What is a Palestinian without olives? In Gaza, the world has seen the cost of a diplomacy that claims to uphold a rules-based order but applies it selectively. The United States intervened late, and only to defend an occupation the International Court of Justice (ICJ) has ruled illegal. Alongside other Western nations that built multilateral institutions, the US increasingly pursues nationalist agendas that undermine them. The hypocrisy is stark: one set of rules for Ukraine, another for Gaza.

Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models, particularly in reference-based settings like Reinforcement Learning with Verifiable Rewards (RLVR). However, we uncover a critical vulnerability even in this reference-based paradigm: generative reward models are systematically susceptible to reward hacking. We find that superficial inputs, which we term ''master keys'' such as non-word symbols (e.g., '':'' or ''.'') or generic reasoning openers (e.g., ''Thought process:'' or ''Let's solve this problem step by step.''), can consistently elicit false positive rewards without any substantive reasoning. Our systematic evaluation demonstrates this is a widespread failure affecting a diverse range of models, including leading proprietary systems such as GPT-o1 and Claude-4. These results challenge the assumed robustness of LLM judges and pose a significant threat to their reliability. To address this, we propose a simple yet effective data augmentation strategy using truncated model outputs as adversarial negative examples. The resulting Master Reward Models (Master-RMs) demonstrate state-of-the-art robustness against these ''master key'' attacks while maintaining high performance in standard evaluation settings. We supplement these findings with a comprehensive analysis of the vulnerability across model scales, prompt variations, and common inference-time strategies, offering insights to guide future research on robust LLM evaluation. We release our robust, general-domain reward models and the synthetic training data at https://huggingface.co/sarosavo/Master-RM and https://huggingface.co/datasets/sarosavo/Master-RM.

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists.

Italian researchers have developed a method by which it's possible to bypass facial recognition ID checks for any user, in systems that have been trained on a Deep Neural Network (DNN). The approach works even for target users that enrolled into the system after the DNN was trained, and potentially enables the providers of end-to-end encrypted systems to unlock the data of any user via facial ID authentication, even in scenarios where that is not supposed to be possible. The paper, from the Department of Information Engineering and Mathematics at the University of Siena, outlines a possible compromising of user-encrypted facial ID verification systems by introducing'poisoned' facial images into the training data sets that power them. Once introduced into the training set, the owner of the poisoned face is able to unlock the account of any user through facial ID authentication. Images used in the'Master Key' system, to be included at the training phase.

Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models. Prevention of IP theft and unauthorized usage of such DNN models has been raised as of significant concern by industry. In this paper, we address the problem of preventing unauthorized usage of DNN models by proposing a generic and lightweight key-based model-locking scheme, which ensures that a locked model functions correctly only upon applying the correct secret key. The proposed scheme, known as Deep-Lock, utilizes S-Boxes with good security properties to encrypt each parameter of a trained DNN model with secret keys generated from a master key via a key scheduling algorithm. The resulting dense network of encrypted weights is found robust against model fine-tuning attacks. Finally, Deep-Lock does not require any intervention in the structure and training of the DNN models, making it applicable for all existing software and hardware implementations of DNN.

As we enter a new decade, we take with us the growing challenges we face in many fields, including artificial intelligence and conducting business while ensuring human rights. These hot topics are not going away any time soon. With the speed of innovation and technology, the responsibility of keeping up with development and regulating practices is all the more crucial to ensure a just world. Our upcoming winter academies on AI and international law, and due diligence as a key to responsible conduct, will empower you with the skills and knowledge you need to tackle those issues in your daily work. Winter academy on Artificial Intelligence and International law (20 – 24 January) 2020 will be a critical year to set the tone for the next decade of innovations in Artificial Intelligence (AI), one of the most complex technologies to monitor or regulate.

Just like any lock can be picked, any biometric scanner can be fooled. Researchers have shown for years that the popular fingerprint sensors used to guard smartphones can be tricked sometimes, using a lifted print or a person's digitized fingerprint data. But new findings from computer scientists at New York University's Tandon School of Engineering could raise the stakes significantly. The group has developed machine learning methods for generating fake fingerprints--called DeepMasterPrints--that not only dupe smartphone sensors, but can successfully masquerade as prints from numerous different people. Think of it as a skeleton key for fingerprint-protected devices.