SPEX and ProxySPEX: Scalable Interaction Identification for LLMs

Jul 11, 2026

Berkeley AI Research has introduced SPEX and ProxySPEX, algorithms designed to identify critical interactions among features, data points, and model components in large language models. These methods address the challenge of exponential growth in potential interactions by using ablation-based attribution to measure influence at scale.

Why it matters: This work advances interpretability by enabling scalable detection of complex interactions, which is essential for understanding and ensuring the safety of large AI systems.

Full story at: Berkeley AI Research ↗