Multi-Domain AI Scientist: A General-Purpose System for Autonomous Scientific Discovery

Short Description

This project aims to develop a Multi-Domain AI Scientist — an intelligent multi-agent system capable of understanding, reasoning, and generating scientific knowledge across diverse research fields. Each agent specializes in a distinct aspect of scientific reasoning (e.g., literature analysis, hypothesis generation, experimental design, or result interpretation), and together they form an integrated ecosystem for autonomous discovery. Rather than focusing on a single discipline, the system is designed to learn the universal principles of scientific inquiry, enabling it to adapt seamlessly to new scientific domains without retraining. Ultimately, the goal is to build a domain- agnostic AI collaborator that can assist researchers across computer science, agriculture, physics, biology, chemistry, and beyond.

Research Objectives / Questions

  1. How can AI systems learn general scientific reasoning patterns that apply across multiple research domains?
  2. How can we evaluate the quality, validity, and originality of AI-generated hypotheses and research outputs?
  3. What kinds of multi-agent architectures, role assignments, and communication strategies enable robust, scientific reasoning and collaboration among AI agents?
  4. To what extent can a general-purpose AI Scientist contribute to accelerating real-world scientific discovery?

 Expected Outcomes

  • A prototype domain-agnostic AI Scientist capable of performing literature-based reasoning and hypothesis generation in various scientific fields.
  • Framework for evaluating AI scientific reasoning independent of specific
  • Analytical report on the strengths, weaknesses, and transferability of scientific reasoning in AI systems.

Required Skills

  • Machine Learning & NLP: Proficiency with LLMs, reasoning, and generative
  • Scientific Understanding: Ability to interpret research papers and
  • Programming: Python and
  • Evaluation & Analysis: Skills in designing metrics for creativity, accuracy, and
  • Collaboration: Coordinated teamwork for model development, experimentation, and evaluation.