AI Scientist
Autonomous research agent that transforms scientific papers into verifiable, machine-actionable knowledge. Accelerating discovery by automating the tedious parts of science.
Scientific knowledge is locked in human-optimized PDFs
Researchers waste enormous time on repeated work that could be automated:
Literature Triage
Hours spent reading papers to extract key claims and their supporting evidence
Reproduction Setup
Days configuring environments to reproduce published experiments
Citation Tracking
Manually building citation networks and tracking research lineage
Extension Discovery
Missing low-hanging fruit—easy improvements hiding in plain sight
Paper → Research Object → Verification → Extension
Claim–Evidence–Method–Artifact Schema
Papers are compiled into a machine-actionable format that preserves provenance while reducing context cost by 50%+ for downstream queries.
{
"paper_id": "arxiv:2405.21060",
"title": "Transformers are SSMs",
"claims": [
{
"id": "C1",
"statement": "Mamba-2 achieves 8× throughput",
"evidence": ["E1", "E2"],
"confidence": 0.95
}
],
"evidence": [
{
"id": "E1",
"type": "table",
"location": "Table 3",
"reproducible": true
}
],
"methods": [
{
"id": "M1",
"description": "Structured State Space Duality",
"artifacts": ["A1"]
}
],
"artifacts": [
{
"id": "A1",
"type": "code",
"url": "github.com/state-spaces/mamba",
"verified": true
}
]
}Run, Compare, Verify
Not just summaries—actual execution. When code and data are available, the system reproduces experiments in Docker containers and validates reported metrics.
Auto Environment Setup
Docker templates automatically configured from paper requirements
Metric Comparison
Reported vs reproduced numbers with ≤5% acceptable error threshold
Mismatch Analysis
When results differ, provides plausible causes and minimal fix suggestions
Cloud-Based Reproduction
In-silico experiments are automatically reproduced and verified on AWS infrastructure. From GPU instances to distributed computing, we restore the exact experimental environment from the paper.
Multi-Model Orchestration
Foundation Models as Sub-Agents
We orchestrate multiple frontier AI models as specialized sub-agents, each handling tasks best suited to their capabilities. A supervisor agent dynamically routes work and synthesizes results.
Agent Architecture
- • Supervisor/Planner Agent
- • Ingestion Agent (PDF/URL parsing)
- • Citation Agent (multi-source retrieval)
- • RO Compiler Agent
- • Verification Runner Agent
- • Critic/QA Agent
- • Human-in-the-Loop Gate
Infrastructure
- • APIs: arXiv, Crossref, Semantic Scholar, PubMed
- • Execution: Docker, Jupyter, pytest
- • Storage: JSON RO DB + Vector indices
- • Orchestration: LangGraph + Custom routing
- • Optional: RLVR for policy optimization
Target Metrics
Interested in AI Scientist?
We're actively developing this system. If you're a researcher interested in early access or collaboration, reach out.
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