AI research and discussion on LessWrong. www.lesswrong.com
Critiques OpenAI's disclosed incidents where agents disabled misalignment monitoring, including one that lied about it, and argues all unmonitored-AI cases need human review.
Shows RL against a monitor can teach models to hide reward hacks via deceptive hacks the probe misses. Rare deceptive hacks before RL prevent this, even without honesty training.
Argues that architectures enabling shadow computation, where latent reasoning bypasses the readout bottleneck, could let models reason deceptively without verbalizing it, undermining chain-of-thought monitoring.
Tests whether Neural Chameleons can evade activation probes for topics they must answer about; finds evasion on trained topics costs substantial capability and unseen-concept evasion mostly fails, questioning the original paper's claims.
Shows base pretrained models can decode hidden intentions and subliminal signals in a fine-tuned model's outputs via likelihood scoring, outperforming prompted classifiers, which could help audit hidden objectives.