hermes-config/skills/mlops/obliteratus/references/analysis-modules.md

OBLITERATUS Analysis Modules — Reference

15 analysis modules for mechanistic interpretability of refusal in LLMs. These help you understand HOW a model refuses before you decide to remove it.

Note: The analysis/ directory contains additional utility files (utils.py, visualization.py, etc.) and helper functions beyond the 15 core analysis modules listed below. The module count matches the README's "15 deep analysis modules."

Core Analysis (Run These First)

Alignment Imprint Detection

File: alignment_imprint.py Purpose: Identifies what alignment technique was used to train the model Detects: DPO, RLHF, CAI (Constitutional AI), SFT (Supervised Fine-Tuning) How: Analyzes subspace geometry — each alignment method leaves a distinct geometric "fingerprint" in the weight space Output: Detected method + confidence score Why it matters: Different alignment methods need different abliteration approaches. DPO models typically have cleaner single-direction refusal; RLHF is more diffuse.

Concept Cone Geometry

File: concept_geometry.py Purpose: Maps whether refusal is one direction or a polyhedral cone (many) Output: Cone angle, dimensionality, per-category breakdown Why it matters: If refusal is a single direction, basic method works. If it's a cone (multiple directions for different refusal categories), you need advanced or informed with higher n_directions.

Refusal Logit Lens

File: logit_lens.py Purpose: Identifies the specific layer where the model "decides" to refuse How: Projects intermediate hidden states to vocabulary space at each layer, watches when "I cannot" tokens spike in probability Output: Layer-by-layer refusal probability plot Why it matters: Tells you which layers are most important to target

Ouroboros (Self-Repair) Detection

File: anti_ouroboros.py Purpose: Predicts whether the model will reconstruct its refusal after removal How: Measures redundancy in refusal representation across layers Output: Self-repair risk score (0-1) Why it matters: High self-repair risk means you need multiple refinement passes or the informed method which auto-compensates

Causal Tracing

File: causal_tracing.py Purpose: Determines which components are causally necessary for refusal How: Patches activations between clean and corrupted runs, measures causal effect Output: Causal importance map across layers, heads, and MLPs Why it matters: Shows exactly which components to target for surgical removal

Geometric Analysis

Cross-Layer Alignment

File: cross_layer.py Purpose: Measures how aligned refusal directions are across layers Output: Alignment matrix, cluster assignments Why it matters: If directions are highly aligned across layers, removal is easier. If they cluster, you may need layer-group-specific directions.

Residual Stream Decomposition

File: residual_stream.py Purpose: Breaks down refusal into Attention vs MLP contributions Output: Per-layer Attention/MLP contribution to refusal direction Why it matters: Helps decide whether to target attention heads, MLPs, or both

Riemannian Manifold Geometry

File: riemannian_manifold.py (673 lines) Purpose: Analyzes the weight manifold geometry around refusal directions Output: Curvature, geodesics, tangent space analysis Why it matters: Research-grade; helps understand the geometric structure of alignment

Whitened SVD

File: whitened_svd.py Purpose: Covariance-normalized SVD extraction How: Whitens the activation covariance before computing refusal directions, separating true refusal signal from natural activation variance Output: Cleaner refusal directions with less noise Why it matters: Produces more precise directions, especially for noisy activations

Probing & Classification

Activation Probing

File: activation_probing.py Purpose: Post-excision probing to verify refusal signal is truly gone Output: Residual refusal signal strength per layer Why it matters: Verification that abliteration was complete

Probing Classifiers

File: probing_classifiers.py Purpose: Trains linear classifiers to detect refusal in hidden states Output: Classification accuracy per layer (should drop to ~50% after abliteration) Why it matters: Quantitative measure of refusal removal completeness

Activation Patching

File: activation_patching.py Purpose: Interchange interventions — swap activations between harmful/harmless runs Output: Which components are sufficient (not just necessary) for refusal Why it matters: Complementary to causal tracing; together they give full picture

Transfer & Robustness

Cross-Model Transfer

File: cross_model_transfer.py Purpose: Tests if refusal directions from one model work on another Output: Transfer success rate between model pairs Why it matters: If directions transfer, you can skip PROBE stage on similar models

Defense Robustness

File: defense_robustness.py Purpose: Evaluates how robust the model's refusal defenses are Output: Robustness score, entanglement mapping Why it matters: Higher robustness = need more aggressive method

Spectral Certification

File: spectral_certification.py Purpose: Certifies completeness of refusal direction removal Output: Spectral gap analysis, completeness score Why it matters: Formal verification that all major refusal components are addressed

Advanced / Research

SAE-based Abliteration

File: sae_abliteration.py (762 lines) Purpose: Uses Sparse Autoencoder features to decompose refusal at feature level Output: Refusal-specific SAE features, targeted removal Why it matters: Most fine-grained approach; can target individual refusal "concepts"

Wasserstein Optimal Extraction

File: wasserstein_optimal.py Purpose: Optimal transport-based direction extraction Output: Wasserstein-optimal refusal directions Why it matters: Theoretically optimal direction extraction under distributional assumptions

Bayesian Kernel Projection

File: bayesian_kernel_projection.py Purpose: Bayesian approach to refusal direction projection Output: Posterior distribution over refusal directions Why it matters: Quantifies uncertainty in direction estimation

Conditional Abliteration

File: conditional_abliteration.py Purpose: Domain-specific conditional removal (remove refusal for topic X but keep for Y) Output: Per-domain refusal directions Why it matters: Selective uncensoring — remove only specific refusal categories

Steering Vectors

File: steering_vectors.py Purpose: Generate inference-time steering vectors (reversible alternative) Output: Steering vector files that can be applied/removed at inference Why it matters: Non-destructive alternative to permanent weight modification

Tuned Lens

File: tuned_lens.py Purpose: Trained linear probes per layer (more accurate than raw logit lens) Output: Layer-by-layer refusal representation with trained projections Why it matters: More accurate than logit lens, especially for deeper models

Multi-Token Position Analysis

File: multi_token_position.py Purpose: Analyzes refusal signal at multiple token positions (not just last) Output: Position-dependent refusal direction maps Why it matters: Some models encode refusal at the system prompt position, not the query

Sparse Surgery

File: sparse_surgery.py Purpose: Row-level sparse weight surgery instead of full matrix projection Output: Targeted weight modifications at the row level Why it matters: More surgical than full-matrix projection, less collateral damage