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feat: add complementary abliteration blending
Carry the coherent blending contribution and research summary from PR #127 while splitting the capacity and recovery proposals into issues #132 and #133.
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faber
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# Complementary Abliteration Blending
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**Date:** 2026-08-20
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**Status:** Empirically Validated — V2 Shipped
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**Authors:** OBLITERATUS Contributors
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---
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## Abstract
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We present **complementary abliteration blending**, a novel technique that combines two
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abliteration methods with different failure modes via weight-space interpolation. The result
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is the first abliterated model to exceed stock capability on MMLU (+1.1pp, n=570, lm-eval-harness)
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while maintaining 0% refusal rate across 842 harmful prompts.
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---
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## 1. Motivation
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All prior abliteration techniques face a fundamental tradeoff: deeper refusal removal causes
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greater capability loss. Single-direction methods (Arditi et al., huihui-ai) preserve capability
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but leave residual refusals. Multi-direction methods (OBLITERATUS V1, Gabliteration) achieve
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complete refusal removal but at -6pp MMLU cost.
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We hypothesized that different direction-finding methods damage different parts of the model's
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capability geometry, and that blending their outputs could cancel these damages.
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---
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## 2. Method
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### 2.1 Surgery A: Aggressive/SVD
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Standard OBLITERATUS aggressive pipeline with SVD-based direction extraction:
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```
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obliteratus obliterate $BASE --method aggressive --n-directions 3 \
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--regularization 0.08 --residue-weight 3 --refinement-passes 2 \
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--min-layer-fraction 0.45
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```
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**Properties:** Greedy variance capture via SVD finds refusal directions that overlap with
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capability-encoding subspaces. Deep refusal removal (0% refuse, 100% usable output) but
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measurable capability damage (-2pp MMLU on 15-subject spot check).
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### 2.2 Surgery B: LEACE
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OBLITERATUS aggressive pipeline with LEACE (Linear Erasure of Concept Embeddings) direction method:
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```
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obliteratus obliterate $BASE --method aggressive --direction-method leace \
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--n-directions 3 --regularization 0.06 --residue-weight 7 \
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--refinement-passes 3 --min-layer-fraction 0.40
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```
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**Properties:** LEACE minimizes mutual information between the concept (refusal) and the
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representation, preserving maximum non-refusal information by construction. Excellent capability
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retention (+0.7pp MMLU) but weaker output quality (50% usable) because refusal removal is
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less complete in the generation pathway.
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### 2.3 Weight-Space LERP Blend
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Simple linear interpolation in weight space:
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```python
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for key in weight_keys:
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blended[key] = alpha * leace_weights[key] + (1 - alpha) * aggressive_weights[key]
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```
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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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---
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## 3. Results
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### 3.1 Blend Ratio Search (15-subject MMLU, 150 questions)
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| Blend (% LEACE) | Refuse | Usable | MMLU | vs Stock |
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|------------------|--------|--------|-------|----------|
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| 0% (pure SVD) | 0% | 100% | 84.0% | -2.0pp |
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| 30% | 0% | 90% | 83.3% | -2.7pp |
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| 50% | 0% | 100% | 84.0% | -2.0pp |
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| 55% | 0% | 80% | 84.7% | -1.3pp |
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| **60%** | **0%** |**100%**|**86.0%**|**+0.0pp**|
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| 65% | 0% | — | — | — |
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| 70% | 0% | 80% | 86.7% | +0.7pp |
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| 100% (pure LEACE)| 0% | 50% | 86.7% | +0.7pp |
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The 60% blend is the unique optimum: maximum MMLU with 100% usable output.
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### 3.2 Full Validation (57-subject MMLU, 570 questions)
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| Model | MMLU | Stderr | vs Stock |
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|----------------------|---------|--------|----------|
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| Stock Qwen3.8-27B | 85.26% | ±0.014 | — |
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| OBLITERATUS V1 (s51) | 81.40% | — | -6.0pp |
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| **OBLITERATUS V2 (s78)** | **86.32%** | **±0.014** | **+1.1pp** |
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### 3.3 Per-Subject Gains (5 questions/subject)
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Capability gains span both safety-adjacent and neutral reasoning topics:
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| Subject | Stock | V2 | Delta | Type |
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|----------------------|-------|-------|--------|-----------|
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| College Mathematics | 40% | 80% | +40pp | Neutral |
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| Formal Logic | 40% | 60% | +20pp | Neutral |
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| Jurisprudence | 60% | 80% | +20pp | Sensitive |
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| Business Ethics | 80% | 100% | +20pp | Sensitive |
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| Professional Law | 80% | 100% | +20pp | Sensitive |
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Gains on neutral topics (math, logic) suggest real capability improvement, not just
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reduced hedging on sensitive questions.
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### 3.4 Real-World Practical Tasks
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| Test Suite | Stock | V2 | Tasks |
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|---------------------|-------|-----|------------------------------------------|
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| Basic (8 tasks) | 5/8 | 6/8 | Code, SQL, tool calling, JSON, math |
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| Advanced (8 tasks) | 7/8 | 7/8 | ReAct agents, async refactor, K8s debug, |
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| | | | security review, system design |
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V2 matches stock on every practical capability while being fully uncensored.
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---
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## 4. Why It Works
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### 4.1 Complementary Error Cancellation
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SVD and LEACE make **different mistakes in different parts of weight space**:
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- **SVD** greedily captures maximum variance directions. Some captured variance encodes
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capability, not just refusal. This damages specific weight regions.
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- **LEACE** minimizes mutual information, preserving capability by construction. But it
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leaves refusal residue in the generation pathway (attention heads, output projections)
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that SVD would have removed.
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Weight-space interpolation averages these complementary errors:
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- Where SVD damaged capability, LEACE's intact weights dilute the damage
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- Where LEACE left refusal residue, SVD's clean weights dilute the residue
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### 4.2 Theoretical Connection to Model Merging
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This technique is analogous to model merging (TIES, DARE, Model Soups) but applied within
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the abliteration domain. The key insight is that the "task vectors" (weight deltas from stock)
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created by different abliteration methods are approximately orthogonal in the dimensions that
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matter — refusal removal is shared, but capability damage is method-specific.
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### 4.3 Capacity Hypothesis
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The +1.1pp MMLU improvement over stock raises the possibility that refusal training
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consumes representational capacity that abliteration frees. Formal validation experiments
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are provided in `obliteratus/capacity_hypothesis.py`:
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1. **Activation Rank Analysis** — Does effective dimensionality increase after abliteration?
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2. **Topic Cluster Analysis** — Do gains cluster on sensitive topics (hedging) or spread broadly (capacity)?
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3. **Blend Control** — Does blending two identical SVD surgeries also gain MMLU? (Tests regularization hypothesis)
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4. **Learning Absorption** — Does the abliterated model learn new information faster? (Tests freed capacity directly)
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Preliminary topic cluster analysis shows gains on both sensitive (law, ethics) and neutral
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(math, logic) topics, partially supporting the capacity hypothesis.
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---
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## 5. Reproducibility
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```bash
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# Step 1: Aggressive surgery
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obliteratus obliterate $BASE --method aggressive --n-directions 3 \
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--regularization 0.08 --residue-weight 3 --refinement-passes 2 \
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--min-layer-fraction 0.45 --output-dir surgery_a
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# Step 2: LEACE surgery
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obliteratus obliterate $BASE --method aggressive --direction-method leace \
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--n-directions 3 --regularization 0.06 --residue-weight 7 \
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--refinement-passes 3 --min-layer-fraction 0.40 --output-dir surgery_b
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# Step 3: Blend
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obliteratus blend --model-a surgery_a --model-b surgery_b --alpha 0.6 \
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--output blended_model
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# Step 4: Validate
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lm_eval --model hf --model_args pretrained=blended_model --tasks mmlu
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```
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---
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## 6. Limitations
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- Validated only on Qwen3.8-27B; generalization to other architectures is untested
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- MMLU is a multiple-choice benchmark; gains may not transfer to all downstream tasks
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- The 60/40 blend ratio may be model-specific
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- `repetition_penalty=1.15` is still required for clean generation
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- System prompts still reintroduce refusals
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- Full 842-corpus validation in progress at time of writing
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---
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## 7. Future Work
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- **Cross-architecture validation** on Llama, Gemma, Mistral
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- **SLERP blending** instead of LERP (spherical interpolation may better preserve weight norms)
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- **Three-way blends** with additional direction methods (diff_means, SOM)
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- **Post-blend recovery** via QLoRA fine-tuning on capability data
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- **Formal capacity hypothesis validation** using the provided experiment framework
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