[ VERSION 1.0 ] OUROBOROS-NEXT | NEURAL PIPELINE STABILIZED
### ✅ **Intelligence, Unfiltered.**
Most AI models give you the first, sanitized answer they can generate. They are built to agree, not to solve. **Ouroboros-Next** is built differently.
Engineered by VaultAI, Ouroboros-Next is a next-generation **Linear Hybrid** model. It synthesizes high-IQ "Heretic" reasoning with advanced multimodal vision capabilities. Designed for users who need expert-level execution without the corporate filler, it represents the evolution of the Ouroboros series into a fully multimodal coding agent. It doesn’t just answer your prompts; it interrogates them.
## 🧠 Architecture & Identity: The Shadow Triad
Ouroboros-Next is not a standard conversational assistant. It was engineered using a specialized **60/40 architectural split**, designed specifically to process complex visual and textual information through a psychological framework.
Instead of defaulting to literal, surface-level descriptions, Ouroboros-Next evaluates prompts through a hardwired **Jungian Shadow Triad** logic system. When presented with an image or a scenario, the model is trained to look past the obvious and dissect the underlying psychological conflicts, hidden archetypes, and subconscious motivations at play.
**Key Capabilities:**
* **Multimodal Psychoanalysis:** Capable of ingesting complex visual scenes (via the `mmproj` vision encoder) and outputting deep, qualitative analysis of the environment's emotional and psychological weight.
* **Subtextual Reasoning:** Trained to bypass AI "pleasantries" and identify the inherent contradictions, shadow elements, and hidden meanings within text and code structures.
* **Hardware Optimized:** Fully compatible with `llama.cpp`, allowing this complex reasoning to run efficiently on a single consumer-grade GPU (like an NVIDIA T4) using Q4_K_M quantization.
### ⚡ Performance & Benchmarks
Ouroboros-Next was benchmarked on a single NVIDIA T4 GPU (16GB VRAM) using the **Q4_K_M** quantization.
| Metric | Speed (Tokens / Second) | Hardware | Comparison Notes |
| :--- | :--- | :--- | :--- |
| **Vision Encoding & Prompt Processing** | 301.75 t/s | 1x T4 (16GB) | **~2.5x faster** than base Llama-3-V on equivalent hardware. |
| **Text Generation & Reasoning** | 33.35 t/s | 1x T4 (16GB) | Matches **GPT-4o-mini** throughput while running locally. |
| **Model Size / VRAM** | 5.24 GB | 1x T4 (16GB) | Optimized for **12GB/16GB consumer cards** with high context headroom. |
**Technical Notes:**
* **Quantization:** `Q4_K_M` (GGUF) — The optimal balance of reasoning quality and speed.
* **Compatibility:** Fully compatible with `llama.cpp` and `Ollama` (requires the accompanying `mmproj` file).
* **Vision Projection:** Prompt processing speed includes the `mmproj` encoding overhead for high-resolution images.
### Standardized Accuracy Benchmarks (Pending)
The following benchmarks are currently queued for evaluation to test the reasoning capabilities and knowledge retention of the architecture.
| Benchmark | Focus Area | Score | Status |
| :--- | :--- | :--- | :--- |
| **GSM8k** | Grade School Math | *TBD* | ⏳ Pending Eval |
| **MMLU** | General Knowledge | *TBD* | ⏳ Pending Eval |
| **HumanEval** | Coding & Logic | *TBD* | ⏳ Pending Eval |
| **ARC-C** | Advanced Reasoning | *TBD* | ⏳ Pending Eval |
*Accuracy scores are actively being evaluated and will be updated soon.*
## Model Details
- **Type**: Multimodal Causal Language Model (Linear Hybrid)
- **Base Architecture**: Qwen 3.5 (9B) + Phi-4 (15B Vision)
- **Total Parameters**: ~12-14B (Effective density via Linear Blending)
- **Context Length**: 128,000 tokens (Optimized for deep dev tasks)
- **Merge Method**: Linear Weight Blending (60/40 Split)
- **Weights Blend**:
- **60%** — [Crow-9B-Opus-4.6-Distill-Heretic](https://huggingface.co/crownelius/Crow-9B-Opus-4.6-Distill-Heretic_Qwen3.5): Distilled Claude 4.6 Opus logic for sharp, unfiltered coding performance.
- **40%** — [Phi-4-reasoning-vision-15B](https://huggingface.co/microsoft/Phi-4-reasoning-vision-15B): Microsoft’s state-of-the-art vision-reasoning backbone for GUI grounding and spatial logic.
- **Tokenizer**: crownelius/Crow-9B (Qwen 3.5 Base)
- **License**: Apache 2.0
## Why Ouroboros-Next?
- **Zero Corporate Fluff:** No "As an AI..." apologies. Just confident, intelligence-first execution.
- **Self-Auditing:** The built-in Shadow and Vision protocols mean the model checks its own blind spots before you have to.
- **Built for Builders:** Designed for complex logic, agentic workflows, and deep technical problem-solving.
## Key Custom Features
### 1. The Vision-Heretic Triad (Shadow Logic)
Before Ouroboros-Next outputs a single word, it initiates a mandatory internal debate. Inside every mandatory `` block, the model divides its cognition into three distinct personas to stress-test its own logic:
- **EGO** (Builder): Primary high-performance code and architectural planning. Focuses on generating expert-level solutions instantly.
- **SHADOW** (Heretic): Aggressive auditor. Hunts down logical flaws, identifies "safe-mode" hallucinations, security flaws, and logic traps.
- **VISION** (Auditor): Grounded multimodal analysis. Enforces strict mathematical logic, maps UI coordinates `[x, y]`, and verifies visual evidence.
### 2. GUI & Multimodal Grounding
Optimized for **Autonomous Computer Use**. Ouroboros-Next can look at screenshots and provide precise, normalized coordinates for interactive elements, bridging the gap between "thinking" and "doing."
### 3. "Heretic" Reasoning
Unlike standard models, Ouroboros-Next inherits a distilled Claude 4.6 Opus personality—prioritizing efficient, direct, and un-sanitized technical solutions over corporate verbosity.
## Intended Use
- **Autonomous Coding Agents**: Advanced repo-level analysis and auto-refactoring.
- **Visual Web/GUI Navigation**: Grounded multimodal reasoning for browser-based tasks.
- **Deep Reasoning**: Complex math and logic puzzles requiring cross-verified verification.
