More than a trillion dollars are lost every year due to system failures. To resolve them, engineers must troubleshoot outages quickly. An important task in incident response involves analyzing observability metrics, or time series data that snapshot the health of software systems. For example, an engineer for a service may use Datadog to answer questions like "When did latency start increasing?" and "What metrics outside of latency are also behaving abnormally?" to localize the root cause of the anomalous behavior. These time series question-answering (TSQA) tasks are essential for engineers, and present challenging and necessary tasks for SRE models and agents to perform.

We utilize an open and widely used data format, i.e., JSON format, for the MEQA dataset. "context": "Roadside IED kills Russian major general [...]", # The context of the question "question": "Who died before AI-monitor reported it online?", "What event contains Al-Monitor is the communicator? "What event is after #1 has a victim? "Who died in the #2? major general,local commander,lieutenant general" We present a list of Datasheets [Gebru et al., 2021] for the MEQA dataset, synthesizing many of the For what purpose was the dataset created?

However, there's a lack of insight into how these models perform in terms

Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.

Reasoning over TKGs has two challenges: 1.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

Understanding the intricate motivations behind human actions is paramount in developing sophisticated systems capable of nuanced behavior analysis.

Despite this remarkable progress, there is a noticeable absence of datasets that combine electrocardiogram (ECG) data with question answering.

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.