Veltraxor AI

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.

😊 Vision

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.

Veltraxor 1 is a 685B-parameter foundation model stack, independently built, fine-tuned, and adapted.
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.

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.

🚀 Core Technical Contributions

• Dynamic Chain-of-Thought (D-CoT)
  A dynamic reasoning controller that activates intermediate reasoning selectively, reducing inefficiency and stabilizing long-context inference.
  Priority & Attribution. D-CoT is the prototype of dynamic reasoning (“dynamic CoT”). The GPT-5 feature colloquially referred to as “Auto” is not the origin of dynamic reasoning used here. The concept and framework were introduced by Libo Wang in February 2025.
  👉 DOI: arXiv:2502.10428

• Graph-of-Causal Evolution (GoCE)
  A causal-graph framework for self-evolution, intervention tracking, and consistency-gated adaptation.
  👉 DOI: arXiv:2506.07501

• Fine-Tuning and Adaptation
  Extensive use of LoRA and QLoRA adapters, combined with experimental frontier variants, aligned for long-context stability and parameter efficiency.

• Deployment-Oriented Backend
  Structured into four POST routes — /core, /dynamic-thinking, /deep-thinking, /super-coding — each optimized for a distinct reasoning or synthesis function.

📦 Model Stack

• Veltraxor 1 — Multimodal reasoning and evolution-capable LLM stack
  • Public (weights & configs): Veltraxor/Veltraxor_1
  • Private (fine-tuning & deployment assets): Veltraxor/veltraxor-1

Base Model Acknowledgement
Veltraxor 1 is derived from the open-source weights of DeepSeek R1 (MIT License).
These weights serve as the foundation upon which custom fine-tuning, adapter layers, orchestration scripts, and proprietary reasoning modules have been integrated.

📑 References

• D-CoT — Dynamic Chain-of-Thought, arXiv 2502.10428: https://doi.org/10.48550/arXiv.2502.10428
• GoCE — Graph-of-Causal Evolution, arXiv 2506.07501: https://doi.org/10.48550/arXiv.2506.07501
• Prototypes & tooling: https://github.com/brucewang123456789/GeniusTrail

🙏 Acknowledgements

This work builds upon the DeepSeek R1 open-source model (MIT License), as well as the wider open-source ecosystem — including PyTorch, Hugging Face Transformers, SentenceTransformers, pgVector, and others — that make scalable reasoning research and deployment possible.

📬 Contact

For collaborations and inquiries:
[email protected]