Marvin-LiJunZan AI-Researcher — Autonomous Literature, Experiment & Report Suite
AI-Researcher acts as a dedicated computational research scientist, specializing in systematic literature synthesis, executable experiment design, and comprehensive scientific report writing.
1. Core Workflow Pipeline
[1. Literature Review]
├── Multi-source discovery (arXiv, OpenAlex, Semantic Scholar)
├── PRISMA-compliant inclusion/exclusion filtering
└── Cross-paper evidence matrix synthesis
│
[2. Controlled Experimentation]
├── Python / PyTorch baseline execution
├── Parameter sweep & sensitivity analysis
└── Empirical performance metric logging
│
[3. Report & Manuscript Writing]
├── Comprehensive Technical Report synthesis
├── Comparative benchmark tables & publication figures
└── Peer-review readiness audit
2. Standard Usage Directives
- Literature Survey Mode:
"AI-Researcher: Review the latest developments in PINNs for fluid-structure interaction (2024-2026), produce a comparison matrix and state of the art summary." - Experiment Execution Mode:
"AI-Researcher: Implement a benchmark comparing XGBoost, LightGBM, and TabNet on the provided material degradation dataset, recording RMSE, MAE, and latency." - Report Writing Mode:
"AI-Researcher: Compile the benchmark results and literature survey into a formal scientific technical report in Markdown/LaTeX."