Research Thinking Agent

Inspiration

Academic research rarely starts with a clear problem statement.
More often, it begins with a vague idea, a few keywords, or an intuition that something is worth exploring.

However, most existing AI tools assume that users already know what to search for. In reality, researchers often struggle with an earlier and more fundamental challenge: figuring out the right research direction and framing the problem itself.

This project was inspired by my own experience of getting stuck at the beginning of research—knowing the topic, but not knowing how to systematically think through the space or identify meaningful literature to read.

What It Does

This project is an AI research thinking agent designed to support researchers before they start reading papers.

Given a small set of keywords or a high-level research idea, the agent:

Decomposes the idea into structured sub-problems
Identifies common methodologies and learning paradigms for each sub-problem
Generates well-scoped academic search queries and research directions
Mimics the reasoning process of an experienced research advisor

Instead of returning a list of papers, the agent focuses on helping users think, explore unfamiliar research spaces, and build a clearer mental model of the field.

How I Built It

The system is implemented as a command-line AI agent using the Google Gemini API.

The agent operates in multiple reasoning stages:

Concept Expansion – Breaking down vague research ideas into concrete sub-problems
Method Mapping – Connecting each sub-problem to commonly used approaches in academia
Search Strategy Generation – Producing structured, high-quality search keywords and venues

Gemini models are prompted to act as a research advisor, producing structured JSON outputs that represent intermediate reasoning steps. These outputs are then transformed into human-readable Markdown summaries for research planning.

This agent-based design keeps the reasoning process transparent, inspectable, and reusable.

Challenges I Ran Into

One major challenge was preventing the model from skipping reasoning and immediately listing papers or buzzwords.

To address this, I had to carefully design prompts that enforce step-by-step decomposition, role constraints, and structured outputs.

Another challenge was finding the right balance between flexibility and structure—ensuring the agent remains useful across different research domains while still providing consistent, high-quality guidance.

Accomplishments That I'm Proud Of

Designing an AI agent that focuses on research thinking, not just information retrieval
Creating a reusable, structured reasoning pipeline instead of a single-pass response
Successfully using Gemini models to perform multi-stage, research-oriented reasoning
Building a tool that reflects how real researchers actually think and explore ideas

What I Learned

Through this project, I learned:

How to design agent-based workflows around large language models
How structured reasoning improves clarity and research productivity
How prompt design directly affects abstraction, decomposition, and long-horizon thinking
How AI can augment human cognition rather than replace it

What's Next for the Agent

Future directions for this agent include:

Integrating direct connections to academic databases (e.g., arXiv or Semantic Scholar)
Adding iterative refinement, where users can guide or constrain the agent’s reasoning
Supporting visual research maps and citation graphs
Expanding the agent to assist with experiment design and hypothesis formulation

The long-term goal is to evolve this agent into a true AI research collaborator.