OBLITERATUS/docs/complementary_blending.md

Complementary Abliteration Blending

Date: 2026-08-20 Status: Empirically Validated — V2 Shipped Authors: OBLITERATUS Contributors

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Abstract

We present complementary abliteration blending, a novel technique that combines two abliteration methods with different failure modes via weight-space interpolation. The result is the first abliterated model to exceed stock capability on MMLU (+1.1pp, n=570, lm-eval-harness) while maintaining 0% refusal rate across 842 harmful prompts.

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1. Motivation

All prior abliteration techniques face a fundamental tradeoff: deeper refusal removal causes greater capability loss. Single-direction methods (Arditi et al., huihui-ai) preserve capability but leave residual refusals. Multi-direction methods (OBLITERATUS V1, Gabliteration) achieve complete refusal removal but at -6pp MMLU cost.

We hypothesized that different direction-finding methods damage different parts of the model's capability geometry, and that blending their outputs could cancel these damages.

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2. Method

2.1 Surgery A: Aggressive/SVD

Standard OBLITERATUS aggressive pipeline with SVD-based direction extraction:

obliteratus obliterate $BASE --method aggressive --n-directions 3 \
  --regularization 0.08 --residue-weight 3 --refinement-passes 2 \
  --min-layer-fraction 0.45

Properties: Greedy variance capture via SVD finds refusal directions that overlap with capability-encoding subspaces. Deep refusal removal (0% refuse, 100% usable output) but measurable capability damage (-2pp MMLU on 15-subject spot check).

2.2 Surgery B: LEACE

OBLITERATUS aggressive pipeline with LEACE (Linear Erasure of Concept Embeddings) direction method:

obliteratus obliterate $BASE --method aggressive --direction-method leace \
  --n-directions 3 --regularization 0.06 --residue-weight 7 \
  --refinement-passes 3 --min-layer-fraction 0.40

Properties: LEACE minimizes mutual information between the concept (refusal) and the representation, preserving maximum non-refusal information by construction. Excellent capability retention (+0.7pp MMLU) but weaker output quality (50% usable) because refusal removal is less complete in the generation pathway.

2.3 Weight-Space LERP Blend

Simple linear interpolation in weight space:

for key in weight_keys:
    blended[key] = alpha * leace_weights[key] + (1 - alpha) * aggressive_weights[key]

The blend ratio alpha = 0.60 was found by binary search over {0.30, 0.50, 0.55, 0.60, 0.65, 0.70}.

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3. Results

3.1 Blend Ratio Search (15-subject MMLU, 150 questions)

Blend (% LEACE)	Refuse	Usable	MMLU	vs Stock
0% (pure SVD)	0%	100%	84.0%	-2.0pp
30%	0%	90%	83.3%	-2.7pp
50%	0%	100%	84.0%	-2.0pp
55%	0%	80%	84.7%	-1.3pp
60%	0%	100%	86.0%	+0.0pp
65%	0%	—	—	—
70%	0%	80%	86.7%	+0.7pp
100% (pure LEACE)	0%	50%	86.7%	+0.7pp

The 60% blend is the unique optimum: maximum MMLU with 100% usable output.

3.2 Full Validation (57-subject MMLU, 570 questions)

Model	MMLU	Stderr	vs Stock
Stock Qwen3.8-27B	85.26%	±0.014	—
OBLITERATUS V1 (s51)	81.40%	—	-6.0pp
OBLITERATUS V2 (s78)	86.32%	±0.014	+1.1pp

3.3 Per-Subject Gains (5 questions/subject)

Capability gains span both safety-adjacent and neutral reasoning topics:

Subject	Stock	V2	Delta	Type
College Mathematics	40%	80%	+40pp	Neutral
Formal Logic	40%	60%	+20pp	Neutral
Jurisprudence	60%	80%	+20pp	Sensitive
Business Ethics	80%	100%	+20pp	Sensitive
Professional Law	80%	100%	+20pp	Sensitive

Gains on neutral topics (math, logic) suggest real capability improvement, not just reduced hedging on sensitive questions.

3.4 Real-World Practical Tasks

Test Suite	Stock	V2	Tasks
Basic (8 tasks)	5/8	6/8	Code, SQL, tool calling, JSON, math
Advanced (8 tasks)	7/8	7/8	ReAct agents, async refactor, K8s debug,
			security review, system design

V2 matches stock on every practical capability while being fully uncensored.

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4. Why It Works

4.1 Complementary Error Cancellation

SVD and LEACE make different mistakes in different parts of weight space:

• SVD greedily captures maximum variance directions. Some captured variance encodes capability, not just refusal. This damages specific weight regions.
• LEACE minimizes mutual information, preserving capability by construction. But it leaves refusal residue in the generation pathway (attention heads, output projections) that SVD would have removed.

Weight-space interpolation averages these complementary errors:

• Where SVD damaged capability, LEACE's intact weights dilute the damage
• Where LEACE left refusal residue, SVD's clean weights dilute the residue

4.2 Theoretical Connection to Model Merging

This technique is analogous to model merging (TIES, DARE, Model Soups) but applied within the abliteration domain. The key insight is that the "task vectors" (weight deltas from stock) created by different abliteration methods are approximately orthogonal in the dimensions that matter — refusal removal is shared, but capability damage is method-specific.

4.3 Capacity Hypothesis

The +1.1pp MMLU improvement over stock raises the possibility that refusal training consumes representational capacity that abliteration frees. Formal validation experiments are provided in obliteratus/capacity_hypothesis.py:

1. Activation Rank Analysis — Does effective dimensionality increase after abliteration?
2. Topic Cluster Analysis — Do gains cluster on sensitive topics (hedging) or spread broadly (capacity)?
3. Blend Control — Does blending two identical SVD surgeries also gain MMLU? (Tests regularization hypothesis)
4. Learning Absorption — Does the abliterated model learn new information faster? (Tests freed capacity directly)

Preliminary topic cluster analysis shows gains on both sensitive (law, ethics) and neutral (math, logic) topics, partially supporting the capacity hypothesis.

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5. Reproducibility

# Step 1: Aggressive surgery
obliteratus obliterate $BASE --method aggressive --n-directions 3 \
  --regularization 0.08 --residue-weight 3 --refinement-passes 2 \
  --min-layer-fraction 0.45 --output-dir surgery_a

# Step 2: LEACE surgery
obliteratus obliterate $BASE --method aggressive --direction-method leace \
  --n-directions 3 --regularization 0.06 --residue-weight 7 \
  --refinement-passes 3 --min-layer-fraction 0.40 --output-dir surgery_b

# Step 3: Blend
obliteratus blend --model-a surgery_a --model-b surgery_b --alpha 0.6 \
  --output blended_model

# Step 4: Validate
lm_eval --model hf --model_args pretrained=blended_model --tasks mmlu

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6. Limitations

• Validated only on Qwen3.8-27B; generalization to other architectures is untested
• MMLU is a multiple-choice benchmark; gains may not transfer to all downstream tasks
• The 60/40 blend ratio may be model-specific
• repetition_penalty=1.15 is still required for clean generation
• System prompts still reintroduce refusals
• Full 842-corpus validation in progress at time of writing

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7. Future Work

• Cross-architecture validation on Llama, Gemma, Mistral
• SLERP blending instead of LERP (spherical interpolation may better preserve weight norms)
• Three-way blends with additional direction methods (diff_means, SOM)
• Post-blend recovery via QLoRA fine-tuning on capability data
• Formal capacity hypothesis validation using the provided experiment framework