Agentic Paper Search: How AI Reads Papers for You

Every researcher knows the frustration: you have a specific, nuanced question — something like "papers that use contrastive learning for medical image segmentation with limited labeled data" — and keyword search returns thousands of tangentially related results.

Why Keywords Fall Short

Traditional academic search engines match terms against titles, abstracts, and metadata. This works well for broad queries but breaks down when your need requires understanding the methodology, experimental setup, or contribution of a paper.

Consider these scenarios where keyword search struggles:

• Finding papers that use a specific combination of techniques
• Identifying work that addresses a particular limitation of existing methods
• Locating studies with specific experimental conditions

The Agentic Approach

An agentic paper search system doesn't just match keywords — it reads, understands, and evaluates papers against your criteria. Here's how the process works:

Step 1: Query Expansion

The system takes your natural language question and generates multiple related search terms, accounting for synonyms, related concepts, and different ways the same idea might be expressed in the literature.

Step 2: Broad Search

Using expanded terms, the system queries multiple academic APIs (arXiv, Semantic Scholar) to gather a wide pool of candidates.

Step 3: Abstract Filtering

Each candidate's abstract is evaluated for relevance. This narrows the pool from hundreds to a manageable set of promising papers.

Step 4: Full Paper Reading

This is where the agentic approach truly shines. The system downloads and reads the full text of promising papers, evaluating methodology sections, experimental setups, and results against your specific criteria.

Step 5: Synthesis

Finally, the system returns matched papers with specific section references, explaining exactly why each paper is relevant to your query.

The Difference in Practice

Imagine searching for "papers that propose novel attention mechanisms for long-document summarization and evaluate on arXiv papers."

A keyword search might return thousands of results about attention mechanisms, summarization, or arXiv — most irrelevant to your specific need.

An agentic search would read through candidate papers, identify those that specifically propose new attention mechanisms (not just use existing ones), verify they target long-document summarization (not short-text), and confirm they evaluate on arXiv paper datasets.

Building for Researchers

At Accept Ideas, our agentic paper finder is designed to handle exactly these kinds of nuanced research questions. By combining broad search with deep reading, we help you find the papers that truly matter — in seconds rather than days.

We're currently in development and will be sharing more details about our approach soon. Join our waitlist to get early access.