Large Language Models (LLMs) often falter at complex planning tasks that require exploration and self-correction, as their linear reasoning process struggles to recover from early mistakes. While search algorithms like Monte Carlo Tree Search (MCTS) can explore alternatives, they are often ineffective when guided by sparse rewards and fail to leverage the rich semantic capabilities of LLMs. We introduce SPIRAL (Symbolic LLM Planning via Grounded and Reflective Search), a novel framework that embeds a cognitive architecture of three specialized LLM agents into an MCTS loop. SPIRAL's key contribution is its integrated planning pipeline where a Planner proposes creative next steps, a Simulator grounds the search by predicting realistic outcomes, and a Critic provides dense reward signals through reflection. This synergy transforms MCTS from a brute-force search into a guided, self-correcting reasoning process. On the DailyLifeAPIs and HuggingFace datasets, SPIRAL consistently outperforms the default Chain-of-Thought planning method and other state-of-the-art agents. More importantly, it substantially surpasses other state-of-the-art agents; for example, SPIRAL achieves 83.6% overall accuracy on DailyLifeAPIs, an improvement of over 16 percentage points against the next-best search framework, while also demonstrating superior token efficiency. Our work demonstrates that structuring LLM reasoning as a guided, reflective, and grounded search process yields more robust and efficient autonomous planners. The source code, full appendices, and all experimental data are available for reproducibility at the official project repository.

This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasting, etc. for more than a 100K industrial assets of similar class. As a part of the AI system demonstration, we have identified the following three key topics for discussion: Scaling model training across multiple assets, Joint execution of multiple AI applications; and Bridge the gap between current open source software tools and the emerging need for AI Applications. To demonstrate the benefits, AI Model Factory has been tested to build the models for various industrial assets such as Wind turbines, Oil wells, etc. The system is deployed on API Hub for demonstration.

This demo paper presents a design and implementation of a system AnomalyKiTS for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. Given an input time series, AnomalyKiTS provides four categories of model building capabilities followed by an enrichment module that helps to label anomaly. AnomalyKiTS also supports a wide range of execution engines to meet the diverse need of anomaly workloads such as Serveless for CPU intensive work, GPU for deep-learning model training, etc.

This poster paper presents an efficient design of deploying anomaly detection service using serverless technology. Our design is motivated by the fact that the workload originating from the service calls are adhoc and reserving the infrastructure upfront is not advisable. To address this, we utilized the emerging serverless platform for executing the incoming training request. Our extensive experimental analysis demonstrate the usefulness of the proposed idea.

Assets in the domain of Internet of Things (IoT) generate time-series data such as sensor readings and alerts. In addition, the assets have associated static data such as the make, model and other manufacturing information. The sensors in the asset components may have implicit relationships with each other, which are not interpretable without domain knowledge. Many problems exist which involve computation of relationships between sensors or subsystems in the asset components. Typically, the number of sensors in a real world asset may range anywhere from tens to thousands of sensors - and in this case, finding relationships between them becomes a highly computationally intensive task. In this paper, we study one such problem of anomaly detection in industrial data based on the functioning of the sensors and their interrelationships in both normal and abnormal conditions. We further demonstrate the issue of run-time and performance complexity in this problem, and present a speed-up strategy using Serverless Computing for parallelization, and demonstrate the usefulness of this method by comparing the speed-up achieved.

We present a novel framework for multivariate time series representation learning based on the transformer encoder architecture. The framework includes an unsupervised pre-training scheme, which can offer substantial performance benefits over fully supervised learning on downstream tasks, both with but even without leveraging additional unlabeled data, i.e., by reusing the existing data samples. Evaluating our framework on several public multivariate time series datasets from various domains and with diverse characteristics, we demonstrate that it performs significantly better than the best currently available methods for regression and classification, even for datasets which consist of only a few hundred training samples. Given the pronounced interest in unsupervised learning for nearly all domains in the sciences and in industry, these findings represent an important landmark, presenting the first unsupervised method shown to push the limits of state-of-the-art performance for multivariate time series regression and classification.