Model Understanding and Generative Alignment Laboratory (MUGA LAB)
Research Repository, Reference Hub, and Prompt Engineering Framework
Maintained by @lexmuga
https://lexmuga.github.io/mugalab
About MUGA LAB
MUGA LAB (Model Understanding and Generative Alignment Laboratory) explores the intersection of
mathematical interpretability, human-aligned generative systems, and reproducible AI research.
The lab bridges model understanding and alignment through rigorous, pedagogically oriented frameworks — integrating interpretability, calibration, and value-guided generative design.
Mission and Focus
Model Understanding
How can we interpret and explain model behavior transparently?
Research areas:
- Explainability and feature attribution
- Calibration and reliability analysis
- Uncertainty estimation (epistemic and aleatoric)
Generative Alignment
How can models remain ethically and contextually aligned with human intent?
Focus areas:
- Human-in-the-loop learning and feedback
- Value-sensitive generation and ethical constraints
- Reinforcement learning from human feedback (RLHF)
Research Axes
| Axis | Description |
|---|---|
| Explainability | Quantifying influence, relevance, and interpretive coherence |
| Alignment | Embedding human feedback and ethical priors |
| Uncertainty | Modeling predictive confidence via BayesFlow |
| Optimization | Adaptive hyperparameter search with Optuna and DEHB |
Research and Teaching Synergy
MUGA LAB serves both as a research collective and a teaching framework, designed to support:
- Reproducible ML experiments
- Explainable NLP and embeddings
- Pedagogical prompt engineering
- Interpretability-driven model analysis
Reference Works
Model Understanding and Generative Alignment (2025)
Foundational MUGA LAB document outlining the theory and pedagogy of interpretability–alignment synergy.
Read PDF →
Prompt Engineering and Pedagogical Design (forthcoming)
Research guide on structured prompt engineering for interpretive reasoning.
Active Course: Predictive Analytics for Text (AY 2025–2026)
A modular course exploring text vectorization, embeddings, and interpretability.
Includes synchronized Jupyter notebooks covering:
- Traditional and embedding-based vectorization
- Master split generation for reproducibility
- Feature analysis using SHAP
Tools and Frameworks
- Vectorization: TF–IDF · Word2Vec · FastText · GloVe
- Optimization: Optuna · DEHB
- Uncertainty Modeling: BayesFlow
- Interpretability: SHAP · ECE · Calibration Curves
All workflows adhere to the MUGA Reproducibility Framework, ensuring deterministic data splits, transparent preprocessing, and traceable feature generation.
Repository Structure
mugalab/
├── README.md
├── index.md
├── references/
│ └── 2025-model-understanding/
│ └── model_understanding.pdf
├── courses/
│ └── predictive_analytics_for_text/
│ ├── notebooks/
│ ├── helpers/
│ └── scripts/
├── assets/
│ └── images/
Research Directions
| Theme | Example Topics |
|---|---|
| Interpretable Generative Models | Explainable latent representations |
| Value-Integrated Learning | Aligning outputs with human feedback |
| Uncertainty in Prediction | Probabilistic calibration and BayesFlow |
| Evolutionary Optimization | Differential evolution with HyperBand (DEHB) |
Contact
MUGA LAB — mugalab.research@gmail.com
https://lexmuga.github.io/mugalab
Citation
If referencing this repository or lab framework:
MUGA LAB (2025). Model Understanding and Generative Alignment Laboratory — Research References and Prompt Engineering Framework.
https://lexmuga.github.io/mugalab
License
Released under the MIT License.
Educational and research reuse encouraged with attribution.
Migration Notice
This site is currently hosted under: https://lexmuga.github.io/mugalab
A transition to the organization-level domain
https://mugalab.github.io is planned for AY 2026–2027.
All internal links use relative paths to ensure seamless migration.
🧭 “Interpretability guides alignment — alignment grounds understanding.”
— MUGA LAB, 2025 Reference Series