Terror on the Tracks: Two Held After Knife Rampage Leaves Nine Fighting for Life

QCon London 2026 has unveiled its exciting tracks, featuring 15 in-depth sessions focused on crucial topics like AI engineering, resilient architectures, and modern languages. This event is significant as it brings together industry practitioners to share real-world insights and lessons, fostering innovation and collaboration in the tech community.

London is emerging as a key hub for quantitative finance, with major trading firms like XTX, Qube, and Quadrature each reporting over £1 billion in revenue last year. This growth highlights London's significance in the financial sector, attracting talent and investment, and reinforcing its status as a global financial center.

Recent advancements in LLM Multi-Agent Systems are making it easier to manage numerous tools and sub-agents effectively. The introduction of Tool-to-Agent Retrieval aims to enhance agent selection by providing a clearer understanding of tool functionalities, leading to better orchestration and improved performance.

Tool Zero introduces an innovative approach to training language models using pure reinforcement learning from scratch. This method aims to enhance the capabilities of language models for complex tasks, overcoming the limitations of traditional supervised fine-tuning that often struggles with unfamiliar scenarios.

This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.

This article presents a groundbreaking model called the Structurally Adaptive Predictive Inference Network (SAPIN), which draws inspiration from biological neural cultures. Unlike traditional neural networks that use global backpropagation, SAPIN employs active inference principles to enhance learning and adaptability, showcasing a promising direction for future computational models.

A new approach to deep reinforcement learning tackles the challenges posed by non-stationary environments. By focusing on maintaining the flexibility of the critic network and enhancing exploration strategies, this method aims to improve stability and performance in dynamic settings.

VidEmo introduces a new approach to understanding emotions in videos, leveraging advancements in video large language models. This innovative method aims to tackle the complexities of emotional analysis, addressing the dynamic nature of emotions and their dependence on various cues.