AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, experimentation, validation, reporting, and revision. This shift marks a transition from task-level AI for science to workflow-level research automation. Yet current systems remain fragmented, differing in autonomy, domain scope, execution environment, validation mechanism, and human oversight, while still struggling with evidence preservation, reproducibility, weak-direction rejection, provenance tracking, cross-domain robustness, and accountable scientific closure. This survey examines these developments through Au...
| Authors | Guiyao Tie, Jiawen Shi, Dingjie Song et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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