I Analyzed Every IEEE Top Paper from 2025 Using This Tool

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If you’ve ever tried to do “serious” literature review at scale, you know the pain: thousands of PDFs, inconsistent formatting, endless tabs, and a constant fear you’re missing the one paper that changes everything.

So I built a system to make academic research feel searchable, explorable, and useful again.

This is my Academic Paper Analysis & Generation System — a multi-layer RAG (Retrieval-Augmented Generation) pipeline that indexed and analyzed 5,634 IEEE Access papers from 2025, extracted 225,855 references, measured quality patterns across the entire corpus, and even generated draft papers (with citations) that you can refine with human-in-the-loop review.

This post explains what I built, what I learned from the dataset, and how you can use the workflow for faster (and more grounded) research.

What the system does 

This tool is designed to help with three jobs that usually take forever:

  1. Analyze a massive corpus (patterns, structure, writing norms, quality markers)

  2. Explore and answer questions across thousands of papers (RAG Q&A)

  3. Generate a draft paper using what the corpus actually looks like (structure + citations + iterative refinement)

The key idea is simple: instead of treating papers like static PDFs, treat the whole corpus like a queryable research database.

Dataset overview: the corpus I analyzed

Source: IEEE Access Journal (2025)

Total indexed papers: 5,634

Total extracted references: 225,855

Paper length distribution: 2,204 – 9,301 words (avg 6,630, median 6,085)

Section count: 1 – 23 sections per paper (avg 20.1)

References per paper: 15 – 80 (avg 42)

In-text citations: 20 – 590 (avg 137.5)

Average references section length: 1,981 words

Detailed corpus statistics

Metric

Minimum

Mean

Median

Maximum

Word Count

2,422

6,630

6,085

9,301

References Count

15

42

38

80

In-text Citations

20

137.5

107

590

References per 1k Words

3

6.5

6.5

12

Section Count

1

20.1

18

23

Avg Sentence Length

5.5

18.0

17.5

97.1

Figures per Paper

3

9

7

15

Tables per Paper

1

4

3

8

What stood out from the analysis

1) These papers are structurally

dense

Up to 23 sections in a single paper is normal in this dataset. The “shape” of IEEE-style writing is very consistent: deep methodology, heavy citation, lots of segmentation.

2) Citations are not “extra” — they’re a huge chunk of the paper

Across the dataset, references are ~30% of total word count on average. That’s wild, and it changes how you should write if you’re aiming for IEEE-style output.

3) Reproducibility is still a gap

Only 19.5% of papers include code/GitHub links. That’s one of the biggest “future work” signals if you care about research that can be validated and reused.

4) Most papers look “rigorous” on paper

  • 99% contain mathematical content

  • 94% include comparative analysis

  • 88% acknowledge limitations

  • 32% run ablation studies

That doesn’t mean every result is perfect — but it does mean IEEE Access has strong norms you can model.

Deep quality assessment (full corpus)

Metric Category

Corpus Findings

Mathematical Rigor

99% (5,577) contain mathematical content; avg 41.36 math indicators/paper; 91% include statistical testing

Reproducibility

19.5% (1,100) provide code/GitHub links; 47% report multiple experimental runs; 59% include error reporting (std, variance)

Research Standards

94% (5,313) include comparative analysis; 88% acknowledge limitations; 32% perform ablation studies

Content Richness

Avg 9 figures + 4 tables/paper; 4.94 unique performance metrics/paper; 29.34 dataset mentions/paper

Academic Writing

Flesch Reading Ease: 41.74 (college level); Grade level: 9.73; 82% make novelty claims; 58% claim SOTA

Citation network intelligence (why this matters)

Total references analyzed: 225,855

Citation density: 6.5 references per 1,000 words

Peak citation years: 2024 (30,293), then 2023, 2022

Citation velocity: 90% of references are from the last 15 years

Most influential works inside the corpus (by citation frequency):

  • “Attention Is All You Need” — 149

  • “Adam: A Method for Stochastic Optimization” — 140

  • “Deep Residual Learning” — 126

  • “Dropout…” — 111

  • “Batch Normalization” — 107

The workflow (from the video transcript)

Here’s the flow I demo in the video — the important part is this isn’t “chat with the internet.” It’s chat only with the dataset, grounded in the indexed papers.

Step 1: Ingest and normalize the papers

The system takes raw, messy content and normalizes it into something usable:

  • chunking large papers intelligently

  • preserving structure and context

  • extracting metadata (title, authors, year) so citations can be built later

Step 2: Embed into a vector database

Once chunked, everything becomes embeddings and gets stored in a vector DB (I used “Quadrant/Qdrant-style” vector storage in my setup).

That’s what unlocks semantic search — meaning-based retrieval, not just keywords.

Step 3: RAG Q&A across the corpus

You can ask questions like:

  • “What are the top research gaps across X?”

  • “What trends show up in AI + education papers?”

  • “What methods dominate this subfield?”

The system retrieves the strongest evidence chunks, then generates a response grounded in those chunks.

Step 4: Paper Explorer (themes + mapping)

This is the “landscape mode”:

  • enter a topic

  • get themes + influential papers

  • visualize connections between themes (my demo includes a 3D relationship map)

This is for when you’re trying to understand an area before reading 50 papers.

Step 5: Draft paper generation (with citations)

This is where it gets fun:

  • pick depth + style

  • choose how many papers to cite

  • generate a structured draft paper based on a template derived from corpus norms

Then I do a sanity check on citations and iterate.

Step 6: External reference integration (Semantic Scholar API)

IEEE can’t be the only source of truth. So the system can:

  • generate keywords from the corpus

  • pull external papers via API

  • integrate them into the draft without rewriting everything from scratch

Step 7: Refinement pass + quality scoring

The final stage runs a “self-critique” quality evaluation:

  • flags what’s too long (abstract, intro, etc.)

  • highlights missing elements (figures, tables, weak citations)

  • exports markdown + PDF

The output isn’t “publish-ready” (and it shouldn’t be). It’s a high-quality starting point that saves days of manual work.

IEEE-style word count guidelines (based on the corpus)

These are the practical writing targets I derived from the dataset:

Section

Target Words

% of Body

% of Total

Abstract

91

2.0%

1.4%

Introduction

548

12.0%

8.4%

Related Work

914

20.0%

14.0%

Methodology

1,142

25.0%

17.4%

Experiments

685

15.0%

10.5%

Results

685

15.0%

10.5%

Discussion

366

8.0%

5.6%

Conclusion

137

3.0%

2.1%

Body Total

4,569

100%

69.8%

References

1,981

–

30.2%

Total Article

6,550

–

100%

Key observations:

  • Introductions are basically universal (98.9% presence rate)

  • Methodology is the longest section on average

  • References are massive (~30% of total words)

Where this tool is genuinely useful

If you’re doing any of these, this workflow helps a lot:

  • mapping a new research area quickly

  • extracting research gaps and opportunities across a field

  • building a literature review foundation (with traceability)

  • drafting a paper structure that matches IEEE norms

  • reducing “blank page” time to near zero

What’s next (improvements I’m actively thinking about)

A few things I’m focused on next:

  • better citation verification + tighter grounding checks

  • stronger structure enforcement during generation (especially abstracts)

  • adding multi-source corpora to avoid single-publisher bias

  • making the Paper Explorer maps easier to interpret and export

Links

GitHub repo: https://github.com/roangws/IEEE