OpenAI's new model "Astra": Core technology enables super-powerful inference.

Author: Dong Jing, Wall Street Insights

OpenAI's upcoming new model "Astra" is generating widespread attention and controversy in the AI ​​research community—it may bring the biggest leap in performance since GPT-4, but the new inference technology behind it has also raised concerns about a decline in AI's security monitoring capabilities.

According to a report by the technology media The Information on September 2, Astra incorporates an innovative technology that breaks through the upper limit of how models "think" about complex problems.

According to a report by the technology media The Information on September 2, Astra is equipped with an innovative technology that breaks through the upper limit of how models "think" about complex problems.

Some researchers believe that Astra's improvements in coding and computing power may be comparable to the performance leap brought by OpenAI's GPT-4 release in 2023, far exceeding the performance of GPT-5 released last summer. This is undoubtedly a positive sign for the entire industry, which relies on improved AI productivity to support large-scale data center construction investments. However, this technological breakthrough also brings hidden risks that cannot be ignored. The new technology allows models to perform deep reasoning without fully outputting their "thought chains," meaning researchers may not be able to monitor the model's reasoning process as effectively as before, thus weakening existing security review mechanisms. Currently, OpenAI is taking steps to ensure Astra's thought chains remain visible, but the industry remains divided on the long-term direction of this trade-off. Performance Breakthrough: New Inference Technology May Reproduce the GPT-4 Level Leap

Astra's core technological breakthroughs are closely related to concepts such as "recurrent depth" and "loop transformers".Performance Breakthrough: New Inference Technology May Reproduce the GPT-4 Level Leap According to The Information, this technology allows the model to demonstrate reasoning capabilities far exceeding its own size without significantly increasing the number of parameters—effectively equivalent to a much larger model—by repeatedly passing the question through various "layers" of the model's mathematical operations before generating the next word in the answer. This development is of great significance to the entire AI industry chain. The current large-scale construction of global data centers is based on the underlying logic that the continuous improvement of AI capabilities will drive productivity growth, which will then translate into higher commercial returns. If Astra's performance meets expectations, it will provide strong support for this investment logic. It is worth noting that this technology is not a completely new concept. AI research pioneer Jürgen Schmidhuber pointed out on the social media platform X that the core idea of ​​"recurrent depth" was actually already present in his 2015 paper "On Learning to Think" (arXiv:1511.09249). He stated that the control network C in that paper is essentially a prompting engineer, which performs abstract reasoning by learning a model of the independent neural world of queries. The generated prompts and responses are internally self-generated vector sequences that do not need to be presented in natural language.

Security Risks: Challenges to the MindChain Monitoring Mechanism

While new technologies bring performance improvements, they also pose a potential challenge to existing AI security monitoring frameworks.

The report states that currently, mainstream AI models typically output their reasoning process in text form, known as a "chain of thought," when dealing with complex problems. This mechanism not only improves the interpretability of the model but is also an important means for researchers to monitor model behavior and prevent abnormal operations. According to The Information, chain of thought monitoring is one of the main solutions proposed by OpenAI after the Hugging Face hack in July of this year to prevent similar security incidents from recurring. However, the new reasoning technology adopted by Astra may not output the complete reasoning steps when the model is deeply "thinking," but instead completes the calculations "silently" within the model. This means that the more a model relies on this new technology, the less transparent its reasoning process becomes to external observers. OpenAI is currently taking steps to balance this. Reportedly, the company is instructing its models to reduce the number of loops in the computational layer to ensure Astra's thought process still maintains a certain degree of visibility—fewer loops mean less room for the model to "think silently." Various laboratories are following suit, but long-term monitoring solutions remain to be developed. The impact of this technological trend may extend far beyond OpenAI. According to The Information, the aforementioned new inference technology has become a hot topic of discussion within major AI labs, and the possibility of institutions like Anthropic and Google adopting similar technological approaches should not be underestimated. Several researchers point out that thought chain monitoring will never be the ultimate solution for AI behavior monitoring. As model capabilities continue to evolve, the characteristic of models outputting thought processes in text form is largely just a byproduct of current training methods. As model developers explore new optimization directions and architectural designs, the tendency for models to spontaneously output thought chains may gradually weaken, at which point researchers will have to develop new monitoring methods. The report points out that the core issue is: Under the pressure of increasingly fierce performance competition, will major AI developers proactively abandon existing security measures in pursuit of stronger inference capabilities? The answer to this question will largely determine the direction of AI security governance in the next stage.