Current context augmentation methods, such as retrieval-augmented generation, are essential for solving knowledge-intensive reasoning tasks.However, they typica...
In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling l...
Attributional inference, the ability to predict latent intentions behind observed actions, is a critical yet underexplored capability for large language models ...
Large Language Models (LLMs) struggle to reason over large-scale enterprise spreadsheets containing thousands of numeric rows, multiple linked sheets, and embed...
Automating Infrastructure-as-Code (IaC) is challenging, and large language models (LLMs) often produce incorrect configurations from natural language (NL). We p...
Artificial Intelligence (AI) systems have shown good success at classifying. However, the lack of explainability is a true and significant challenge, especially...
Reinforcement Learning (RL) remains a central optimisation framework in machine learning. Although RL agents can converge to optimal solutions, the definition o...
While quantum annealing (QA) has been developed for combinatorial optimization, practical QA devices operate at finite temperature and under noise, and their ou...
Dataflows were rightly? considered 'the slowest and least performant option' for ingesting data into Power BI/Microsoft Fabric. However, things are changing rap...
Neural Combinatorial Optimization (NCO) has mostly focused on learning policies, typically neural networks, that operate on a single candidate solution at a tim...
Evolving neural network architectures is a computationally demanding process. Traditional methods often require an extensive search through large architectural ...
Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. The post Under the Uzès Sun: When Historical Data Reveals the C...
