If chain of thought is a model "thinking aloud" to itself, then why does it express doubt, frustration or suspicion about the problems it's solving, sometimes for pages and pages of its scratchpad?

And what does chain of thought mean for AI safety?

We'll hear from Julian Schulz, a researcher who's studying encoded reasoning in large language models, about where the opportunities, risks and weirdness lie in chain of thought. Here are some links to his research:

• On a model jailbreaking its monitor: https://www.lesswrong.com/posts/szyZi5d4febZZSiq3/monitor-jailbreaking-evading-chain-of-thought-monitoring
• A roadmap for safety cases based on CoT: https://arxiv.org/html/2510.19476v1#S1
• His posts on Less Wrong: https://www.lesswrong.com/users/wuschel-schulz

Some of the other papers we discussed include:

• On the biology of a large language model: https://transformer-circuits.pub/2025/attribution-graphs/biology.html
• Monitoring reasoning models for misbehavior and the risks of promoting obfuscation: https://arxiv.org/pdf/2503.11926
• How steganography comes about: https://arxiv.org/pdf/2506.01926
• Assuring agent safety evals by analysing transcripts (with excerpts from weird monologues): https://www.alignmentforum.org/posts/e8nMZewwonifENQYB/assuring-agent-safety-evaluations-by-analysing-transcripts
• Stress-testing deliberative misalignment: https://www.apolloresearch.ai/research/stress-testing-deliberative-alignment-for-anti-scheming-training/
• And the "watchers" CoT snippet from the paper above:  https://www.antischeming.ai/snippets#using-non-standard-language