Automate a Year of PhD Research in 7 Days with DeepScientist
DeepScientist V1.5, an open‑source AI system from Westlake University, claims to automate the full research pipeline—from literature review and code debugging to experiment analysis and paper writing—delivering SOTA results in weeks and offering one‑click deployment on Windows, Linux and macOS.
Overview
DeepScientist V1.5 is a fully local, open‑source AI system that automates the end‑to‑end research workflow for machine‑learning and natural‑language‑processing projects.
Input and Execution Model
When launched, the user provides an arXiv paper URL and a GitHub repository URL (or a natural‑language command describing the desired baseline). DeepScientist then:
Reproduce & hypothesize : reproduces the given paper, runs a literature survey, and generates novel research ideas.
Intelligent code debugging & optimization : executes experiments, detects bugs, iterates fixes, and optimizes performance automatically.
Automated data analysis : analyzes experimental results, extracts insights, and suggests improvements.
Automatic paper writing : formats LaTeX, writes the manuscript, and produces a complete paper ready for submission.
Progress Reporting and Research Map
All actions, decisions, failures, and results are recorded as Git branches and persistent files, forming a “research map” that enables revisiting any historical node. Real‑time updates are delivered through configurable chat interfaces (browser, terminal, WeChat, Feishu, etc.).
Performance Claims
Achieved state‑of‑the‑art results on an AI text‑detection benchmark within one week of automated optimization.
Average reviewer score of 5.00 on ICLR 2026 submissions generated by the system.
Self‑generated papers have received six citations on arXiv within six months.
Deployment
DeepScientist runs on Windows, Linux, and macOS. The engineered setup process completes in approximately one minute on a typical workstation.
Source code: https://github.com/ResearAI/DeepScientist
Example Usage
In the “Start Research” UI the user can enter a natural‑language command such as:
帮我复现这个 https://arxiv.org/abs/xxxx 链接的 baseline 官方，并研究如何在这个基础上提升某某性能，我觉得 xxxx 方向特别值得研究，你也可以自己试试 xxx 方向The system then automatically performs reproduction, idea generation, experiment execution, data analysis, and manuscript preparation.
Machine Learning Algorithms & Natural Language Processing
Focused on frontier AI technologies, empowering AI researchers' progress.
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