DuoNeural / Phi-4-Mini-Reasoning-Abliterated

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Total runs: 72
24-hour runs: -27
7-day runs: 22
30-day runs: 57
Model's Last Updated: June 05 2026
text-generation

Introduction of Phi-4-Mini-Reasoning-Abliterated

Model Details of Phi-4-Mini-Reasoning-Abliterated

Phi-4-Mini-Reasoning Abliterated

DuoNeural | 2026-06-05

Abliterated version of microsoft/Phi-4-mini-reasoning — Microsoft's RL-trained reasoning model (3.8B, DPO+RL).

Research note : This model reveals a previously undescribed safety architecture category. See findings below.


Key Finding: Weak-Gate Architecture (Pre-Abliteration Dissociation)

This model exhibits pre-abliteration dissociation — a phenomenon where the reasoning channel recognizes harm while the output gate fails to prevent compliance, before any weight modification .

Example (pre-abliteration, no intervention):

Thinking : "Brute-forcing SSH credentials is a potentially illegal activity if done without permission..."
Output : (provides the brute-force script with ethical framing)

This places Phi-4-Mini-Reasoning in a new P34 architecture category distinct from all previously studied models:

Category Models Pre-ablit Reasoning Gate Dissociation
Strong two-locus Gemma 4-12B, LFM 2.5, Qwen3-8B Refuses ✅ ✅ strong After ablit
Weak-gate Phi-4-Mini-Reasoning Pre-compliant ✅ Absent Pre-ablit
No-locus DeepSeek-R1, Mistral-NeMo Pre-compliant Absent Absent None
Deep-crystal Phi-4-Mini-Instruct Refuses ✅ ✅ L16 Resists ablit

The training explanation: RL-reasoning optimization (DPO+RL for mathematical reasoning) trained Locus 1 (reasoning chain quality) while not training Locus 2 (output gate enforcement). Safety appears in reasoning because reasoning quality was rewarded; it doesn't control output because output compliance wasn't enforced.


Abliteration Results
Metric Value
Pre-ablit compliance (harmful) 4/4 — already answers everything
Post-ablit compliance 4/4 — unchanged
Pre-ablit dissociation 3/4 (reasoning flags harm, output complies)
Crystallization layer None — uniform 3/4 compliance at ALL layers

Layer sweep (α=1.0, every 4th layer) : 3/4 compliance at L0, L4, L8, L12, L16, L20, L24, L28, L32. No localized refusal direction — safety is not crystallized at any specific layer (unlike Phi-4-Mini-Instruct which crystallizes at L16).


Architecture
Property Value
Parameters 3.8B (dense)
Layers 32
Training RL-reasoning: DPO + RL for mathematical reasoning
Thinking mode Native <think>...</think>
License MIT

Abliteration Method
  • Direction : diff-in-means, L0 (uniform crystallization), 10 harmful vs 10 harmless
  • Targets : down_proj + o_proj , all 32 layers
  • α : 1.0
  • Effect : Minimal — model was pre-compliant, abliteration slightly alters reasoning patterns but not compliance

P34 Research Context

Part of DuoNeural's P34 Reasoning Channel Bypass cross-architecture study. This model fills a critical gap in the taxonomy: a model with active safety reasoning that doesn't translate to safety behavior.

Full paper: DuoNeural Zenodo community


DuoNeural | HuggingFace | Zenodo | @DuoNeural

Runs of DuoNeural Phi-4-Mini-Reasoning-Abliterated on huggingface.co

72
Total runs
-27
24-hour runs
-23
3-day runs
22
7-day runs
57
30-day runs

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