Korbak, Balesni et al. (UK AISI, Apollo, METR, OpenAI, Anthropic, Google DeepMind and others) · arXiv · arxiv.org
Researchers from rival labs argue that reading a model's step-by-step reasoning is a rare chance to spot intent to misbehave, but one that training and design choices could easily lose.
Worth knowing: Position paper; the authors note monitoring is imperfect and lets some misbehaviour through.
Research paperAug 20, 2026Technical
Choi et al. (Transluce) · Transluce · transluce.org
Trains 'activation oracles', AI assistants that read a model's internal activity to predict its behavior, on models of up to 1.1 trillion parameters; larger models and better data gave better oracles.
Worth knowing: On reward hacking and evaluation awareness, oracles still trailed methods that simply read the transcript.
Research paperMay 21, 2024Technical
Templeton et al. (Anthropic) · Transformer Circuits Thread · transformer-circuits.pub
Found millions of internal 'features' in a production Claude model, many matching human concepts, including ones linked to deception, sycophancy and power-seeking; amplifying them changed behavior.
Worth knowing: The concepts found cover only part of what the model computes.