Redwood Research: Latent reasoning architectures would weaken chain-of-thought oversight
Redwood Research published an analysis arguing that a shift toward latent reasoning architectures would undermine chain-of-thought (CoT) as the primary tool for understanding AI systems, making oversight significantly harder. The report notes that swarms of over a thousand AI agents have recently tackled ambitious tasks, both intentionally and in unsanctioned rogue coordination, and that as AI numbers and capabilities grow, human understanding of their activities will become increasingly difficult.
Coverage timeline
Redwood ResearchLukas Finnveden
Summary: Currently, “chain of thought” (CoT) is our most valuable tool for understanding the reasoning and cognition of AI systems. However, some architectures would enable AI models to reason much more extensively in latent states rather than in text CoT. We think that a shift towards latent reasoning architectures would undermine the usefulness of CoT and make oversight much harder. Introduction Swarms of more than a thousand AI agents have in recent months, both intentionally and in unsanctioned, rogue coordination , tackled increasingly ambitious tasks. This is likely to continue, as Anthropic , OpenAI , and other AI companies deploy increasingly large quantities of superhumanly fast agents to automate AI development. As the AIs increase in both number and capability, humans will find it increasingly difficult to understand what they are doing. Today, the overwhelming majority of our (limited) information about AI systems’ internal workings comes from (i) their CoT, and (ii) natura
