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Lexsi Labs: Building the Scientific Foundations for Safe & Aligned Intelligence

November 6, 2025

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning

November 6, 2025

Introducing TabTune: A unified library for inference and fine-tuning tabular foundational models.

November 6, 2025

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Latest Research Papers

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning

November 4, 2025

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models

November 4, 2025

Interpretability as Alignment: Making Internal Understanding a Design Principle

September 10, 2025

Interpretability-Aware Pruning for Efficient Medical Image Analysis

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods

February 18, 2025

Bridging the Gap in XAI—The Need for Reliable Metrics in Explainability and Compliance

February 12, 2025

Lexsi Labs is dedicated to building the foundations for Safe Superintelligence — uniting alignment theory, interpretability science, and agentic autonomy into one research continuum to make AI aligned, interpretable and fit for the future.

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