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title: README
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<div align="center">
<a href="https://lexsi.ai/">
<img src="https://raw.githubusercontent.com/Lexsi-Labs/TabTune/refs/heads/docs/assets/lexsilogowhite.png" width="600">
</a>
<br>
<a href="https://lexsi.ai/">https://www.lexsi.ai</a>
<br><br>
Paris 🇫🇷 · Mumbai 🇮🇳 · London 🇬🇧
<br><br>
<a href="https://discord.gg/dSB62Q7A" style="display:inline-block; vertical-align:middle;">
<img src="https://raw.githubusercontent.com/Lexsi-Labs/TabTune/refs/heads/docs/assets/discord.png" width="150">
</a>
<a href="https://github.com/Lexsi-Labs" style="display:inline-block; vertical-align:middle; margin-left:10px;">
<img src="https://raw.githubusercontent.com/Lexsi-Labs/TabTune/refs/heads/docs/assets/githublogo.png" width="150">
</a>
</div>
Lexsi Labs drives Aligned and Safe AI Frontier Research. Our goal is to build AI systems that are transparent, reliable, and value-aligned, combining interpretability, alignment, and governance to enable trustworthy intelligence at scale.
### Research Focus
- **Aligned & Safe AI:** Frameworks for self-monitoring, interpretable, and alignment-aware systems.
- **Explainability & Alignment:** Faithful, architecture-agnostic interpretability and value-aligned optimization across tabular, vision, and language models.
- **Safe Behaviour Control:** Techniques for fine-tuning, pruning, and behavioural steering in large models.
- **Risk & Governance:** Continuous monitoring, drift detection, and fairness auditing for responsible deployment.
- **Tabular & LLM Research:** Foundational work on tabular intelligence, in-context learning, and interpretable large language models.
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