Reading academic papers with citations intact

A graduate student's literature review is a 50-paper corpus by the second year of a thesis. Generic AI tools that "summarize papers" are a research liability — they hallucinate authors and DOIs that look perfectly real. The right tool answers only from the papers you uploaded, with the page references you'd actually cite.

The problem

Citation managers organize. They don't synthesize. Reading 50 papers thoroughly is the dissertation work; reading them often, by topic, mid-draft is the supporting work that costs you weeks. Generic AI summarizers produce confident paragraphs you can't safely cite.

How SeekFiles AI helps

• Cited only from your uploaded papers — never invents external references.
• Cross-paper Q&A — "how does this dataset's reported effect size compare across the 8 papers I uploaded?"
• Hybrid retrieval finds substantive matches across discipline-specific terminology.
• Refuses to guess when no paper in your corpus addresses the question.

A workflow

1. Build a per-thesis assistant. Upload every paper from your reading list.
2. Mid-draft: ask thematic questions and pull the cited passages directly into your literature review.
3. Methods chapter: ask "what methodological choices have papers 3, 7, and 14 made for sample selection — and how did they justify each?"
4. Defense prep: ask the assistant the questions your committee will ask. Find your weakest cited support, fix it.

What to upload

• Every paper in your literature review
• Methodology textbooks and seminal works
• Your supervisor's papers (yes, all of them — they will be quoted at you)
• Your own prior drafts

Synthesis stays your job. Re-reading 50 papers to find one quote — doesn't have to be.