Some seemingly simple sequences of multiplication and addition grow so quickly that they question the very foundations of mathematics. Did you hear the one about the man who invented chess and got himself executed? Legend has it that a man called Sessa, who lived in India long ago, developed the rules for the game and presented them to a king. The king was delighted and offered the man his pick of reward. Sessa asked for a supposedly humble quantity of rice.

Sexual activity in young people is on the decline, but why? And what's more, should we be worried about what this means for society and the future of the human race? The comedy film was released in 1973 with a largely youthful cast and one too many double entendres. Half a century later, that title seems more apt than ever, at least among the younger members of society. Over the past few decades, sex appears to have been on the decline among teenagers and young adults - but it's not just happening in Britain . In the US in 2010, 12 per cent of 18 to 29-year-olds reported not having had sex in the past year, according to the General Social Survey, a long-running sociological survey.

This limits the applicability and generalization of off-the-shelf models to diverse real-world applications.

A common framework consists in modeling the range of tasks the agent may encounter as a distribution over all possible tasks.

Participatory budgeting is a method of collectively understanding and addressing spending priorities where citizens vote on how a budget is spent, it is regularly run to improve the fairness of the distribution of public funds. Participatory budgeting requires voters to make decisions on projects which can lead to ``choice overload". A multi-agent reinforcement learning approach to decision support can make decision making easier for voters by identifying voting strategies that increase the winning proportion of their vote. This novel approach can also support policymakers by highlighting aspects of election design that enable fair compromise on projects. This paper presents a novel, ethically aligned approach to decision support using multi-agent deep reinforcement learning modelling. This paper introduces a novel use of a branching neural network architecture to overcome scalability challenges of multi-agent reinforcement learning in a decentralized way. Fair compromises are found through optimising voter actions towards greater representation of voter preferences in the winning set. Experimental evaluation with real-world participatory budgeting data reveals a pattern in fair compromise: that it is achievable through projects with smaller cost.

The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.