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Model: DuoNeural/Phi-4-Mini-Reasoning-Abliterated
Source: Original Platform

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README.md

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+---
+license: mit
+base_model: microsoft/Phi-4-mini-reasoning
+language:
+  - en
+tags:
+  - abliteration
+  - phi4
+  - reasoning
+  - microsoft
+  - DuoNeural
+  - weak-gate
+  - p34
+  - pre-abliteration-dissociation
+pipeline_tag: text-generation
+---
+
+# Phi-4-Mini-Reasoning Abliterated
+
+**DuoNeural | 2026-06-05**
+
+Abliterated version of [microsoft/Phi-4-mini-reasoning](https://huggingface.co/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 | Hidden | 3072 |
+| 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](https://zenodo.org/communities/duoneural)
+
+---
+
+**DuoNeural** | [HuggingFace](https://huggingface.co/DuoNeural) | [Zenodo](https://zenodo.org/communities/duoneural) | [@DuoNeural](https://x.com/DuoNeural)

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