Paper Deep Read

The Problem

Reading a paper is hard not because the full text is missing, but because the useful details are scattered: the research gap, method components, experimental numbers, and ablation contributions are easy to miss. Formulas often lack symbol definitions, and tables containing datasets, baselines, and metrics can be reduced to vague summaries. This skill turns paper reading into a structured, reviewable workflow instead of producing only a high-level abstract.

How It Works

• Step 1: parse the PDF and assess quality. The script runs 5 checks: garbled text, formula quality, text misalignment, empty sections, and missing tables. It outputs quality.score in the range 0-100. A score of 70 or higher uses the extracted content; below 70, it enters a fallback chain and asks the user only if both fallbacks fail.
• Layer 1: Overview. It produces structured Markdown covering Research Gap, Target Problem, Method, Experiments, Ablation, and Conclusion. Experiments and ablations prioritize numbers, including datasets, baselines, key metrics, and component contributions.
• Layer 2: Method Detail. For each formula, it records the exact formula text, purpose, symbol table, intuition, connections to other formulas, and complexity when applicable. It also describes the overall architecture, data flow, training/inference pipeline, and hyperparameters.
• Layer 3: Innovation & Optimization. It lists evidence-backed strengths, weaknesses, implementable optimization opportunities, new research directions, and additional experiment ideas. If the user provides a research direction, the suggestions can be tailored accordingly.

The final output is a complete Markdown report. Layer 1 is presented inline first, while Layers 2 and 3 can be expanded on demand. If MCP tools are available, they may be used for knowledge-base search or complementary analysis.

Boundaries and Caveats

The analysis is performed by the agent itself; the Python package only handles PDF parsing and quality assessment. It fits research papers, technical reports, and formula-heavy experimental documents. If the PDF quality is poor, the language is mixed, or the paper contains many formulas (10+), it may present an overview first and then let the user choose what to explore deeper. Critical numbers, formulas, and citations should still be verified against the original PDF.