New AI reasoning models, built as neural networks, show striking convergence
Why do we care when separate AI systems start to think alike? The new reasoning models—plain‑old artificial neural networks—are trained on data and a specific problem, then left to figure out how to solve it. While the tech is impressive on its own, researchers have begun to notice a pattern: models that differ in size, training set, or even underlying code often arrive at remarkably similar conclusions.
Here’s the thing: such alignment isn’t guaranteed. Neural nets can take wildly divergent paths, especially when tasked with complex reasoning. Yet, across recent experiments, the outputs have begun to line up, hinting at an underlying regularity in how these networks process information.
The cost of thinking, as the original title suggests, may be lower than we assumed—if the brain‑like machinery is converging on the same solutions without extra effort. That observation leads directly to the quote that follows.
"The fact that there's some convergence is really quite striking."
"The fact that there's some convergence is really quite striking." Reasoning models Like many forms of artificial intelligence, the new reasoning models are artificial neural networks: computational tools that learn how to process information when they are given data and a problem to solve. Artificial neural networks have been very successful at many of the tasks that the brain's own neural networks do well -- and in some cases, neuroscientists have discovered that those that perform best do share certain aspects of information processing in the brain. Still, some scientists argued that artificial intelligence was not ready to take on more sophisticated aspects of human intelligence.
"Up until recently, I was among the people saying, 'These models are really good at things like perception and language, but it's still going to be a long ways off until we have neural network models that can do reasoning," Fedorenko says. "Then these large reasoning models emerged and they seem to do much better at a lot of these thinking tasks, like solving math problems and writing pieces of computer code." Andrea Gregor de Varda, a K. Lisa Yang ICoN Center Fellow and a postdoc in Fedorenko's lab, explains that reasoning models work out problems step by step.
"At some point, people realized that models needed to have more space to perform the actual computations that are needed to solve complex problems," he says.
These newer reasoning models differ from earlier chat‑focused LLMs. While ChatGPT can draft an essay in seconds, it still stumbled on many math puzzles and multi‑step logic. The latest generation is being taught to pause, to work through a problem step by step, much like a person would.
They remain neural networks at their core, learning from data and explicit problem statements. Observers note a surprising convergence among distinct architectures, a point the researchers themselves called “quite striking.” Yet the extent of that convergence, and whether it will translate into consistently reliable performance across domains, is still unclear. The models’ ability to “think” longer suggests they may handle more intricate tasks, but early benchmarks show mixed results.
Critics point out that speed and fluency do not guarantee depth of understanding. In short, the field has moved beyond quick‑fire responses, but whether these reasoning models will consistently outperform their predecessors remains to be demonstrated.
Further Reading
- 100x faster AI reasoning with fewer parameters: New model threatens to change AI forever - Sify
- The cost of thinking - MIT News
- Hierarchical Reasoning Model - arXiv
- Living Through the Singularity? AI Convergence Report 2025 - Mindlace Synapse
- Hierarchical Reasoning Models: Thinking in Layers - Apolo.us
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Common Questions Answered
What does the article say about convergence among new AI reasoning models?
The article notes that despite differences in size, training data, or underlying code, the new reasoning models—plain‑old artificial neural networks—often arrive at remarkably similar conclusions. Researchers describe this alignment as "striking" and consider it an unexpected pattern across distinct architectures.
How do the newer reasoning models differ from earlier chat‑focused LLMs like ChatGPT?
According to the article, the newer models are trained to pause and work through problems step by step, unlike ChatGPT which can draft essays quickly but still struggles with many math puzzles and multi‑step logic. This shift emphasizes explicit problem statements and a more deliberate reasoning process.
Why is the step‑by‑step approach important for the latest generation of reasoning models?
The step‑by‑step approach helps the models emulate human-like problem solving, allowing them to break down complex tasks into manageable parts. This method improves performance on tasks that require multi‑stage reasoning, such as math puzzles, where earlier models often faltered.
What role do artificial neural networks play in the new reasoning models discussed in the article?
Artificial neural networks serve as the core computational framework for the new reasoning models, learning how to process information from data and explicit problem statements. Their success mirrors many brain functions, and they enable the observed convergence across varied model designs.