AI Interpretability

The AI Interpretability Crisis: What Black-Box Models Cost Us

Modern AI systems achieve remarkable results but remain fundamentally opaque. The interpretability crisis threatens trust, safety, and accountability across all AI applications.

neural networks

SymTorch: Extracting Symbolic Math from Neural Networks

New framework converts opaque neural network decisions into interpretable mathematical expressions, enabling better model verification and understanding of AI behavior.

LLM research

Activation Steering: How Reasoning-Critical Neurons Improve LLM R

New research identifies specific neurons responsible for reasoning in LLMs and demonstrates how transferring their activation patterns can significantly improve inference reliability across models.

LLM research

Sparse Autoencoders Enable Fine-Grained Control of LLM Reasoning

New research demonstrates how Sparse Autoencoders can steer LLM reasoning processes, enabling precise control over chain-of-thought behavior without retraining models.