ARIS FAQs

Question 1

Which AI agents and IDEs are compatible with ARIS?

Accepted Answer

ARIS is highly versatile and compatible with a wide range of AI agents and IDEs. It supports Claude Code, OpenAI Codex, Cursor, Antigravity (Google's agent-first IDE), Trae (ByteDance AI IDE), and other LLMs like Kimi, LongCat, DeepSeek. Its Markdown-based skills allow easy adaptation to virtually any agent.

Question 2

What makes ARIS unique compared to other AI research tools?

Accepted Answer

ARIS stands out for its radical lightweight design with zero dependencies, built entirely on plain Markdown files, ensuring no lock-in. It also emphasizes cross-model collaboration, where different AI agents (e.g., Claude Code for execution, Codex for review) work together to avoid blind spots and enhance rigor, mimicking an adversarial review process.

Question 3

Can ARIS help with more than just writing research papers?

Accepted Answer

Yes, ARIS offers a comprehensive suite of skills beyond just paper writing. It can assist with formula derivation, AI-generated publication-quality illustrations, conference posters and presentation slides, structured grant proposal drafting (for various agencies), and even citation impact analysis, covering many aspects of the academic research lifecycle.

Question 4

What is ARIS and how does it automate research?

Accepted Answer

ARIS (Auto-claude-code-research-in-sleep) is a methodology and set of skills that automates the entire ML research paper workflow, from idea generation to writing and revision. It uses lightweight, framework-free Markdown workflows to orchestrate AI agents for tasks like literature review, experiment planning, code execution, paper writing, and critical review.

Question 5

How does ARIS ensure accuracy and prevent AI hallucinations in research papers?

Accepted Answer

ARIS incorporates features like anti-hallucination citation fetching from reputable databases such as DBLP/CrossRef, ensuring references are verifiable. Additionally, its core design of cross-model adversarial review actively probes weaknesses and identifies discrepancies that a single model might miss, leading to more robust and accurate outputs.

ARIS

ARIS

Key Features

Use Cases

Key Features

Use Cases