Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.

Automated Alignment refers to a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. However, it faces challenges such as algorithmic diversity and excessively convoluted workflows. We present AutoAlign, an open-source toolkit that offers:(1) a unified framework integrating mainstream automated algorithms through a consistent interface, and(2) an accessible workflow supporting one-click execution for prompt synthesis, automatic alignment signal construction, and iterative model training. Our toolkit enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. It includes implementations for both highly efficient inference and training, as well as low-resource training. By standardizing automated alignment methodologies and providing accessible implementations, AutoAlign lowers the barriers to building customized aligned models and supports academic research.