Agent Research Plan - Academic Paper Analysis for General AI Audience

Overview

Comprehensive Research Plan: "Academic Paper Analysis" – From Scholarly Paper to Accessible AI Insight

Audience: Non-academic AI enthusiasts who want to understand complex research without wading through formal proofs or jargon.

Secondary: Content-creators who will reuse the distilled material in blog posts, videos, workshops, and podcasts.

Guiding Principles

Respect scholarly accuracy while aggressively translating into everyday language.
Emphasise "Why this matters for AI right now"—connect every concept to an intuitive use case (e.g., ChatGPT prompt-chains, autonomous agents, robotics planning).
Use multi-pass reading + layered outputs (one-sentence → one-paragraph → deep-dive) so material is reusable at different depth levels.
Prefer visuals, examples, and analogies wherever possible; list technical terms once in a glossary and avoid thereafter.

PHASE 0 – Logistics & Roles

Lead Analyst: owns overall synthesis
Annotation Bot: highlights definitions, equations, and diagrams in the PDF
Cross-Checker: verifies fidelity of translated claims
Visualizer: converts frameworks/algorithms into diagrams

Timeline: 5 work blocks (~1.5 h each). All deliverables live under target analysis folder.

PHASE 1 – Orientation Pass (30 min)

Skim PDF front-to-back—note section titles, figures, tables, bold/italic text
Record bibliographic metadata in BibTeX + plain text note: authors, year, venue, DOI, keywords
Draft a 3–5 bullet "Gut-sense Summary" capturing:
- What problem is addressed?
- What's the proposed solution?
- Why is this potentially important for AI systems?

Output file: 00-orientation.md

PHASE 2 – Structural Map (45 min)

Build an outline with page numbers: Intro, Related Work, Model, Experiments, Discussion, Future Work
Extract every heading/sub-heading into a markdown bullet list
Snap quick screenshots of EACH diagram/equation; store under /figures for later redrawing
Tag sections by information type: Concept (C), Method (M), Result (R), Limitation (L), Speculation (S)

Output file: 01-structure-map.md

PHASE 3 – Deep Extraction (2 h, 2 passes)

3A. Concept & Method Digest

For every (C) and (M) tag:

Copy verbatim definition/equation → paraphrase in ≤ 2 sentences plain English
Create "Why it matters" paragraph linking to real-world AI scenarios
Note prerequisite concepts
Flag "difficult jargon" for glossary

3B. Results & Implications Digest

For every (R): summarise experimental setup, metrics, key numbers → translate to qualitative takeaway
For (L) & (S): capture author-stated caveats + commentary on future work

Intermediate outputs:

concepts.csv (ID, term, formal def, plain def, page)
findings.md (bullet list of empirical claims, with context)

PHASE 4 – Synthesis for Non-Experts (1 h)

1. Layered Summary Documents

a) "Tweet-Length TL;DR" (≤ 280 chars) b) "One-pager Explainer" (~400 words, 3 bullets: Core Idea, How It Works, Why You Care) c) "5-min Read" blog draft with headings, analogies, diagrams

2. Glossary

15–25 terms, each ≤ 25 words, cross-linked to deeper notes

3. Visual Assets

Redraw main figure(s) using simple shapes; color-code hierarchy levels
Flowchart of reasoning process; PNG + source

4. FAQ (≥ 6 questions)

Address common questions and comparisons
Connect to familiar AI applications

PHASE 5 – Validation & Packaging (30 min)

Cross-Checker reviews paraphrases against original text for accuracy
Spot-check numbers in findings vs. PDF tables
Run "jargon-detox" (search for ≥ 3-syllable terms not in glossary)
Package all deliverables in organized folder structure

Package Structure:

Analysis Folder/
├── 00-orientation.md
├── 01-structure-map.md
├── concepts.csv
├── findings.md
├── glossary.md
├── blog-draft.md
├── tweet-tldr.txt
├── one-pager.md
├── figures/ (PNGs + sources)
└── meta-completed.json (timestamps, reviewers)

Optional Enhancements

Audio Summary: 2-minute scripted clip for podcasts
Interactive Flashcards: autopopulate Anki deck from concepts.csv
Relation Graph: link this paper to other related works in knowledge graph

Quality Checklist for Each Output

☐ Accuracy to paper? ☐ Plain-English readability (Flesch ≥ 60)? ☐ Concrete AI examples? ☐ Visual aid present? ☐ Links to further reading?

Implementation Notes

Use Task tool for parallel execution of phases
Create folder skeleton and meta-tracking from start
Maintain scholarly accuracy while prioritizing accessibility
Connect every concept to real-world AI applications
Use analogies and examples extensively

This plan ensures systematic movement from dense academic content to compelling, accurate insights that any AI enthusiast can grasp and share.

DannyMac180/Amp oracle research plan.md