3 papers
cs.LG2026
RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?
Rohan Gupta, Erik Jenner
Latent-space monitors aim to detect undesirable behaviours in Large Language Models by leveraging their internal representations rather than relying solely on black-box outputs. Th…
cs.LG2025
InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
Rohan Gupta, Iván Arcuschin, Thomas Kwa +1
Mechanistic interpretability methods aim to identify the algorithm a neural network implements, but it is difficult to validate such methods when the true algorithm is unknown. Thi…
cs.LG2025
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe +20
How can we know whether new mechanistic interpretability methods achieve real improvements? In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretabili…