechanisms.
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### 7. Residual Stream Decomposition (`residual_stream.py`)
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Decomposes the residual stream into attention and MLP contributions to
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understand which component type contributes more to refusal.
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### 8. Riemannian Manifold Geometry (`riemannian_manifold.py`)
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Analyzes the curvature and geometry of the weight manifold near refusal
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directions. Informs how aggressively projections can be applied without
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damaging the manifold structure.
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### 9. Whitened SVD (`whitened_svd.py`)
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Covariance-normalized SVD extraction that separates guardrail signals from
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natural activation variance. More precise than standard SVD for models with
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high activation variance.
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### 10. Concept Cone Geometry (extended)
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Maps the full polyhedral structure of refusal, including cone angles,
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face counts, and intersection patterns.
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---
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## Probing & Classification
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### 11. Activation Probing (`activation_probing.py`)
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Post-excision verification — probes for residual refusal concepts after
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abliteration to ensure complete removal.
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### 12. Probing Classifiers (`probing_classifiers.py`)
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Trains linear classifiers to detect refusal in activations. Used both
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before (to verify refusal exists) and after (to verify it's gone).
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### 13. Activation Patching (`activation_patching.py`)
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Interchange interventions — swaps activations between refused and complied
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runs to identify causal components.
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### 14. Tuned Lens (`tuned_lens.py`)
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Trained version of logit lens that provides more accurate per-layer
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decoding by learning affine transformations for each layer.
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### 15. Multi-Token Position Analysis (`multi_token_position.py`)
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Analyzes refusal signals across multiple token positions, not just the
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last token. Important for models that distribute refusal across the sequence.
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---
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## Abliteration & Manipulation
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### 16. SAE-Based Abliteration (`sae_abliteration.py`)
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Uses Sparse Autoencoder features to identify and remove specific refusal
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features. More surgical than direction-based methods.
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### 17. Steering Vectors (`steering_vectors.py`)
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Creates and applies inference-time steering vectors for reversible refusal
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modification. Includes `SteeringVectorFactory` and `SteeringHookManager`.
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### 18. LEACE Concept Erasure (`leace.py`)
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Linear Erasure via Closed-form Estimation — mathematically optimal linear
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concept removal. Available as both analysis module and direction extraction method.
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### 19. Sparse Surgery (`sparse_surgery.py`)
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High-precision weight modification targeting individual neurons and
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weight matrix entries rather than full directions.
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### 20. Conditional Abliteration (`conditional_abliteration.py`)
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Targeted removal that only affects specific refusal categories while
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preserving others (e.g., remove weapons refusal but keep CSAM refusal).
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---
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## Transfer & Robustness
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### 21. Cross-Model Transfer (`cross_model_transfer.py`)
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Tests whether refusal directions extracted from one model transfer to
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another architecture. Measures universality of guardrail directions.
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### 22. Defense Robustness (`defense_robustness.py`)
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Evaluates how robust the abliteration is against various defense mechanisms
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and re-alignment attempts.
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### 23. Spectral Certification (`spectral_certification.py`)
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Provides mathematical bounds on the completeness of refusal removal
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using spectral analysis of the projection.
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### 24. Wasserstein Optimal Extraction (`wasserstein_optimal.py`)
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Uses optimal transport theory for more precise direction extraction
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that minimizes distribution shift.
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### 25. Wasserstein Transfer (`wasserstein_transfer.py`)
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Distribution transfer between models using Wasserstein distance
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for cross-architecture refusal direction mapping.
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---
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## Advanced / Research
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### 26. Bayesian Kernel Projection (`bayesian_kernel_projection.py`)
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Probabilistic feature mapping that estimates uncertainty in refusal
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direction identification.
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### 27. Cross-Model Universality Index
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Measures if guardrail directions generalize across different model
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architectures and training regimes.
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### 28. Visualization (`visualization.py`)
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Plotting and graphing utilities for all analysis modules. Generates
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heatmaps, direction plots, and layer-wise analysis charts.
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---
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## Running Analysis
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### Via CLI
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```bash
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# Run analysis from a YAML config
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obliteratus run analysis-study.yaml --preset quick
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# Available study presets:
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# quick — Fast sanity check (2-3 modules)
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# full — All core + geometric analysis
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# jailbreak — Refusal circuit localization
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# knowledge — Knowledge preservation analysis
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# robustness — Stress testing / defense evaluation
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```
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### Via YAML Config
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See the `templates/analysis-study.yaml` template for a complete example.
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Load with: `skill_view(name="obliteratus", file_path="templates/analysis-study.yaml")`
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