AI Researchers

How AI researchers and developers use ATLAS

The AI Research Challenge

AI researchers face unique knowledge management challenges:

• Rapid field evolution — Papers, techniques, tools change weekly

• Cross-domain synthesis — ML meets neuroscience meets philosophy

• Implementation details — Code, configs, hyperparameters matter

• Reproducibility — Tracking what worked and why

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How ATLAS Helps

1. Paper & Research Tracking

Unify your reading across:

• arXiv papers and summaries

• Twitter ML threads

• GitHub implementations

• Conference notes

• Your own experiment logs

Example search:

"transformer attention mechanisms alternatives"
→ Your notes on linear attention papers
→ Twitter thread from researcher you follow
→ GitHub repo you starred
→ Your experiment notes comparing approaches

2. Concept Mapping

ATLAS builds your personal ML knowledge graph:

Transformer
├── Attention (self, cross, linear)
├── Architecture variants (GPT, BERT, T5)
├── Training techniques (warmup, gradient accumulation)
├── Your experiments (what you've tried)
└── Open questions (what you're exploring)

3. Experiment Memory

Never lose track of what you've tried:

• Hyperparameter configurations

• Ablation results

• Failed approaches (equally valuable)

• Insights from debugging

4. Literature Synthesis

Connect papers across time:

• How techniques evolved

• Who cites whom

• Your annotations and critiques

• Connections to your own work

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x402 Opportunities

AI research knowledge is highly valuable to:

Other Researchers

• Verified paper summaries

• Implementation gotchas

• Reproduction attempts

• Cross-paper synthesis

AI Agents

• Grounded technical queries

• Verified best practices

• Human-evaluated approaches

• Real experiment results

Industry Practitioners

• Academic-to-production translation

• State-of-the-art summaries

• Technique comparisons

• Implementation guidance

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Example Queries (x402)

Query	Tier	Value
"What's current SOTA for long-context transformers?"	Standard	Quick landscape
"Compare Mamba vs Transformer efficiency tradeoffs"	Deep	Verified analysis
"Production gotchas for RAG systems"	Premium	Battle-tested wisdom

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Workflow Integration

Daily Research

Morning:
1. ATLAS surfaces new papers matching your interests
2. Review and annotate relevant ones
3. Concepts auto-extracted and connected

During work:
4. Search ATLAS for related prior work
5. Surface your own notes on similar problems
6. Connect current work to prior insights

End of day:
7. Log experiment results
8. ATLAS extracts learnings automatically

Writing Papers

1. Search ATLAS for all related concepts
2. Surface your annotations on relevant papers
3. Find connections you've noted over time
4. Draft sections with your accumulated insight
5. ATLAS suggests citations from your library

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Getting Started

Connect Your Sources

1. arXiv — Paper abstracts and links

2. Twitter — ML researcher threads

3. GitHub — Starred ML repos

4. Notes — Your research notes and annotations

5. Experiments — Lab notebooks and logs

Build Your Graph

Focus on extracting:

• Techniques — Methods, architectures, approaches

• Results — Benchmarks, comparisons, findings

• Open questions — What's unsolved, what you're curious about

• Your opinions — Evaluations and critiques

Enable x402

If your research domain is valuable:

1. Configure knowledge exposure

2. Set appropriate pricing (research expertise is premium)

3. List in marketplace

4. Help others while generating revenue