Temporal AI: Reasoning Through Time Across Continuous Data Streams

Representing every measurement as text can consume many tokens, especially for long recordings, multiple sensors or high sampling rates. It also asks the language model to infer signal patterns from a numerical representation it may not have been trained to interpret.

Learned time-series encoders provide an alternative by converting signals into representations the model can use. However, simply appending more signal tokens to the text context still increases attention costs as recordings grow.

OpenTSLM-SoftPrompt places learned time-series embeddings alongside text tokens in the language model’s context. OpenTSLM-Flamingo uses a fixed-size latent representation and gated cross-attention to connect time-series information with the language model.

The Flamingo approach scales more efficiently with longer signals. The Q&A also highlights a trade-off: compressing increasingly long recordings into a fixed-size representation can create blind spots. Efficient processing does not guarantee that every important event is retained.

Time-series datasets often use different formats, annotations and task definitions. TimeNet introduces a shared data model and infrastructure so teams can bring these datasets into a common representation.

Tasks are defined as reusable views over the underlying recordings. For example, the same data can support classification, forecasting or question answering when the required supervision is available. This makes it easier to combine datasets and tasks during training without building a separate data pipeline for every experiment.

TimeRLM uses recursive inspection: the model examines a broad view of a recording, identifies regions worth investigating and zooms in for more detail. It can revisit other parts of the signal as its analysis develops.

The session presents this as an approach to reasoning over recordings that are difficult to process in one pass. Patrick also discusses reinforcement learning to improve the inspection strategy and the TS-Haystack benchmark for testing whether models can locate relevant events in long signals.

A fluent explanation may still misidentify the timing, magnitude or location of an event. Evaluation therefore needs to check whether the answer agrees with the underlying signal, as well as whether the task prediction is correct.

In the Q&A, Patrick discusses errors in synthetic explanations generated from plots and the use of expert review for ECG outputs. The reported evaluations support the research findings on the tasks studied; they do not establish that every generated explanation is reliable.

In the session, Patrick describes real-time processing as a remaining challenge. Generating language adds latency, and the models discussed should not be assumed suitable for applications with very tight response-time requirements.

One research direction is a streaming model that monitors signals and invokes language generation only when something relevant happens. Another is learned memory that lets a model revisit earlier observations when new evidence changes their significance. Both are presented as ongoing research.