The Long and Short of It

Context is crucial to understanding any complex issue. This is nothing new, but it has been an especially hot topic in the world of artificial intelligence over the past few years with the rise in popularity of large language models (LLMs). With each new release, these models are offering up ever larger context windows, which allows users to provide the models with more background information along with their prompts. And that background information can make the difference between getting a good response and some wild hallucinations.

That’s the good news. The not so good news is that as context windows grow larger, the computational resources needed to run the algorithms also grows. And when discussions around the water cooler at the companies developing the latest and greatest LLMs keep coming back to standing up a nuclear reactor to power the data center, or dropping another ten billion dollars on GPUs, that is a pretty big concern.

All the data, none of the bloat

A pair of researchers at MIT has developed a new type of machine learning model that might be able to give us the benefits of lots of context without the pain of massive amounts of additional computations. Their development, called LinOSS, is a linear oscillatory state-space model that was modeled on observations of biological neural networks. It is capable of working with very long sequences of data in a way that is very computationally efficient.

LinOSS draws inspiration from forced harmonic oscillators — a concept from physics that is also observed in biological systems like the brain. Traditional state-space models are already known for their ability to handle long sequences better than many Transformer-based models, but they typically require restrictive mathematical conditions to remain stable over time. These constraints can limit a model’s expressive power and increase computational overhead.

The LinOSS algorithm breaks away from those limitations by leveraging a simplified, physics-based design that uses only a nonnegative diagonal matrix for its internal dynamics. This choice makes the model both more stable and significantly more efficient than previous methods. The researchers also introduced a novel discretization technique that preserves time-reversible dynamics, mimicking the symmetry found in natural systems.

Importantly, LinOSS has been rigorously proven to be a universal approximator, meaning that it can learn to mimic any continuous, causal relationship between inputs and outputs over time. So it is not just a more efficient model, it is also highly flexible and very powerful.

Is it really better?

In empirical tests, LinOSS consistently outperformed leading state-space models like Mamba, S5, and LRU, particularly in tasks involving sequences of extreme length — up to 50,000 data points or more. In some benchmarks, LinOSS was nearly twice as fast and accurate as Mamba and 2.5 times better than LRU.

The code has been made open source, and the researchers hope the broader AI community will build on their work to push the boundaries of efficient long-sequence modeling even further. As context windows continue to grow and the demand for smarter, faster AI increases, LinOSS might just be the kind of solution we need.