OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard.
How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards.
arXiv:2603. 20508v2 Announce Type: replace-cross Abstract: Reasoning language models (RLMs) and the intermediate chains of thought they emit play an increasingly central role in multi-agent setups such as inter-model monitoring or distillation into smaller models.
By Dani Roytburg, Shreya Sridhar, Daphne Ippolito
arXiv:2602. 13904v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning is fundamental to modern LLM architectures and represents a critical intervention point for AI safety.
By Manqing Liu, David Williams-King, Ida Caspary, Linh Le, Hannes Whittingham, Puria Radmard, Cameron Tice, Edward James Young
The paper proposes a user‑centric Chain‑of‑Thought (CoT) reasoning framework that structures LLM reasoning traces into self‑contained, verifiable steps using XML‑like tags. This design allows users to independently assess and correct the AI’s reasoning while preserving performance on mathematical reasoning tasks. User studies show that the approach improves perceived usefulness and ease of use compared to standard CoT.
By Philipp Schr\"oppel
arXiv:2607. 07229v1 Announce Type: new Abstract: Prior work has shown that chain-of-thought (CoT) reasoning is often unfaithful: a model's stated reasoning does not reliably reflect the process that produced its output.
By Silvia Santano