The municipal company of Rio de Janeiro innovated on the open-source Quen 3.5 397B AI model by implementing a novel hybrid reasoning post-training method that combines explicit and latent reasoning, significantly improving the model’s speed and accuracy without heavy computational resources. This unexpected advancement highlights the potential for local governments and smaller entities to contribute meaningfully to AI development, emphasizing the importance of democratizing access to AI technology.
The video discusses a recent innovation on an open-source AI model, specifically the Quen 3.5 397B, which is known for its intelligence and performance. Surprisingly, this meaningful advancement was achieved not by a major tech company or renowned research institution, but by the municipal company of Rio de Janeiro’s city government. Despite being a local government entity, they managed to enhance the model’s benchmark performance significantly, showcasing an impressive feat in AI development.
The key innovation revolves around a novel post-training method based on a hybrid reasoning approach described in a research paper titled “Reasoning Switch Thinking in Latent and Explicit for Pareto Superior Reasoning LLM.” This method combines two types of reasoning: explicit reasoning, where the model verbalizes every thought step-by-step (similar to chain-of-thought prompting), and latent reasoning, where the model processes information internally without outputting intermediate steps. The hybrid approach allows the model to switch between these modes, optimizing both speed and accuracy.
To illustrate, the video uses a puzzle-solving metaphor: explicit reasoning is like solving a puzzle by speaking out loud every move, while latent reasoning is solving it silently in the mind before acting. The hybrid model can internally reason without verbose output but switch to explicit reasoning when necessary, resulting in faster and more accurate problem-solving. This approach reduces the time taken to answer complex queries compared to traditional chain-of-thought models, which can be slow and prone to overthinking.
The Rio de Janeiro municipal company applied this hybrid reasoning post-training to the Quen 3.5 397B model, achieving notable improvements without requiring heavy computational resources. This is particularly interesting because it demonstrates that significant AI advancements can come from unexpected places, including local governments, and that open science and open-source models enable broader participation in AI innovation. However, the video notes that the municipal company’s website lacks information about AI initiatives, suggesting this work might not be widely publicized.
Overall, the video highlights the importance of democratizing AI development and the potential for smaller or less expected entities to contribute meaningfully to the field. It also underscores the value of hybrid reasoning techniques in improving model efficiency and accuracy. The presenter expresses enthusiasm for seeing more such innovations globally, especially as access to powerful AI models becomes more restricted in some regions. This case serves as an inspiring example of local innovation impacting the broader AI landscape.
