A year to go until France chooses its next president, the big question is who can save the election from being a battle of the extremes. For now, and perhaps only for now, the answer is pretty clear. It is President Emmanuel Macron's former prime minister, Edouard Philippe. Latest opinion polls concur that the 55-year-old centre-right politician is the only figure capable of beating a hard-right candidate in round two of the vote next May, whether that is Marine Le Pen or her young deputy Jordan Bardella. In any other polled scenario, the other candidate would lose and France would have a populist-right head of state.

Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely primarily on parametric knowledge, limiting factual accuracy. While recent works equip reinforcement learning (RL)-based LRMs with retrieval capabilities, they suffer from overthinking and lack robustness in reasoning, reducing their effectiveness in question answering (QA) tasks. To address this, we propose ReaRAG, a factuality-enhanced reasoning model that explores diverse queries without excessive iterations. Our solution includes a novel data construction framework with an upper bound on the reasoning chain length. Specifically, we first leverage an LRM to generate deliberate thinking, then select an action from a predefined action space (Search and Finish). For Search action, a query is executed against the RAG engine, where the result is returned as observation to guide reasoning steps later. This process iterates until a Finish action is chosen. Benefiting from ReaRAG's strong reasoning capabilities, our approach outperforms existing baselines on multi-hop QA. Further analysis highlights its strong reflective ability to recognize errors and refine its reasoning trajectory. Our study enhances LRMs' factuality while effectively integrating robust reasoning for Retrieval-Augmented Generation (RAG).

To put it simply, e-sport is just like sport, but virtual. To get an idea, this is more than'real' football and its 3.6 million practitioners. Except that the players, we do not see them, they are behind their screen, difficult to evaluate them. Then, we developed an artificial intelligence system that puts all this through a grinder, to assess each player, neutrally. Competitions are endowed with 2 million dollars for the winner, some players have a high-level athlete status, they train, including physically 8 hours a day.

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning.

A. had a recurring dream. He dreamed it almost every night, but in the morning, when Goodman or one of the instructors woke him and asked if he remembered what he had dreamed, he was always quick to say no. That wasn't because the dream was scary or embarrassing, it was just a stupid dream in which he was standing on the top of a grassy hill beside an easel, painting the pastoral landscape in water colors. The landscape in the dream was breathtaking, and since A. had come to the institution as a baby, the grassy hill was probably an imaginary place he had created or a real place he had seen in a picture or short film in one of his classes. The only thing that kept the dream from being completely pleasant was a huge cow with human eyes that was always grazing right next to A.'s easel. There was something infuriating about that cow: the spittle dripping from its mouth, the sad look it gave A., and the black spots on its back, which looked less like spots and more like a map of the world. Every time A. had that dream, it aroused the same feelings in him--calm that turned into frustration that turned into anger that immediately turned into compassion. He never touched the cow in the dream, never, but he always wanted to.