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Abstract
Semantic measure (SM) algorithms allow software to mimic the human ability of assessing the strength of the semantic relations between elements such as concepts, entities, words, or sentences. SM algorithms are typically evaluated by comparison against gold standard datasets built by human annotators. These datasets are composed of pairs of elements and an averaged numeric rating. Building such datasets usually requires asking human annotators to assign a numeric value to their perception of the strength of the semantic relation between two elements. Large language models (LLMs) have recently been successfully used to perform tasks which previously required human intervention, such as text summarization, essay writing, image description, image synthesis, question answering, and so on. In this paper, we present ongoing research on LLMs capabilities for semantic relations assessment. We queried several LLMs to rate the relationship of pairs of elements from existing semantic measures evaluation datasets, and measured the correlation between the results from the LLMs and gold standard datasets. Furthermore, we performed additional experiments to evaluate which other factors can influence LLMs performance in this task. We present and discuss the results obtained so far. © André Fernandes dos Santos and José Paulo Leal.

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Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.

Abstract
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that is currently unsolvable by any Machine Learning method. It demands strong generalization and reasoning capabilities, which are known to be weaknesses of Neural Network-based systems. In this work, we propose a Program synthesis system to solve ARC, Induce Logic Programs for Abstract Reasoning (ILPAR), which casts an ARC problem as a sequence of Inductive Logic Programming (ILP) problems. We have implemented a simple Domain Specific Language (DSL) that corresponds to a small set of object-centric abstractions relevant to ARC. This is Background Knowledge used by ILP to create abstract Logic Programs that provide reasoning capabilities to our system. When solving each ARC task, ILPAR can generalize from a few training examples: pairs of Input-Output grids. The Logic Programs are able to generate objects present in the Output grid and the combination of these can transform an Input grid into an Output grid. We randomly chose some tasks from ARC that do not require more than the small number of primitives we implemented in our DSL and showed that providing only this to ILPAR, it can solve tasks that require each different reasoning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.