This paper introduces a robust pipeline enabling Large Language Models (LLMs) to perform precise Table Question Answering (Table-QA) on complex statistical tables. Processing complex, visually oriented table formats remains a major challenge for large language models, as they exhibit inherent multi-dimensional and hierarchical structures. Despite their visual complexity, these formats adhere to strict structural conventions. Our approach leverages these regularities—such as specific cell border styles and hierarchical indentations—to transform these files into a unified JSON structure using a deterministic extraction method. Instead of generating direct answers, the LLM is fine-tuned to produce Python scripts that programmatically extract information from the generated JSON files. To achieve this, we generated a synthetic dataset comprising approximately 13,000 high-quality question-code pairs. Our fine-tuned Microsoft Phi-4 model achieves a functional success score of 88.5
The Ride-Hailing Problem is an online optimization challenge that coordinates a fleet of vehicles to serve requests revealed over time. The problem is NP-hard, and many solution approaches use stochastic or machine-learning based algorithms, which perform well but are difficult to interpret. We study the trade-offs between performance and explainability. Our results show that greedy assignment consistently outperforms more complex methods, indicating that explainability need not come at the cost of efficiency. We also find that the effectiveness of repositioning strategies strongly depends on demand patterns, highlighting the value of context-aware policies.
Professional ethics boards in fields such as engineering must identify applicable obligations when multiple provisions of the same ethical code generate competing requirements. In prior work we defined a nine-component case representation for professional ethics analysis. This paper presents the ontological implementation of that representation, grounded in the Basic Formal Ontology (BFO) and classified through the Information Artifact Ontology (IAO). Type-level disjointness is verified by the Pellet OWL-DL reasoner. Three object properties (competesWith, prevailsOver, defeasibleUnder) expose the competition structure between obligations as asserted, SPARQL-queryable relationships between typed individuals. OntServe, an ontology server, instantiates the representation as a knowledge graph of case ontologies extracted from a professional board of ethics archive.
This paper introduces Warehouse Logistics 2D (WaLo2D), an open-source environment designed for testing multi-agent reinforcement learning algorithms in automated warehouse logistics. WaLo2D is a configurable, low-compute simulation that models simple 2D warehouse tasks. This enables researchers and developers to quickly evaluate cooperative strategies for package storage, pick-up, and redistribution centre management. While more realistic 3D simulations are essential for final deployment, they often slow down early policy training due to the time-consuming manual crafting of details and high hardware requirements. In contrast, overly simplified models, such as discrete grid-based navigation, may not generalise well. WaLo2D bridges this gap by providing an accessible testbed that accelerates the initial evaluation of algorithms and tuning of reward functions, which is a critical advantage given the high number of repetitions required in multi-agent reinforcement learning experiments. It supports logistics scenarios, such as package routing, storage management and truck loading. The project is available at https://github.com/KIForAN/WaLo2D .
Linear recurrent neural networks (LRNNs) provide a simple yet powerful framework for modeling time series. Unlike traditional recurrent neural networks, LRNNs use linear activation functions and can be trained without backpropagation by solving linear equation systems. This paper summarizes their theoretical properties, learning procedure, and ability to approximate arbitrary time-dependent functions. A key contribution is a method for reducing network size using eigenvalue analysis, enabling both efficient learning and architecture optimization, which is nowadays an important topic. We demonstrate the properties and advantages of LRNNs by several case studies, among them multiple superimposed oscillators (MSO), robotic soccer (RoboCup), and stock price prediction. LRNNs outperform the previous state-of-the-art for the MSO task with a minimal number of units.
