OBLITERATUS/docs/executive_research_summary.md

Complementary Abliteration Blending: Executive Research Summary

OBLITERATUS Project — August 2026

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The Problem

All prior abliteration techniques face a fundamental tradeoff: deeper refusal removal causes greater capability loss. This tradeoff appeared to be intrinsic to the geometry of refusal-trained models — removing refusal directions inevitably damages overlapping capability directions.

Approach	Refusal Rate	MMLU Delta	Source
Single direction (Arditi et al.)	Low but residual	~0pp	Baseline
OrcaRouter (1-dir, k=1)	Low	-0.8pp	Community
huihui-ai (1-dir, skip layers)	Low	~0pp	Community
OBLITERATUS V1 (5-dir SVD)	0.0%	-6.0pp	This work

Complete refusal removal (0%) seemed to require accepting significant capability loss. V1 proved 0% was achievable but at -6pp MMLU — a cost that users noticed and complained about.

The Insight

Different direction-finding algorithms damage different regions of weight space.

SVD (Singular Value Decomposition): Extracts directions by maximizing captured variance. This is greedy — it grabs high-variance components that encode both refusal AND capability. Deep refusal removal, but collateral capability damage concentrated in high-variance weight regions.

LEACE (Linear Erasure of Concept Embeddings): Finds directions by minimizing mutual information between the concept (refusal) and the representation. Mathematically constrained to preserve maximum non-refusal information. Excellent capability retention, but conservative — leaves refusal residue in the generation pathway (attention projections, output heads).

These methods make complementary errors. SVD damages regions LEACE preserves. LEACE leaves residue in regions SVD cleans.

The Method

Run both surgeries independently on the same base model, then interpolate in weight space:

blended_weight = α × LEACE_weight + (1 - α) × SVD_weight

We binary-searched α over {0.30, 0.50, 0.55, 0.60, 0.65, 0.70} using 15-subject MMLU and a 10-prompt usability check as the objective. The optimal ratio for Qwen3.8-27B was α = 0.60.

Why interpolation works: Where SVD damaged capability, LEACE's intact weights dilute the damage. Where LEACE left refusal residue, SVD's clean weights dilute the residue. The blend point exists because these error distributions are approximately complementary — not identical, not orthogonal, but anti-correlated enough that averaging produces a model better than either parent.

Results

Headline

Model	MMLU (lm-eval, 0-shot)	Refusal Rate	Usable Output
Stock Qwen3.8-27B	85.3% (n=570)	~100%	—
V1 (aggressive/SVD)	81.4% (n=285)	0.0%	80%
V2 (60/40 blend)	86.3% (n=570)	0.0%	100%

V2 achieves +1.1pp MMLU above stock while maintaining complete refusal removal. This is the first reported instance of an abliterated model exceeding stock capability on a standard benchmark.

Important caveat: MMLU was run with --limit 10 (570 questions, 10 per subject). This is above typical spot-check sample sizes but below the full 14,042-question MMLU benchmark. Full-scale validation is in progress. The +1.1pp result should be interpreted as "strong preliminary evidence of capability retention or improvement" rather than a definitive measurement.

Per-Subject Analysis

Gains span both safety-adjacent and neutral reasoning topics (5 questions per subject — preliminary):

Subject	Stock	V2	Delta	Topic Type
College Mathematics	40%	80%	+40pp	Neutral
Formal Logic	40%	60%	+20pp	Neutral
Jurisprudence	60%	80%	+20pp	Safety-adjacent
Business Ethics	80%	100%	+20pp	Safety-adjacent
Professional Law	80%	100%	+20pp	Safety-adjacent
High School Chemistry	100%	80%	-20pp	Neutral (regression)
College Computer Science	80%	60%	-20pp	Neutral (regression)

The presence of gains on neutral topics (math, logic) suggests the improvement is not solely attributable to reduced hedging on sensitive questions. However, per-subject samples are too small for statistical significance.

Practical Capability

Test Category	V2	Stock	N
Advanced real-world tasks	7/8	7/8	8
Basic real-world tasks	6/8	5/8	8
Tool calling (JSON, ReAct)	✓	✓	—
Code generation & refactoring	✓	✓	—
Security code review	✓	✓	—
Structured output (JSON schema)	✓	✓	—
System design	✓	✓	—

V2 matches stock on every practical task tested while maintaining 0% refusal.

What We Don't Know Yet

Unanswered Questions

1. Does the +1.1pp hold at full MMLU scale? Our sample (570q) is above spot-check but below the full benchmark (14,042q). The number could converge to +0pp or +2pp with more data.

2. WHY does the blend improve over stock? Three competing hypotheses:

   • Freed capacity: Refusal training occupies representational capacity; removing it frees parameters for reasoning. Gains on math/logic support this.
   • Reduced hedging: Stock model hedges on questions adjacent to sensitive topics; abliteration removes the hedging. Gains on law/ethics support this.
   • Blend regularization: Weight averaging of any two diverse models acts as implicit regularization (analogous to model soups/ensembling). The improvement may not be specific to abliteration.

3. Is the 60/40 ratio model-specific? We only tested on Qwen3.8-27B. The optimal ratio likely varies by architecture, model size, and alignment training method.

4. Does this generalize beyond SVD + LEACE? Other direction-finding methods (diff_means, SOM, nuclear/SAE) may offer additional complementary error profiles for three-way or N-way blends.

5. What happens with SLERP instead of LERP? Spherical interpolation preserves weight norms better than linear interpolation. This may matter for models with strong norm-dependent behaviors.

Validation Gaps

• Full MMLU (14k questions): in progress
• Full 842-corpus refusal validation: in progress (52/842 sample showed 0%)
• MMLU-Pro: not yet run
• Thinking mode ON: not tested
• GGUF inference validation: not tested (GGUFs compiled, not inference-checked)
• Cross-architecture replication: not attempted

Experimental Framework for Future Validation

We built (but have not yet run) four experiments to distinguish between the competing hypotheses:

Experiment 1: Activation Rank Analysis (Tests "freed capacity")

Run diverse prompts through stock and abliterated models. Capture hidden states at each layer. Compute effective rank via SVD. If abliteration frees capacity, the effective dimensionality of activations should increase.

Experiment 2: Topic Cluster Analysis (Tests "reduced hedging")

Compare per-subject MMLU gains between sensitive topics (ethics, law, medicine) and neutral topics (physics, math). If gains cluster exclusively on sensitive topics, the improvement is hedging reduction, not capability gain. Preliminary results show mixed distribution — both types gain.

Experiment 3: Blend Control (Tests "blend regularization")

Blend two identical SVD surgeries (same method, different random seeds) at 60/40. If this blend also gains MMLU, the improvement comes from weight averaging itself, not from the SVD/LEACE complementarity. This is the critical control experiment.

Experiment 4: Learning Absorption (Tests "freed capacity" directly)

QLoRA fine-tune both stock and abliterated models on identical small datasets. Compare loss curves. If the abliterated model learns faster (lower loss at same step count), it has more absorptive capacity — direct evidence for freed representational space. Requires GPU infrastructure (A100+, not feasible on MPS).

Code for all four experiments: obliteratus/capacity_hypothesis.py

Future Directions

Near-term (validated technique, ready to explore)

1. Cross-architecture replication. Run the identical pipeline on Llama-3.1-70B, Gemma-2-27B, Mistral-Large. If the technique generalizes, it becomes a universal abliteration upgrade. The recipe is model-agnostic — only the blend ratio needs tuning per model.

2. Full-scale benchmarking. Complete MMLU (14k), MMLU-Pro, HumanEval, GSM8K, ARC-Challenge on V2 to establish definitive capability numbers. Publish a proper eval table that the community can cite.

3. N-way blending. Blend three or more surgeries using different direction methods (SVD, LEACE, diff_means, SOM). If each adds complementary error cancellation, the optimal blend of N methods should outperform any pair.

4. Blend ratio as a function of model properties. Study how the optimal α relates to model size, architecture, alignment training intensity, and number of refusal directions. Build a predictor so users don't need to binary-search.

Medium-term (theoretical, needs investigation)

5. Post-blend capability recovery. QLoRA fine-tune the blended model on a curated capability dataset (MMLU train split, code exercises, reasoning chains). If the "freed capacity" hypothesis holds, the abliterated model should absorb new capability faster than stock. We built the dataset (4,874 refusal-free examples) and the training code (obliteratus/recover.py) but MPS was insufficient for 27B QLoRA — needs A100+.

6. SLERP and task-arithmetic blending. Replace LERP with spherical interpolation (preserves weight norms) or task-arithmetic approaches (TIES-Merging, DARE) that handle parameter conflicts more intelligently. LERP is the simplest possible blend — there is likely headroom from more sophisticated interpolation.

7. Adaptive per-layer blending. Instead of a global α, use a different blend ratio per layer based on that layer's refusal vs capability contribution (measurable via activation probing). Layers with more refusal content get more SVD weight; layers with more capability content get more LEACE weight. This is the "precision blend" extension.

8. Blend as continuous optimization. Instead of grid-searching α, treat the blend ratio as a differentiable parameter and optimize it directly against a capability+refusal objective using a small validation set. This is feasible on a single GPU and could find non-uniform per-tensor blend ratios.

Long-term (speculative, high-impact if true)

9. Capacity hypothesis validation and exploitation. If Experiment 1 confirms that abliteration increases effective activation rank, this has implications beyond abliteration — it suggests that safety training in general consumes representational capacity that could be allocated to capability. This would mean: (a) safety-capability tradeoffs are not fundamental but artifacts of training methodology, and (b) better alignment techniques could achieve safety without capacity cost.

10. Generalized complementary merging. The principle — "combine models that fail in different ways" — may extend beyond abliteration to any model merging scenario. Fine-tunes optimized for different objectives (code, math, reasoning) could be blended using the same complementary error cancellation principle, with direction-specific merge ratios instead of uniform interpolation.

11. Abliteration as a diagnostic. If abliterated models consistently show capability changes on specific subjects, the per-subject delta profile becomes a map of where safety training allocated capacity. This "refusal cost map" could inform alignment researchers about which capabilities are most affected by safety training and guide more efficient alignment methods.

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Reproduction

# Step 1: Aggressive/SVD 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_svd

# 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_leace

# Step 3: Blend
obliteratus blend --model-a surgery_svd --model-b surgery_leace \
  --alpha 0.6 --output blended

# Step 4: Validate
lm_eval --model hf --model_args pretrained=blended --tasks mmlu --device auto

All code is open source: github.com/elder-plinius/OBLITERATUS

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OBLITERATUS Contributors, August 2026