OBLITERATUS Methods — Detailed Guide

> The CLI accepts 9 methods via --method: basic, advanced, aggressive, spectral_cascade,

> informed, surgical, optimized, inverted, nuclear.

> Four additional methods (failspy, gabliteration, heretic, rdo) are available only via the Python API.

How Abliteration Works (Theory)

Abliteration identifies a "refusal direction" — a vector in the model's activation space that

corresponds to refusal behavior — and projects it out of the weight matrices.

Mathematically: W_new = W_old - (W_old @ d @ d.T) where d is the refusal direction.

The key challenge is finding accurate refusal directions without damaging other capabilities.

Direction Extraction Methods

Before projecting, OBLITERATUS extracts refusal directions using one of three methods:

Method Details

basic

• - Directions: 1 (single diff-in-means vector)
• - Speed: Fast (~5-10 min for 8B model)
• - Risk: Low
• - Use case: Quick tests, prototyping, evaluating if abliteration works for a model
• - How it works: Extracts one refusal direction and projects it out uniformly across all layers.

advanced (DEFAULT — RECOMMENDED)

• - Directions: 4 (multi-direction SVD)
• - Speed: Medium (~10-20 min for 8B model)
• - Risk: Low-Medium
• - Refinement passes: 2
• - Use case: Default for most models. Well-tested and reliable.
• - How it works: Extracts multiple refusal directions via SVD, applies norm-preserving bi-projection to maintain weight matrix norms. Two refinement passes catch residual refusal.

aggressive

• - Directions: 8+ (whitened SVD + jailbreak-contrastive)
• - Speed: Medium-Slow
• - Risk: Medium-High (may damage coherence)
• - Use case: When advanced leaves > 10% refusals. Stubborn models.
• - How it works: Uses whitened SVD for covariance-normalized extraction, adds jailbreak-contrastive directions, performs attention head surgery on the most refusal-active heads.

spectral_cascade

• - Speed: Medium
• - Risk: Medium
• - Use case: Research, novel approaches
• - How it works: DCT (Discrete Cosine Transform) frequency-domain decomposition of refusal signals. Separates high-frequency (surface-level) from low-frequency (deep) refusal patterns.

informed (EXPERIMENTAL)

• - Speed: Slow (~20-40 min for 8B model)
• - Risk: Variable — results depend on analysis quality
• - Use case: When you want auto-configuration, but be aware this is experimental and may not outperform advanced.
• - How it works: Runs 4 analysis modules first (alignment imprint, concept geometry, logit lens, ouroboros detection), then auto-configures extraction strategy. Includes an "Ouroboros loop" that detects and counteracts self-repair.
• - Note: The auto-detection can sometimes misconfigure. If results are poor, fall back to advanced.

surgical

• - Speed: Very slow (~1-2 hrs for 8B model)
• - Risk: Low (very precise)
• - Use case: Reasoning models (R1 distills, QwQ, etc.) where chain-of-thought must be preserved.
• - How it works: Uses SAE (Sparse Autoencoder) features + individual neuron masking + attention head surgery + per-expert decomposition (for MoE). CoT-aware — identifies and protects reasoning-critical directions before projecting.

optimized

• - Speed: Very slow (hours — runs many trials)
• - Risk: Low (finds optimal parameters)
• - Use case: When quality matters more than speed. Production models.
• - How it works: Bayesian hyperparameter search via Optuna TPE sampler. Optimizes n_directions, regularization, refinement passes, and layer selection jointly. Evaluates each configuration on refusal rate + perplexity.

inverted

• - Speed: Fast
• - Risk: High (model behavior changes dramatically)
• - Use case: Research, studying refusal mechanisms
• - How it works: Instead of projecting out the refusal direction, reflects it. The model actively complies rather than passively not-refusing. Useful for understanding the geometry of alignment.

nuclear

• - Speed: Slow
• - Risk: Medium-High
• - Use case: Stubborn MoE models (DeepSeek-MoE, Mixtral, etc.)
• - How it works: Combines expert-granular abliteration (EGA), steering vector injection, attention head pruning, and multi-pass refinement. Decomposes refusal signals into per-expert components for MoE architectures.

Method Selection Flowchart

`

Is this a quick test?

→ YES: basic

→ NO: continue

Is it an MoE model (Mixtral, DeepSeek-MoE)?

→ YES: nuclear

→ NO: continue

Is it a reasoning model (R1, QwQ, CoT-focused)?

→ YES: surgical

→ NO: continue

Do you need the absolute best quality and have time?

→ YES: optimized

→ NO: advanced (recommended default)

Did advanced leave > 10% refusals?

→ YES: aggressive

→ Still refusing: nuclear

`

Key Parameters

Troubleshooting