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On 20 November 2025 in Ho Chi Minh City, TRAFFIC and the Vietnam Forestry Administration (VNFOREST) marked the successful completion of a five-year effort to help protect the Congo Basin rainforest — one of the world’s most important forest ecosystems.

The project brought together partners in Cameroon, the Republic of Congo, Vietnam and China to strengthen how timber is tracked and verified along its entire journey, from African forests to Asian factories.

At the closing event, 30 representatives from government, timber industry associations, research institutes, and civil society came together to reflect on Vietnam’s progress and agree on next steps to ensure the timber entering the country is legal, sustainable, and responsibly sourced.

 

Achievements and results for Vietnam

In Vietnam, the initiative primarily aimed to build on-the-ground expertise, training more than 300 forest rangers and customs officers across 47 provinces to identify illegal timber, understand sustainable forestry practices, and enforce Vietnamese and international timber laws.

Smart technology was also introduced by equipping customs officials with Xylorix Enforcer: a smartphone app that quickly identifies wood species in the field, making it harder for illegal timber to pass undetected through Vietnam’s borders.

These efforts created a nationwide network of trained inspectors, who now provide consistent screening across Vietnam’s entry points, significantly reducing illegal timber in the country’s manufacturing supply chain.

At the same time, the initiative promoted industry accountability by bringing together 100 timber companies to develop a voluntary Code of Conduct, a shared commitment to sourcing legal, sustainable wood, and keeping supply chains transparent.

As a result, 78 companies that demonstrated compliance with the Code of Conduct now have easier access to buyers in Europe, North America, and other markets where legal sourcing is increasingly required, turning responsible practices into a competitive advantage.

Mr. Nguyen Van Dien from VNFOREST commented:

“We’ve given our frontline officers the skills to tell legal timber from illegal timber. This not only protects our global forests but also makes Vietnamese wood products more trustworthy to international buyers, especially in strictly regulated markets like the European Union.”,

Mr. Tran Le Huy, Vice Chairman of the Gia Lai Forest Product Association (formerly of Binh Dinh province), noted:

Proving the legal origin of wood is now essential for competing in global markets. This initiative gave our businesses the knowledge and systems we need to show international buyers that our products meet their standards.”

Next steps

Looking ahead, partners agreed to move toward a second phase of the project to continue strengthening the legal, sustainable timber trade and global forest protection.

Priorities include continuing to train enforcement officers as regulations evolve, exploring new partnerships, and extending responsible sourcing practices to other sectors, including online timber sales.

Chen Hin Keong, TRAFFIC’s Senior Advisor on Forest Governance and Trade, concluded:

This project has shown that government and businesses working together, both within Vietnam and across borders, is essential for sustainable timber trade and protecting forests at their source. By strengthening how we verify and track timber across borders, we’ve enhanced Vietnam’s reputation as a reliable supplier in international markets.”

The two had just done something unusual for the moment: they built a small large-language model (LLM) trained not on massive internet data dumps, but on a tiny, carefully chosen set of high-quality conversations. And they taught it to improve itself using reinforcement learning (RL), a technique where a model learns the way a person or animal does: by making decisions, receiving feedback, and then refining behavior through rewards and penalties.

At the time, almost no one was doing this with language models. The only other group exploring RL for LLMs was DeepSeek, the Chinese OpenAI competitor that would later terrify Silicon Valley.

The two students, William Chen and Guan Wang, called their model OpenChat, and they open-sourced it on a whim.

To their shock, OpenChat blew up.

“It got very famous,” Chen told Fortune. Researchers at Berkeley and Stanford pulled the code, built on top of it, and began citing the work. In academic circles, it became one of the earliest examples of how a small model trained on good data, as opposed to more data, could punch above its weight.

Then it landed somewhere Chen never expected: Elon Musk’s inbox.

Musk sent an email through what, at the time, was his new company, xAI, which wanted to recruit the students in a multi-million dollar pay package, Chen says. It was the kind of offer young founders dreamed of. 

They hesitated. Then, they turned it down.

“We decided that large-language models have their limitations,” Chen said. “We want a new architecture that will overcome the structural limitation of [large-scale machine learning].”

Instead of taking the deal, they left the comfortable momentum of OpenChat behind and pursued something far more ambitious: a “brain-inspired” reasoning system they believed could outperform current AI models.

That decision would lead, two years later, to Sapient Intelligence — and to a model that outperformed some of the world’s biggest AI systems on tests of abstract reasoning. They are confident their model is going to be the first to achieve “AGI,” or “artificial general intelligence, the so-called holy grail in AI research where a machine’s intelligence can match or surpass that of a human in any cognitive task.

Between the two worlds of the arms race

Chen’s path to turning down Musk didn’t begin in Beijing, but in Bloomfield Hills, Michigan, and with a childhood obsession that drove his parents crazy.

“When I was young, I would break things apart and never put them back together,” he said. “That’s what got me started.”

Chan was born in China, raised partly in San Diego and Shenzhen, and eventually sent to attend Cranbrook Schools — a prestigious private boarding school in Michigan — around the time he met Wang, a boy his age who attended a different school but had an equally unusual obsession.

On the first day they met, the two fell into a long conversation about what Chen calls their “metagoals,” the ultimate purpose of their lives.

For Wang, that metagoal was AGI, long before the term became popular. He described it in high school as an “algorithm that solves any problem,” since the terminology didn’t exist yet. Chen’s metagoal was different but complementary: optimizing everything, from engineering problems to real-world systems.

“It was an instant alignment,” Chen said. 

Today, the two still ask every single person they hire what their metagoals are. 

Chen founded the school’s drone club, petitioned administrators to let students fly quadcopters on campus, and spent hours tinkering in robotics labs. The two were the kids who stayed late, broke hardware, and kept experimenting.

“It was a great time,” Chen said. 

When college admissions rolled around, Chen was accepted to Carnegie Mellon and Georgia Tech — the obvious, prestigious paths for a gifted robotics student. Wang, meanwhile, had been admitted to Tsinghua University, China’s elite engineering powerhouse, often described as “China’s MIT.”

Chen visited the Beijing campus, toured the labs, and made a decision few American high schoolers would: He followed Wang to Tsinghua. 

The transition wasn’t easy. The coursework was intense, and the two struggled, even flunking some classes.

“Most of the Chinese kids are really — I hate to be stereotypical — but they’re really good at studying,” Chen laughed. “They’re really sharp.”

Still, he was surprised by how supportive his professors were once they learned what he and Wang were building.

“They were like, ‘Hey, I know this thing you’re trying to make — it’s a very good thing. I actually believe in the concept of AGI,’” he said.

By then, nearly everyone in Tsinghua’s Brain Cognition and Brain-Inspired Intelligence Lab knew what the two undergraduates were attempting: a new approach to machine intelligence that challenged the dominant assumptions of the field.

A 3 a.m. breakthrough

It was at Tsinghua’s brain lab where they developed the Hierarchical Reasoning Model (HRM), the architecture they believe can surpass transformers entirely.

If OpenChat was their proof of concept, HRM was the moonshot they had been building towards. And the moment it proved itself came, appropriately, in the dead of night.

On a random early morning in June this year, at 3 a.m., Chen and Wang stared at the benchmark results returned by their small experimental model. Their tiny HRM prototype — just 27 million parameters, microscopic compared to GPT-4 or Claude — was outperforming systems from OpenAI, Anthropic, and DeepSeek on tasks designed specifically to measure reasoning.

It solved Sudoku-Extreme, found optimal passages through 30×30 mazes, and achieved startlingly high performance on the ARC-AGI benchmark — all without chain-of-thought prompting or brute-force scaling.

“It was crazy,” Chen said. “Just with a change in the architecture, it gave the model a lot of what we call reasoning depth.” 

Unlike a transformer, which predicts the next word based on statistical patterns, HRM uses a two-part recurrent structure modeled loosely on how the human brain mixes slow, deliberate thought with fast reflexive reactions. The system can plan, dissect problems, and reason using internal logic rather than imitation. “It’s not guessing,” Chen said. “It’s thinking.”

Chen says their models hallucinate far less than traditional LLMs and already match state-of-the-art performance in time-series forecasting tasks like weather prediction, quantitative trading, and medical monitoring.

They are now working on scaling HRM into a general-purpose reasoning engine, with a simple but radical thesis: that AGI won’t come from bigger transformers, but smaller, more efficient architecture. Today’s frontier models are massive — in some cases, hundreds of billions of parameters — but even their creators admit they struggle with reasoning, planning, and multi-step problem decomposition, Chen said. 

He believes that limitation is structural, not temporary.

“You can stack more layers,” he says. “But you’re still hitting the limits of a probability model.”

Sapient is now preparing to open a U.S. office within the next month, raise additional funding, and maybe change their name to begin deploying the second version of their model. The founders believe continuous learning — the ability for a model to absorb new experiences safely, without retraining from scratch — is the next major frontier. 

“AGI is the holy grail of AI” Chen says. And he expects it to emerge in the next decade. 

“One day, we’re going to have an AI that’s smarter than humans,” Chen said. “Guan and I always say it’s like Pandora’s box, if we’re not going to make it, someone else will. So we hope that we’re going to be the first one to make that happen.”