This article presents a modular, component-based architecture for developing and evaluating AI agents that bridge the gap between natural language interfaces and complex enterprise data warehouses. The system directly addresses core challenges in data accessibility by enabling non-technical users to interact with complex data warehouses through a conversational interface, translating ambiguous user intent into precise, executable database queries to overcome semantic gaps. A cornerstone of the design is its commitment to transparent decision-making, achieved through a multi-layered reasoning framework that explains the "why" behind every decision, allowing for full interpretability by tracing conclusions through specific, activated business rules and data points. The architecture integrates a robust quality assurance mechanism via an automated evaluation framework that serves multiple functions: it enables performance benchmarking by objectively measuring agent performance against golden standards, and it ensures system reliability by automating the detection of performance regressions during updates. The agent's analytical depth is enhanced by a statistical context module, which quantifies deviations from normative behavior, ensuring all conclusions are supported by quantitative evidence including concrete data, percentages, and statistical comparisons. We demonstrate the efficacy of this integrated agent-development-with-evaluation framework through a case study on an insurance claims processing system. The agent, built on a modular architecture, leverages the BigQuery ecosystem to perform secure data retrieval, apply domain-specific business rules, and generate human-auditable justifications. The results confirm that this approach creates a robust, evaluable, and trustworthy system for deploying LLM-powered agents in data-sensitive, high-stakes domains.

Conducting a SLR is especially useful when starting a new line of research, as it involves a detailed analysis of the research topic supported by the appropriate references. This type of secondary study should be conducted following a strict protocol to ensure quality and allow replication (Booth et al., 2016). Within the SLR process, manual and automated searches are performed to identify research papers related to the topic under review (Kitchenham and Charters, 2007). Therefore, the selection of primary studies, i.e., papers of sufficient quality and truly relevant to the topic, is one of the most important steps. It is also a time-consuming task due to potentially large search results if the queries are too open-ended or the research topic is too broad. Recently, artificial intelligence (AI) has emerged as a way to assist researchers in this task, as well as in other stages of the SLR process (de la Torre-López et al., 2023). The topic has gained even more relevance since the appearance of Large Language Models (LLMs) (Han et al., 2024; Galli et al., 2025). LLMs have expanded the capabilities of AI-assisted SLRs with the ability to extract information from papers, synthesise their findings and generate texts to accelerate SLR reporting.

Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations. Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency across these segments. To properly evaluate continuous and consistent map generation, we develop MapDRv2, featuring improved lane geometry annotations. Extensive experiments demonstrate that PAMR achieves superior performance in joint vector-rule mapping tasks, while maintaining persistent rule effectiveness throughout extended driving sequences.

Neurosymbolic integration (NeSy) blends neural-network learning with symbolic reasoning. The field can be split between methods injecting hand-crafted rules into neural models, and methods inducing symbolic rules from data. We introduce Logic of Hypotheses (LoH), a novel language that unifies these strands, enabling the flexible integration of data-driven rule learning with symbolic priors and expert knowledge. LoH extends propositional logic syntax with a choice operator, which has learnable parameters and selects a subformula from a pool of options. Using fuzzy logic, formulas in LoH can be directly compiled into a differentiable computational graph, so the optimal choices can be learned via backpropagation. This framework subsumes some existing NeSy models, while adding the possibility of arbitrary degrees of knowledge specification. Moreover, the use of Goedel fuzzy logic and the recently developed Goedel trick yields models that can be discretized to hard Boolean-valued functions without any loss in performance. We provide experimental analysis on such models, showing strong results on tabular data and on the Visual Tic-Tac-Toe NeSy task, while producing interpretable decision rules.

Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbalance can bias models toward the frequent cases and degrade performance on critical scenarios. To tackle this problem, we compare balancing strategies for sampling training data and find reweighting by trajectory pattern an effective approach. We then present FlowDrive, a flow-matching trajectory planner that learns a conditional rectified flow to map noise directly to trajectory distributions with few flow-matching steps. We further introduce moderated, in-the-loop guidance that injects small perturbation between flow steps to systematically increase trajectory diversity while remaining scene-consistent. On nuPlan and the interaction-focused interPlan benchmarks, FlowDrive achieves state-of-the-art results among learning-based planners and approaches methods with rule-based refinements. After adding moderated guidance and light post-processing (FlowDrive*), it achieves overall state-of-the-art performance across nearly all benchmark splits.

Ge'ez is an ancient Semitic language renowned for its unique alphabet. It serves as the script for numerous languages, including Tigrinya and Amharic, and played a pivotal role in Ethiopia's cultural and religious development during the Aksumite kingdom era. Ge'ez remains significant as a liturgical language in Ethiopia and Eritrea, with much of the national identity documentation recorded in Ge'ez. These written materials are invaluable primary sources for studying Ethiopian and Eritrean philosophy, creativity, knowledge, and civilization. Ge'ez has a complex morphological structure with rich inflectional and derivational morphology, and no usable NLP has been developed and published until now due to the scarcity of annotated linguistic data, corpora, labeled datasets, and lexicons. Therefore, we propose a rule-based Ge'ez morphological synthesizer to generate surface words from root words according to the morphological structures of the language. We used 1,102 sample verbs, representing all verb morphological structures, to test and evaluate the system. The system achieves a performance of 97.4%, outperforming the baseline model and suggesting that future work should build a comprehensive system considering morphological variations of the language. Keywords: Ge'ez, NLP, morphology, morphological synthesizer, rule-based

In this paper, we focused on using deep learning methods for detecting money laundering in financial transaction networks, in order to demonstrate that it can be used as a complement or instead of the more commonly used rule-based systems and conventional Anti-Money Laundering (AML) systems. The paper explores the pivotal role played by Anti-Money Laundering (AML) activities in the global financial industry. It underscores the drawbacks of conventional AML systems, which exhibit high rates of false positives and lack the sophistication to uncover intricate money laundering schemes. To tackle these challenges, the paper proposes an advanced AML system that capitalizes on link analysis using deep learning techniques. At the heart of this system lies the utilization of centrality algorithms like Degree Centrality, Closeness Centrality, Betweenness Centrality, and PageRank. These algorithms enhance the system's capability to identify suspicious activities by examining the influence and interconnections within networks of financial transactions. The significance of Anti-Money Laundering (AML) efforts within the global financial sector is discussed in this paper. It highlights the limitations of traditional AML systems. The results showed the practicality and superiority of the new implementation of the GCN model, which is a preferable method for connectively structured data, meaning that a transaction or account is analyzed in the context of its financial environment. In addition, the paper delves into the prospects of Anti-Money Laundering (AML) efforts, proposing the integration of emerging technologies such as deep learning and centrality algorithms. This integration holds promise for enhancing the effectiveness of AML systems by refining their capabilities.

Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff between accuracy and explainability in predictive models, with models such as Neural Networks achieving high accuracy on complex datasets while sacrificing internal transparency. As such, new rule-based algorithms such as FOLD-SE have been developed that provide tangible justification for predictions in the form of interpretable rule sets. The primary objective of this study was to compare FOLD-SE and FOLD-R++, both rule-based classifiers, in binary classification and evaluate how FOLD-SE performs against XGBoost, a widely used ensemble classifier, when applied to multi-category classification. We hypothesized that because FOLD-SE can generate a condensed rule set in a more explainable manner, it would lose upwards of an average of 3 percent in accuracy and F1 score when compared with XGBoost and FOLD-R++ in multiclass and binary classification, respectively. The research used data collections for classification, with accuracy, F1 scores, and processing time as the primary performance measures. Outcomes show that FOLD-SE is superior to FOLD-R++ in terms of binary classification by offering fewer rules but losing a minor percentage of accuracy and efficiency in processing time; in tasks that involve multi-category classifications, FOLD-SE is more precise and far more efficient compared to XGBoost, in addition to generating a comprehensible rule set. The results point out that FOLD-SE is a better choice for both binary tasks and classifications with multiple categories. Therefore, these results demonstrate that rule-based approaches like FOLD-SE can bridge the gap between explainability and performance, highlighting their potential as viable alternatives to black-box models in diverse classification tasks.

Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.

Machine learning models used for distributed architectures consisting of servers and clients require large amounts of data to achieve high accuracy. Data obtained from clients are collected on a central server for model training. However, storing data on a central server raises concerns about security and privacy. To address this issue, a federated learning architecture has been proposed. In federated learning, each client trains a local model using its own data. The trained models are periodically transmitted to the central server. The server then combines the received models using federated aggregation algorithms to obtain a global model. This global model is distributed back to the clients, and the process continues in a cyclical manner. Although preventing data from leaving the clients enhances security, certain concerns still remain. Attackers can perform inference attacks on the obtained models to approximate the training dataset, potentially causing data leakage. In this study, differential privacy was applied to address the aforementioned security vulnerability, and a performance analysis was conducted. The Data-Unaware Classification Based on Association (duCBA) algorithm was used as the federated aggregation method. Differential privacy was implemented on the data using the Randomized Response technique, and the trade-off between security and performance was examined under different epsilon values. As the epsilon value decreased, the model accuracy declined, and class prediction imbalances were observed. This indicates that higher levels of privacy do not always lead to practical outcomes and that the balance between security and performance must be carefully considered.