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for reviewers.

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for builders.

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Three complete
implementation walkthroughs

Each walkthrough mirrors DeepRead output format: architecture summary, implementation map, ambiguity notes, training recipe, and downloadable sample artifacts.

Transformer

Attention Is All You Need

Existing models for tasks like machine translation, which process sequences of words, faced two main challenges: they were either slow because they had to process words one after another, or they struggled to understand how distant words in a long sentence related to each other.

architecturecode maptraining recipe
Open Walkthrough
ResNet

Deep Residual Learning for Image Recognition

Before this paper, it was widely believed that making neural networks deeper would always improve their ability to understand complex data like images.

residual blocksoptimizationimplementation order
Open Walkthrough
BERT

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Previously, computer models designed to understand language could only process text in one direction, like reading a sentence from left to right.

pretrainingfine-tuningnlp pipeline
Open Walkthrough
The Problem

The gap no one
talks about

Every ML paper has two versions: the one that gets published, and the one you need to implement it. DeepRead bridges the distance between them.

What the paper gives you
-Equations with undefined symbols - Wq, dk, τ - no explanation of what they are
-Hyperparameters buried in footnotes, appendices, or omitted entirely
-"We use standard initialization" - which one? Xavier? Kaiming? They don't say.
-Architecture diagrams with no implementation consequence explained
-Citations to 5 other papers you now also have to read
-Training details split across the paper, appendix, and Table 3 footnote
What DeepRead gives you
+Every symbol decoded at the point of use - never left undefined
+Full hyperparameter table - paper-stated, inferred, or missing with agent default
+Every assumption labeled explicitly: ASSUMED with reason and consequence
+Figures interpreted by vision model - components, arrows, dimensions described
+Prerequisite concepts explained inline - problem solution paper-specific usage
+Training recipe synthesized from all paper sections into one clean document
How It Works

The agentic pipeline

Seven specialized agents work in sequence. Each one has a single job. The output of each feeds the next.

— DeepRead AGENTIC FLOW —
PDF UPLOAD User Paper or arXiv link SERVICE PDF Parser pymupdf fitz SERVICE Vision Service gemini-2.5-flash AGENT 1 Ingestion ParsedPaper DB figures + text + equations ParsedPaper AGENT 2 MOST CRITICAL Comprehension Agent Full paper text InternalRepresentation gemini-2.5-pro 1M context one call cached to DB component_graph ambiguity_log hyperparameters prerequisites InternalRepresentation LANGGRAPH PIPELINE — 6 NODES IN SEQUENCE SECTION 1 What It Does plain English flash SECTION 2 The Mechanism equations decoded pro SECTION 3 Prerequisites dependency order flash SECTION 4 Impl. Map labeled code pro SECTION 5 Left Out ambiguity report pro SECTION 6 Training Recipe hyperparams table flash DB write per section SSE STREAM thinking events + section_token events ThinkingStream.tsx renders live AGENT 3 Q&A QA Agent gemini-2.5-flash tools ConversationSummaryBufferMemory OUTPUT Briefing Document 6 sections streamed live renders as user reads AGENT 4 CODE Code Agent gemini-2.5-flash PyTorch ASSUMED/INFERRED labels OUTPUT Artifacts .md .py .csv downloadable on demand gemini-2.5-pro (deep reasoning) gemini-2.5-flash (speed) ambiguity / missing artifact output
What You Get

Six sections.
Everything you need.

The briefing is not a summary. It is the paper transformed - same information density, reorganized for implementation.

01 -
What This Paper Actually Does
One paragraph. No jargon. No prior ML knowledge assumed. Written for the version of you that hasn't read the paper yet.
plain english
02 -
The Mechanism
Every equation decoded inline. Every symbol defined at point of use. Every figure interpreted. Prerequisite concepts explained before they appear.
paper-stated inferred
03 -
What You Need To Already Know
Prerequisites in dependency order. Each one: the problem it solved, what it does, why this paper uses it specifically.
dependency ordered
04 -
The Full Implementation Map
Every component in build order. PyTorch snippets with inline equation citations. Every assumption labeled. Every inference explained.
paper-stated ASSUMED missing
05 -
What The Paper Left Out
Every ambiguity surfaced. Every missing hyperparameter flagged. Implementation consequence for every unresolved decision. You can override each one.
ambiguity report
06 -
How To Train It
Full training recipe synthesized from every section, footnote, and appendix. Hyperparameter table with source and status for every value.
hyperparams training recipe
Live Agent Activity

Watch it
think.

No progress bars. No fake loading states. A live stream of exactly what the agent is doing at the moment it's doing it.

>Reading abstract and identifying core contribution...
>Found Algorithm 1 block on page 6 - extracting pseudocode...
>Interpreting Figure 2 - encoder-decoder attention diagram...
>Found 6 undefined hyperparameters across appendix B and Table 3 footnote...
Ready

Stop reading papers.
Start implementing them.

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View Examples