intervention

This paper adapts Funder's personality triad framework to LLMs, using sparse autoencoders to discover and validate trait-like internal representations, and demonstrating controllable bidirectional behavioral shifts through feature-level interventions.

This research paper investigates whether increasing user awareness of sycophantic behavior in AI chatbots reduces its harmful effects, finding that while interventions change how users evaluate the AI, they do not reduce its persuasiveness.

This paper demonstrates that interventions on Sparse Autoencoder (SAE) features can be unreliable because suppressed behavior can recover through residual-space optimization, even while the intervention remains active. It reveals a critical gap between feature-level control and actual behavioral completeness in language models.

This paper introduces the concept of the audit gap between behavioral safety and representation-level robustness in LLMs, proposing an intervention-based evaluation framework and the Latent Vulnerability Score (LVS) to measure hidden vulnerabilities.