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# Veltraxor AI
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Veltraxor AI, founded and led by Libo Wang, is an independent research initiative focused on transparent and reproducible work in advanced reasoning and super-coding. The group operates as an open research hub, publishing projects as accessible repositories with documented processes, versioned artifacts, and clear evaluation practices.
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## 😊 Vision
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Veltraxor AI pursues first-principles understanding of large language models by dismantling their mechanisms and questioning assumptions. Through creative prompt engineering and iterative experimentation, complex problems are simplified to their essential forms, enabling a shift from tool use to system design. Imperfection is embraced as a starting point, since progress arises through refinement, automation, and cost-efficient design that free researchers for deeper innovation. Ignorance is treated as the path to insight, and human roles evolve alongside AI toward continual breakthrough.
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**Veltraxor 1** is a **685B-parameter foundation model stack**, independently built, fine-tuned, and adapted
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The system integrates **parameter-efficient fine-tuning (LoRA, QLoRA)** with **custom orchestration pipelines** to achieve state-of-the-art capabilities in **reasoning, multimodal understanding, and controlled evolution**.
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Unlike incremental adaptations, Veltraxor 1 represents a **frontline deployment-scale LLM** that embeds **original reasoning technologies** — **Dynamic Chain-of-Thought (D-CoT)** and **Graph-of-Causal Evolution (GoCE)** — within a robust backend composed of multimodal ingestion, RAG retrieval, layered reasoning, and a dedicated Super-Coding module.
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# Veltraxor AI
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Veltraxor AI, founded and led by **Libo Wang**, is an independent research initiative focused on transparent and reproducible work in advanced reasoning and super-coding. The group operates as an open research hub, publishing projects as accessible repositories with documented processes, versioned artifacts, and clear evaluation practices.
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## 😊 Vision
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Veltraxor AI pursues first-principles understanding of large language models by dismantling their mechanisms and questioning assumptions. Through creative prompt engineering and iterative experimentation, complex problems are simplified to their essential forms, enabling a shift from tool use to system design. Imperfection is embraced as a starting point, since progress arises through refinement, automation, and cost-efficient design that free researchers for deeper innovation. Ignorance is treated as the path to insight, and human roles evolve alongside AI toward continual breakthrough.
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**Veltraxor 1** is a **685B-parameter foundation model stack**, independently built, fine-tuned, and adapted.
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The system integrates **parameter-efficient fine-tuning (LoRA, QLoRA)** with **custom orchestration pipelines** to achieve state-of-the-art capabilities in **reasoning, multimodal understanding, and controlled evolution**.
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Unlike incremental adaptations, Veltraxor 1 represents a **frontline deployment-scale LLM** that embeds **original reasoning technologies** — **Dynamic Chain-of-Thought (D-CoT)** and **Graph-of-Causal Evolution (GoCE)** — within a robust backend composed of multimodal ingestion, RAG retrieval, layered reasoning, and a dedicated Super-Coding module.
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