#1 DevLog Meta-research: I Got Tired of Tab Chaos While Reading Research Papers.

Every time I sit down to explore a research topic, the same thing happens.

I open arXiv for preprints. Then Semantic Scholar for citations. Then Crossref to verify a reference. Then back to arXiv because I forgot the paper I was on. Then I lose the thread entirely.

Sound familiar?

That frustration is why I started building Meta-Research an AI-powered web platform for academic literature search, analysis, and management. It's still in active development, but I wanted to share the problem it's trying to solve and what I've built so far.

The core problem

Researching a topic today means juggling:

• Multiple search engines with overlapping but non-identical indexes
• No way to see how papers connect to each other visually
• PDFs you can read but can't talk to
• No single place to save, organize, and revisit papers

The existing tools are either paywalled, too broad, or don't integrate AI in a meaningful way. I wanted one workspace that handles all of it.

What I've built so far

1. Unified search across major databases

Instead of running the same query on four different sites, Meta-Research hits them all at once, arXiv, Crossref, OpenAlex, and Semantic Scholar and surfaces results in a single view.

# Simplified example of a unified search call
def unified_search(query):
    results = []
    results += search_arxiv(query)
    results += search_crossref(query)
    results += search_openalex(query)
    results += search_semantic_scholar(query)
    return deduplicate_and_rank(results)

Each source has its own API quirks, rate limits, and response formats normalizing them into a consistent schema was one of the trickier early problems.

2. Chat with research papers using LLMs

This is the feature I'm most excited about. You can load a paper and ask it questions directly "What methodology did they use?", "Summarize the limitations", "How does this compare to X?"

Under the hood it's using Groq (Llama) and Google Gemini, depending on the task. Groq is fast for quick Q&A; Gemini handles longer context well.

def chat_with_paper(paper_text, user_question, model="groq"):
    prompt = f"""
    You are a research assistant. Based on the paper below, answer the question.

    Paper:
    {paper_text}

    Question: {user_question}
    """
    if model == "groq":
        return query_groq(prompt)
    return query_gemini(prompt)

For cases where I don't want to hit an API, I also integrated Sumy for local extractive summarization useful for quick overviews without burning tokens.

3. Citation graph visualization

This one changes how you explore literature. Instead of manually chasing citations, Meta-Research generates an interactive graph showing how papers reference each other.

You can see clusters, find highly-cited hubs, and spot gaps — papers that cite each other a lot but aren't directly connected, which often points to an interesting research gap.

It's built dynamically on the frontend using JavaScript, with the graph data computed server-side in Flask.

4. Library and collection management

Users can save papers, create named collections ("Transformer architectures", "My thesis sources"), and pick up where they left off. Auth is handled with Flask-Login, passwords hashed via Werkzeug.

@app.route('/save_paper', methods=['POST'])
@login_required
def save_paper():
    paper_id = request.json.get('paper_id')
    collection = request.json.get('collection', 'default')
    entry = SavedPaper(user_id=current_user.id, paper_id=paper_id, collection=collection)
    db.session.add(entry)
    db.session.commit()
    return jsonify({'status': 'saved'})

Tech stack

I deliberately kept the frontend framework-free for now. Vanilla JS keeps the complexity low while the core features are still taking shape.

What's still rough (being honest)

• The citation graph can get slow with large paper sets need to add pagination or lazy loading
• Multi-source deduplication isn't perfect, the same paper from arXiv and Crossref sometimes shows up twice
• The chat feature works well on shorter papers but struggles with very long PDFs due to context limits
• No collaborative features yet it's fully single-user right now

What's next

• Smarter deduplication using DOI matching
• Streaming responses for the paper chat (so it feels faster)
• A recommendation engine based on your saved papers
• Maybe: export to BibTeX / Zotero

Why I'm sharing this now

Mostly because building in public keeps me accountable. And because if you've felt the same tab-switching pain, I'd love to hear what features would actually matter to you.

Follow along if you're curious.

What's the most annoying part of your research or paper-reading workflow? Drop it in the comments.