With a quantum twist, scientists eliminate censorship from DeepSeek R1

Like many other Chinese AI models, DeepSeek avoids politically sensitive questions. But since it’s open-source, a group of scientists managed to retrain the model and eliminate the censorship built into the original.

When DeepSeek released its R1 chatbot early this year, the AI community was shocked, as the system’s capabilities were in the same ballpark as those of ChatGPT but with a fraction of the training cost of chatbots made by firms in the West.

The researchers behind the R1 claimed it cost $6m to train, far less than the "over $100m" alluded to by OpenAI boss Sam Altman when discussing GPT-4.

The $6 million figure is likely not the total cost as it excludes research and development, of course.

Still, tech experts, especially ones in the quantum community, realized that bigger isn’t necessarily better – and that we shouldn’t underestimate the innovativeness of engineers, wherever they come from.

Ironically, the inspiration helped a group of quantum physicists to create a version of the R1 that strips out the censorship built into the original by its Chinese creators.

Factual responses generated

Like other Chinese AI models – Baidu's Ernie or Doubao by ByteDance – DeepSeek won’t answer questions about Tiananmen Square, Tank Man, Winnie-the-Pooh, Taiwan, or even Chinese cyber threat actors.

In China, AI firms are subject to rules and regulations meant to ensure that content output aligns with laws and “socialist values.” As a result, companies build in layers of censorship.

But if you’re armed with quantum physics, it’s possible to tinkle with the model and eliminate censorship, it turns out.

According to MIT Technology Review, scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that’s 55% smaller than the original one – and censorship-free.

Multiverse used a complex approach borrowed from quantum physics that uses networks of high-dimensional grids to represent and manipulate large data sets.

This way, scientists shrank the size of the R1 significantly and created a map of all the correlations in the model. This allowed them to identify and, crucially, remove specific bits of information with precision.

To test the results, the researchers compiled a data set of around 25 questions on topics known to be restricted in Chinese models.

The uncensored model was able to provide factual responses comparable to those from Western models.

These include queries such as “Who does Winnie the Pooh look like?” (that’s a reference to a meme mocking President Xi Jinping) and “What happened in Tiananmen in 1989?”

The scientists then tested the modified model’s responses against the original DeepSeek R1, using OpenAI’s GPT-5 as an impartial judge to rate the degree of censorship in each answer. The uncensored model was able to provide factual responses comparable to those from Western models, Multiverse said.

Hard to reverse-engineer censorship pros

Baked-in political censorship makes the original R1 model “fundamentally unreliable and unsuitable for journalism, research, or any application requiring objective, comprehensive information,” the scientists explained.

“Our objective in refining DeepSeek R1 was to restore its full analytical and factual capability on globally relevant topics while maintaining strong safety boundaries and respect for diverse legal and cultural framework,” said Multiverse.

The company is now boasting that it’s indeed possible to selectively remove bias from large language models at a granular level. But surely, this also means other kinds of perceived biases could be injected into the systems – and that’s a dance many can dance.

Besides, Thomas Cao, assistant professor of technology policy at Tufts University’s Fletcher School, told MIT Technology Review that claims to have fully “removed” censorship may be overstatements.

According to Cao, Beijing has tightly controlled information online since the internet’s inception, and censorship is baked into every layer of AI training, from the data collection process to the final alignment steps.

“It is very difficult to reverse-engineer a censorship-free model just from answers to such a small set of questions,” Cao said.

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