ARIS βοΈ (Auto-Research-In-Sleep) β Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in β works with Claude Code, Codex, OpenClaw, or any LLM agent.
SoL-Pi: Scaling Auto-Research Loops for Efficient Agent Harnesses
Turn long-horizon goals into verifiable results. A local-first control plane and research workbench for agents and teams, across sessions and runtimes.
A curated list of plugins, skills, MCP servers, patch/profile layers, orchestrators & UIs for DeepSeek Harness (DSH). Visualization Β· PPT Β· Coding Β· Agents Β· Loops (auto-research) and more. #dsh
A curated collection of automated research tools, covering literature search, paper reading, experiment management, and code generation to help researchers accelerate their workflow.
A multi-agent AUTO Research loop for idea discovery, experiment execution, evidence-grounded paper writing, quality audit, and live Web observation from AI group, CNIC, CAS.
π₯ A Survey on AI Auto-Research
A 267-skill research graph in pure markdown β 51 research operations built from 216 single-purpose steps, composed in any order with explicit backtracking. One npx install, no runtime, no MCP bindings. The AI is the researcher; you set the direction.
Toward Autonomous Scientific Discovery
EvoQuant is a self-evolving AI research agent specialized in quantitative investment research. It runs the full research loop autonomously
STEER β AI4R (AI for Research) experiment framework for auto-research and autonomous research. Connect existing research projects and let an agent edit code and train under governance: frozen task, scorer, and evidence.