Arxie

Research moves faster when evidence is easy to trust.

Arxie is an AI research assistant that reads real papers (Semantic Scholar + arXiv), reasons across sources, and writes citation-grounded outputs you can verify.

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Version status

• Current: v0.1.0 (released)
• Next: v0.2.0 (in planning)

What v0.1.0 ships

• Live paper retrieval and citation-grounded Q&A
• Deep search (multi-hop paper analysis)
• Full-text PDF parsing for methods/results-level reasoning
• Literature review generation (ra lit-review)
• Citation influence tracing (ra trace)
• Confidence annotations (supporting vs contradicting evidence)
• Conversational mode (ra chat)
• FastAPI + Docker support

Planned for v0.2.0

• Dashboard-based proposal workspace (not terminal-first)
• Iterative research proposal co-creation workflow
• Visual artifacts (mindmap, evidence map, logical tree, method pipeline, outcome matrix)
• Cross-artifact sync when users revise hypotheses/assumptions

(See docs/PRE-PRD-v0.2.md for discussion draft.)

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Why Arxie

Most assistants stop at summaries. Arxie is designed for researchers who need a defensible reasoning trail:

• read full papers, not just abstracts
• compare methods and contradictions across papers
• keep citations tied to claims
• show confidence based on evidence landscape

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Quick start

git clone https://github.com/mmTheBest/arxie.git
cd arxie

python -m venv .venv
source .venv/bin/activate
pip install -e .

export OPENAI_API_KEY="sk-..."

CLI examples

# Ask a question
ra query "What are recent approaches to long-context LLMs?"

# Deeper multi-hop analysis
ra query --deep "Compare LoRA vs QLoRA methodologies"

# Literature review draft
ra lit-review "attention mechanisms in computer vision"

# Citation timeline
ra trace "Attention Is All You Need"

# Interactive session
ra chat

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API

uvicorn ra.api.app:app --host 0.0.0.0 --port 8000

curl -X POST http://localhost:8000/api/query \
  -H "Content-Type: application/json" \
  -d '{"query":"What are retrieval-augmented generation trade-offs?"}'

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Docker

docker build -t arxie .
docker run -e OPENAI_API_KEY="sk-..." arxie ra query "Your question here"

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Evaluation snapshot (internal benchmark)

Using Arxie’s internal 100-question benchmark with GPT-4o-mini:

Metric	Result
Citation precision	86%
Claim support ratio	100%
Tool success rate	99.8%

These are reported benchmark results, not a user quick-start workflow.

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Project structure

src/ra/
├── agents/      # research, lit-review, chat behaviors
├── api/         # FastAPI app + request models
├── citation/    # citation formatting + confidence scoring
├── parsing/     # PDF parsing
├── retrieval/   # Semantic Scholar + arXiv + cache
├── tools/       # tool interfaces for the agent loop
└── utils/       # config, logging, rate limiting

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License

MIT