Auto-claude-code-research-in-sleep

ARIS is the lightweight, agent-agnostic cousin of AutoResearchClaw. It orchestrates full ML research lifecycles — literature survey, idea generation, experiment automation, paper writing — using nothing but Markdown skill files. No framework, no database, no Docker. Works with Claude Code, Codex, OpenClaw, or any LLM agent.

The secret sauce is adversarial collaboration: Claude Code executes fast, GPT-5.4 reviews slowly and rigorously, probing weaknesses the executor missed. This cross-model tension produces better papers than single-model loops. Compared to AutoResearchClaw (23-stage pipeline, heavier), ARIS is more flexible. Compared to autoresearch (general-purpose), ARIS is research-specific.

Use this when you want autonomous ML research that runs overnight across 20+ GPU experiments. Skip this if you need a polished paper — ARIS improves drafts, it doesn't write final submissions.

The catch: cross-model collaboration means paying two API providers. And "autonomous overnight research" can burn serious GPU hours and API credits if your guard rails aren't tight.