mechanistic interpretability

Inside the Black Box: The Quest to Decode Neural Networks

Researchers are racing to understand what happens inside neural networks. Mechanistic interpretability could reshape how we build, audit, and trust AI systems — from deepfake detectors to video generators.

mechanistic interpretability

MINAR: Opening the Black Box of Neural Algorithmic Reasoning

New research introduces MINAR framework for understanding how neural networks learn to execute algorithms, advancing interpretability methods critical for AI safety and verification.

mechanistic interpretability

Mechanistic Tracing Reveals How LLMs Navigate Pain-Pleasure Decis

New research goes beyond behavioral analysis to trace the internal mechanisms LLMs use when weighing competing reward signals, offering insights into AI decision-making at the circuit level.

mechanistic interpretability

SALVE: New Technique Enables Mechanistic Control of Neural Networ

Researchers introduce SALVE, combining sparse autoencoders with latent vector editing for precise mechanistic control over neural network behaviors and outputs.

mechanistic interpretability

GPT-2 Dissected: How Transformer Layers Process Sentiment

New research reveals how GPT-2's layers divide labor between lexical and contextual processing during sentiment analysis, advancing our understanding of transformer internals.