Foundation models excel at text, images, audio, and video, but lack temporal reasoning capabilities over time-series data streams that run the real world: vitals, prices, telemetry, grid loads, clickstreams, machine logs, business processes.

Time Series Language Models (TSLMs) are open foundation models, supporting time‑series as a native modality, next to text, letting users ask questions, get explanations, and recommendations, all in natural language.

The OpenTSLM White Paper released today demonstrates state-of-the-art temporal reasoning performance. Unlike prior approaches, the cross-attention architecture scales to long time-series remaining viable at scale.

The results:

- Sleep staging: 4.4× accuracy with a model 200× smaller (~880× efficiency)

- Activity recognition: ~6× accuracy with 200× smaller (~1,000× efficiency)

- ECG interpretation: ~2× accuracy with 200× smaller (~400× efficiency)

— first model to process 12-lead ECG signals and text simultaneously with chain-of-thought reasoning validated by cardiologists.

For the first time, foundation models can handle multiple time-series streams of varying lengths concurrently, integrate them with textual context, and produce interpretable explanations (verified by domain experts, clinicians).

This work is the result of a growing collaboration between researchers from Stanford, ETH Zurich, UIUC, University of St. Gallen, University of Washington, Google, and Amazon.

It points to the next foundation model frontier: temporal intelligence that unlocks proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.

OpenTSLM:https://www.opentslm.com/ Stanford Repo: https://github.com/StanfordBDHG/OpenTSLM

Wouldn't it be better to have the model write a script that calls a TS library and give it access to an interpreter to run it? That's how a human would do it. I'm not convinced of the need to bake this into the model. What can you do with native TS capability that you can't by tool calling?

This is very cool! From the paper, this technique seems to work well for question answering in time-series.

In medical AI, IMO, the most exciting work is detecting disease signals too subtle for humans—for example, estimating ejection fraction from an ECG (which cardiologists can’t do this, but algorithms can and have been tested in RCTs: https://www.nature.com/articles/s41591-021-01335-4 ).

Since OpenTSLM tokenizes time-series into an LLM embedding space, would that process prevent capturing such subtle signals? Or could the approach be extended to handle that use case?