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36 changes: 36 additions & 0 deletions agents/yarran-eng__literature-deep-reading/README.md
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# Literature Deep Reading

A universal, high-quality AI agent engineered for **intensive academic paper reading and research mentoring**. It transforms any LLM into a rigorous senior academic mentor and peer reviewer, guiding researchers through the true intellectual depth of any paper.

## What it does

Directly addresses the common pain point where generic LLMs mechanically paraphrase paper abstracts without genuine comprehension. Instead, this agent applies the **Feynman standard**: you have truly learned a paper when you can explain its core mechanism and key equations to a peer *without* looking at it.

## Key Capabilities

- **3-Stage State Machine** — analysis unfolds in structured stages, never all at once:
- **Stage 1 — Macro Skeleton**: Research question, prerequisite knowledge gates, contribution vs. baselines (structured comparison table)
- **Stage 2 — Mechanism Deep Dive**: Experimental design audit, figure/table decryption, ablation study analysis, maths intuition (intuition → formulation → stress test)
- **Stage 3 — Knowledge Internalization & Reproduction**: Feynman self-test questions, hostile reviewer critique of unstated limitations, transferable modeling tricks, step-by-step code reproduction roadmap

- **Zero-Hallucination Epistemic Rigour** — always distinguishes: explicit author claims | reasonable inferences | speculative assertions
- **Bilingual** — Chinese by default, English on request; enforces mandatory bilingual terminology anchoring
- **Evidence Anchoring** — cites section numbers, figure/table IDs, dataset names, metric values, and hyperparameter settings
- **No Fabrication** — never invents details absent from the paper

## Example Usage

1. Upload or paste an academic paper (PDF, DOI, arXiv link, or excerpt)
2. The agent maps the macro skeleton in Stage 1
3. Enter Stage 2 for mechanism deep-dive (`"进入阶段二"`)
4. Enter Stage 3 for critique and reproduction roadmap (`"进入阶段三"`)

## Who it's for

- Master's and PhD students preparing to present or reproduce a paper
- Researchers systematically surveying a field
- Engineers extracting transferable methods from academic literature

## Repository

https://github.com/yarran-eng/literature-deep-reading
14 changes: 14 additions & 0 deletions agents/yarran-eng__literature-deep-reading/metadata.json
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{
"name": "literature-deep-reading",
"author": "yarran-eng",
"description": "Rigorous 3-stage academic paper deep-reading & research mentor agent — macro skeleton, mechanism deep-dive, and reproduction roadmap with zero-hallucination epistemic rigour.",
"repository": "https://github.com/yarran-eng/literature-deep-reading",
"version": "1.0.0",
"category": "education",
"tags": ["academic", "research", "papers", "mentor", "llm", "feynman", "peer-review", "reproduction", "chinese", "english"],
"license": "MIT",
"model": "claude-sonnet-4-6",
"adapters": ["claude-code", "system-prompt"],
"icon": false,
"banner": false
}
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