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机器之心
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Global Race for RSI: Chinese Team Novix Achieves Scaled Closed Loop

The Chinese team Novix announced it has brought recursive self-improvement (RSI) to scaled operation through its Co-Evolutionary RSI mechanism, which embeds self-improvement into real frontier algorithm and engineering tasks while continuously absorbing expert feedback. In just six months, Novix has served 200,000 experts across more than 40 countries and over 500 universities and research institutions. Its core members come from Google, Microsoft, Meta, Zhipu, and Kimi, with prior involvement in projects such as Microsoft Copilot and Kimi K2.

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The Scaling Narrative of RSI: Novix's Self-Reported Scorecard and the Missing Verification Step

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A Machine Heart report claims that Novix, a Chinese team founded only half a year ago, has embedded "co-evolutionary RSI" into real frontier algorithm and engineering tasks and achieved scaled operation, and it provides specific results for three tasks. But all these numbers come from the team's own account and a single media relay, and the report provides no first-party technical documentation or third-party reproduction, so they can currently only be treated as claims awaiting verification, not confirmed capabilities.

  • The report claims Novix was founded only half a year ago and, through word of mouth, serves 200,000 experts, covering more than 40 countries and regions and over 500 universities and research institutions.
  • The report claims its core mechanism is "co-evolutionary RSI": AI proposes directions and executes verification, while experts continuously feed back problem value, evidence standards and evaluation rules.
  • The three public tasks involve model post-training, federal dam flood-discharge scheduling and prediction of cyclic response of steel under seismic loading, and the report gives specific scores.
Open section navigationA repeatedly cited premise: has RSI already moved from concept to scale?

A repeatedly cited premise: has RSI already moved from concept to scale?

The narrative starting point of this Machine Heart report is that on October 6, OpenAI publicly released 722 mathematics papers produced by AI, including a related proof of the quasi-Riemann hypothesis; the report also quotes former Google scientist and xAI co-founder Christian Szegedy as saying that the perilous peaks mathematicians spend a lifetime climbing can be reached directly by machine by airplane, and that mathematics is not coming to an end but rather changing its mode of exploration.

The report then raises a larger question: can this airplane continue flying toward broader scientific and engineering frontiers. The report's answer is that AI laboratories worldwide are turning their eyes to RSI (recursive self-improvement), letting AI not only complete tasks but also participate in the design, training and improvement of the next generation of AI, while most explorations remain at an early stage.

The report then gives its own core judgment: a Chinese team has already brought RSI into scaled operation. This judgment is the fulcrum of the entire article, and it is also the object this article needs to scrutinize—is it a verified fact, or a claim supported by the team's own account?

The mechanism Novix describes: turning expert judgment into system variables

The report claims that Novix was founded by Shi Yu, Tang Jiabin and others, with core members from Google, Microsoft, Meta, Zhipu and Kimi, who had led or participated in projects such as Microsoft Copilot, Kimi K2, AgentOS and AutoAgent. The report calls this mechanism Co-Evolutionary RSI, that is, co-evolutionary RSI.

According to the report's description, in this loop, AI proactively proposes directions, expands the solution space, executes tasks and verifies results; experts continuously feed back what important problems are, what evidence is sufficient for something to hold, what evaluation standards to adopt, and which directions match their own taste and long-term judgment. Every adoption, rejection, correction and feedback becomes a new goal and new standard for the next round of exploration, and further improves the system's task decomposition, tool use, harness and model capabilities.

The report quotes Tang Jiabin as saying: general models tend to propose approximately the same problem distribution, while human expert feedback is the seed variable that determines whether an RSI system is exploring the human unknown. The report also claims that the task distributions, judgment standards, failure records and exploration threads accumulated through collaboration are kept in dedicated spaces, serving only the corresponding users or organizations.

It should be pointed out that the above mechanism description all comes from the team's own account and media relay; the report gives no verifiable criteria for judgment, nor does it explain the substantive difference between "co-evolutionary RSI" and existing AI-assisted scientific research workflows. Therefore, claims at the mechanism level can currently only be regarded as the team's assertions.

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Credibility boundary

All factual content in this article comes from a single Machine Heart report (article_id 21389). That report is a media relay in nature, and its descriptions of Novix's scale, task results and mechanism are all the team's own account; the report provides no first-party technical documentation or third-party independent reproduction. The reserved attitude in this article toward the "scaled closed loop" and its pointing out of missing verification are inferences based on the report's own limitations, not externally verified conclusions. The matter of OpenAI publicly releasing 722 mathematics papers is only mentioned in passing in the original text, and its details and the reliability of the quasi-Riemann hypothesis proof cannot be verified from that report.

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