Overview of the ARIS Architecture

ARIS is presented as a novel methodology for streamlining the often iterative and time-consuming process of ML research. The core principle is "Auto-Research-In-Sleep," suggesting an autonomous operational mode where the system performs deep research tasks without continuous human intervention. The implementation is deliberately lightweight and framework-agnostic, ensuring broad applicability across various research setups.

Key Capabilities and Functional Modules

The system is built around several specialized, low-overhead skills, all managed via Markdown-only interaction. These modules address critical stages of the ML research lifecycle:

• Cross-Model Review Loops: Facilitating the comparison and synthesis of findings across multiple distinct language model outputs, crucial for robustness and validation.
• Idea Discovery: Implementing automated mechanisms to generate, refine, and identify novel research hypotheses and experimental directions.
• Experiment Automation: Managing the lifecycle of ML experiments, from design parameters to execution and result logging, thereby reducing manual overhead.

Technical Flexibility and Agent Compatibility

A defining feature of ARIS is its complete lack of proprietary framework dependency or vendor lock-in. This design choice allows ARIS to interface seamlessly with a diverse ecosystem of Large Language Models (LLMs) and code agents. The system is designed to operate effectively with established models such as Claude Code, Codex, or specialized agents like OpenClaw.

By maintaining a minimalist, Markdown-based interaction layer, ARIS ensures that the research workflow remains modular and highly adaptable to evolving LLM technologies.