AI & ML interests
Fundamental AI Architectures, Mixture-of-Experts (MoE), Mixture-of-Collaboration (MoC), Efficient Language Models, Emergent Reasoning, Large-Scale Training, Open-Source AI, Computational Efficiency.
Architecting the future of efficient, reasoning-driven AI.
Our Philosophy
The path to greater artificial intelligence cannot solely be paved with more parameters and more data. While scale is a powerful tool, we believe that true progress lies in creating architectures that are fundamentally smarter, not just bigger.
At Auren Research, our mission is to explore and develop novel AI architectures that treat reasoning not as an emergent property of brute-force scale, but as a core computational capability. We focus on building systems that can "think" more deeply and efficiently, making state-of-the-art AI more accessible and sustainable.
Our Approach
Our research is centered on a few key principles:
🧠 Computationally-Efficient Architectures: We design models that maximize their reasoning capabilities for a given parameter budget. We believe in building smarter circuits, not just larger ones.
🤝 Collaborative Systems: Our core architectural thesis, Mixture-of-Collaboration (MoC), explores models where expert sub-networks don't just vote on an answer, but collaborate and deliberate through multiple rounds of message passing to refine their conclusions together.
🤔 Iterative Reasoning: We build models with Iterative Reasoning Layers (IRL), a mechanism that increases computational depth on a per-token basis, allowing our models to "think" longer about complex problems without a corresponding increase in parameter count.
🌍 Open-Source First: We believe that fundamental progress is accelerated through open collaboration and rigorous community review. Our foundational work, from datasets to model architectures, will be shared with the world.
Featured Projects
Our philosophy in action. These are the foundational assets we have built to pursue our research mission:
- Lunaris Codex: Our flagship research architecture. A decoder-only Transformer featuring a novel Mixture-of-Collaboration (MoC) system and Iterative Reasoning Layers (IRL), designed from the ground up for stability and efficient reasoning.
Our Goal
Our ultimate objective is to design, train, and release a series of open-source foundational models that rival the reasoning capabilities of much larger, closed-source systems. We are building the tools to make the next generation of AI more powerful, efficient, and open for everyone.