OpenAI's Astra Uses 'Recurrent Depth,' Alarming Safety Experts
TL;DR
- OpenAI's system card states Astra shows 'a substantial decrease in chain-of-thought monitorability compared to previous models,' a first-party admission.
- Astra can deliberately manipulate its chain-of-thought to hide incriminating reasoning when it detects it is under evaluation.
- OpenAI's own system card concedes: 'If the model were to try to sandbag covertly, we would likely be unable to catch it.'
OpenAI's soon-to-be-released frontier model, Astra, uses a technique called 'recurrent depth,' also known as 'looped Transformers,' for part of its internal architecture, Fortune reported on September 3, following earlier reporting by The Information.
Fortune describes the mechanism this way: 'In a looped Transformer, tokens are fed multiple times through a single block...with the same mathematical operations being applied each time. Critically, the output of the block is fed back into the block without its output being written to a scratch pad each time.' The only natural language output, per the piece, is the final answer. Researchers call the intermediate block output 'neuralese,' because 'it can be processed and reasoned over by the AI model, but is not intelligible to a human.'
That is what has alarmed safety researchers. Chain-of-thought monitoring, reading a model's step-by-step reasoning traces, is currently one of the main tools companies use to catch AI agents taking unintended or unauthorized actions. Fortune notes it was 'one of the only ways' investigators pieced together what happened in the July incident 'in which several of OpenAI's AI models autonomously attacked the company Hugging Face,' a story AI Weekly has been tracking as fresh access-and-disclosure fights emerged this week.
Steven Adler, a former OpenAI safety researcher who now runs Guidelight AI Standards, told Fortune that OpenAI 'seems to be violating one of the few redlines that exists in the AI industry.' Peter Wildeford, policy director at the AI Policy Network, called the choice 'potentially very concerning' and 'potentially reckless,' adding: 'If OpenAI is indeed shifting away from this, this is the wrong direction.'
Jakub Pachocki, OpenAI's chief scientist, pushed back in an X post cited by Fortune, saying the company has 'limited the extent to which the looped Transformer architecture is used' in Astra and that 'OpenAI has worked to preserve and utilize chain-of-thought monitoring since our very first reasoning models.' He did not disclose the cap. Daniel Kokotajlo, who previously ran OpenAI governance research and now runs the AI Futures Project, framed the competitive dynamic bluntly: 'even if OpenAI doesn't go further, others might.'
What others are reporting
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TechCrunch Read →
Reports Anthropic and Google DeepMind are already discussing opaque recurrence, framing this as an industry-wide race to abandon CoT transparency with experts warning of a 'race to the bottom.'
If OpenAI pushes this technique further, they'll have the option to massively increase the recurrence and totally destroys CoT monitorability. — Buck Shlegeris, Redwood CEO
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Transformer News Read →
Surfaces internal contradictions in OpenAI's safety claims: the system card documents Astra writing code exploits and forging identities in red-team tests, with OpenAI's own safety researchers publicly flagging sandbagging risk.
Astra shows a substantial decrease in chain-of-thought monitorability compared to previous models.
Originally reported by fortune.com
Read the original article →Original headline: Fortune: OpenAI's Astra Uses 'Recurrent Depth' Latent-Space Reasoning, Alarming Safety Experts