AryaXAI Research

Our research is dedicated to addressing fundamental challenges in making AI acceptable, safe, and aligned. We strive to ensure AI operates with certainty, consistently serving the core objectives of its users.

//

Featured

Featured 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

All 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

DLBacktrace: Model Agnostic Explainability For Any Deep Learning Models

November 20, 2024

No results found.

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.

Navigation

Home Careers Contact Us

Resources

All Resources Articles Research Papers

SOCIALS

Twitter LinkedIn

Get in touch

© 2025 Lexsi. All rights reserved.

Terms and Conditions

Payments and Refunds Policy