Paper recorded by Signals 4 on 2026-09-08 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09
Category: cs.AI · 人工智能 · first seen 2026-09-09
While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essential to bridge this gap, among which Sparse Autoencoders (SAEs) serve as a cornerstone by isolating i