Ukraine's Volodymyr Zelensky has been stripped of Poland's highest state honour, the Order of the White Eagle, over Kyiv's decision to name a military unit after controversial World War Two fighters. Polish President Karol Nawrocki branded Ukraine's decision late last month to name the unit after the Ukrainian Insurgent Army (UPA) outrageous, incomprehensible and deeply disappointing. Nawrocki stressed the diplomatic row would not impact Poland's support for Ukraine against Russia. Ukraine's Foreign Minister Andrii Sybiha denounced Warsaw's move, calling it a strategic mistake and disrespectful. Many in Ukraine regard the UPA, which existed in the 1940s and 1950s, as heroes who fought for Ukrainian independence against the Soviet Red Army as well as Nazi Germany and Polish authorities.

While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from three fundamental challenges: decomposition errors, where tasks are incorrectly broken down; retrieval missing, where key evidence fails to be retrieved; and reasoning errors, where flawed logic propagates through the reasoning chain. A single failure in any of these stages can derail the final answer. We propose Erasable Reinforcement Learning (ERL), a novel framework that transforms fragile reasoning into a robust process. ERL explicitly identifies faulty steps, erases them, and regenerates reasoning in place, preventing defective logic from propagating through the reasoning chain. This targeted correction mechanism turns brittle reasoning into a more resilient process. Models trained with ERL, termed ESearch, achieve substantial improvements on HotpotQA, MuSiQue, 2Wiki, and Bamboogle, with the 3B model achieving +8.48% EM and +11.56% F1, and the 7B model achieving +5.38% EM and +7.22% F1 over previous state-of-the-art(SOTA) results. These findings suggest that erasable reinforcement learning provides a powerful paradigm shift for robust multi-step reasoning in LLMs.

Historically, these systems included purely statistical models, but modern LLMs are deep artificial neural network s trained via machine learning . Once trained, an LLM may be implemented for various purposes, such as in chatbot s and personal assistants, or for translation, sentiment analysis and document review. 2 T he indisputably impressive performance of LLMs on a wide variety of task raises pressing questions about their capacities, and the mechanisms underlying those capacities . For instance, authors have grapple d with the questions of whether LLMs understand language (Bender & Koller, 2020; Mitchell & Krakauer, 2022) whether they possess concepts (Butlin, 2023) or to what extent they possess a theory of mind (Kosinski, 2024; Ullman 2023) . This paper focuses on the representational capacities of LLMs . D o LLMs rely on representations? If so, what do those representations represent? Much r esearch in AI -- for instance, studies using p robing classifiers (Belinkov, 2022), and methods for " e diting " models' representations (Hernandez et al., 202 4; Meng et al., 2022) -- assume s that a representational lens is appropriate . But a key question is whether LLMs can represent real world entities, or only "shallow" linguistic contents that don't reach into extra - linguistic reality (Butlin, 2021; Coelho Mollo & Millière, 2023; Yildirim & Paul, 2024) .

Whether it's Tinder or Hinge, anyone with an online dating account will know that choosing the perfect pictures and words for your profile is a tricky business. From candid photos to funny jokes, it can be difficult to know what will help you bag the likes in a sea of profiles. But help is at hand, as scientists have revealed the one word you definitely should not include on your profile. According to researchers from the University of Warsaw, the word'vegetarian' will immediately put off potential dates. In a new study, the team found that being a vegetarian makes both women and men less attractive as potential partners.

AI CAMERA SURVEILLANCE: The National Capital Region (NCR) is rolling out an advanced artificial intelligence-based visual recognition system that's taking air defense to a whole new level. THE FUTURE IS NOW: Autonomous, unmanned drones and artificial intelligence have already begun to shape the wars today and the future. Two US Air Force F-35 jets and a Polish Air Force F-16 take part in a military parade in Warsaw on Polish Army Day, August 15, 2023, to commemorate the anniversary of the 1920 victory over Soviet Russia at the Battle of Warsaw during the Polish-Soviet War. STAYING IN FIRST PLACE: As the U.S. races to maintain its global leadership in AI, much of the conversation revolves around natural language processing, the reshoring of the semiconductor supply chain and powering data centers. A visitor watches an AI (Artificial Intelligence) sign on an animated screen at the Mobile World Congress (MWC), the telecom industry's biggest annual gathering, in Barcelona.

The F-35 has had to develop a thick skin. From my former colleagues in Congress to defense-industry experts, the world's premier fighter jet is accustomed to criticism for issues with cost, production and more. In November, though, one of America's most influential voices decided to jump on the bandwagon: Elon Musk. Musk shared a video of a drone swarm with the caption, "Meanwhile, some idiots are still building manned fighter jets like the F-35," and he included a trash-can emoji for good measure. You can imagine the pleasant surprise of the men and women who build the F-35, as well as the brilliant men and women who pilot it, when a drone-like swarm of voices came to their defense.

Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RAG in understanding complex multi-hop query and retrieving relevant documents, which require LLMs to perform reasoning and retrieve step by step. Inspired by human's reasoning process in which they gradually search for the required information, it is natural to ask whether the LLMs could notice the missing information in each reasoning step. In this work, we first experimentally verified the ability of LLMs to extract information as well as to know the missing. Based on the above discovery, we propose a Missing Information Guided Retrieve-Extraction-Solving paradigm (MIGRES), where we leverage the identification of missing information to generate a targeted query that steers the subsequent knowledge retrieval. Besides, we design a sentence-level re-ranking filtering approach to filter the irrelevant content out from document, along with the information extraction capability of LLMs to extract useful information from cleaned-up documents, which in turn to bolster the overall efficacy of RAG. Extensive experiments conducted on multiple public datasets reveal the superiority of the proposed MIGRES method, and analytical experiments demonstrate the effectiveness of our proposed modules.

The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning a more profound analysis of slightly inferior models. Particularly in medical and healthcare studies, where the objective extends beyond predictions to valuable insight generation, relying solely on a single model can result in misleading or incomplete conclusions. This problem is particularly pertinent when dealing with a set of models known as $\textit{Rashomon set}$, with performance close to maximum one. Such a set can be numerous and may contain models describing the data in a different way, which calls for comprehensive analysis. This paper introduces a novel process to explore models in the Rashomon set, extending the conventional modeling approach. We propose the $\texttt{Rashomon_DETECT}$ algorithm to detect models with different behavior. It is based on recent developments in the eXplainable Artificial Intelligence (XAI) field. To quantify differences in variable effects among models, we introduce the Profile Disparity Index (PDI) based on measures from functional data analysis. To illustrate the effectiveness of our approach, we showcase its application in predicting survival among hemophagocytic lymphohistiocytosis (HLH) patients - a foundational case study. Additionally, we benchmark our approach on other medical data sets, demonstrating its versatility and utility in various contexts. If differently behaving models are detected in the Rashomon set, their combined analysis leads to more trustworthy conclusions, which is of vital importance for high-stakes applications such as medical applications.

PactumAI co-founder and CEO Martin Rand explains how workers can use artificial intelligence to enhance their careers and positions. Artificial intelligence leader OpenAI has once again updated its ChatGPT chatbot smartphone app, making improvements and minor bug fixes. Recent changes made at the end of last month expanded access to Custom Instructions to iOS devices. "Custom instructions now give you more control over ChatGPT's responses. Set your preferences once, and they'll steer future conversations. This feature is now available for Plus users and expanding to all users in the coming weeks," the update on July 28 noted.

Knowledge can take different forms. We distinguish between declarative knowledge (knowing something) or procedural knowledge (knowing how, know-how), sensorimotor knowledge (riding a bicycle), and affective knowledge (deep understanding). The classic definition of knowledge derived from philosophy defines knowledge as a justified true belief. It can be said to occur in situations where we consider something to be objectively "true" or "stated". Another definition refers to what is "explicit knowledge" that is something that is known and can be written down [30].