Lexsi Labs 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

All Research Papers

Research Paper

AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models

February 23, 2026

Research Paper

Beyond Uniform Credit: Causal Credit Assignment for Policy Optimization

February 13, 2026

Research Paper

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning

February 4, 2026

Research Paper

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

November 4, 2025

Research Paper

Interpretability as Alignment: Making Internal Understanding a Design Principle

September 10, 2025

Research Paper

Interpretability-Aware Pruning for Efficient Medical Image Analysis

July 29, 2025

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Lexsi Labs Research

Featured Papers

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning

C -ΔΘ: Circuit-Restricted Weight Arithmetic for Selective Refusal

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

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

Interpretability as Alignment: Making Internal Understanding a Design Principle

All Research Papers

AlignTune: Modular Toolkit for Post-Training Alignment of Large Language Models

Beyond Uniform Credit: Causal Credit Assignment for Policy Optimization

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning

C -ΔΘ: Circuit-Restricted Weight Arithmetic for Selective Refusal

Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning

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

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

Interpretability as Alignment: Making Internal Understanding a Design Principle

Interpretability-Aware Pruning for Efficient Medical Image Analysis