Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-consuming, but identifying commonly-used or previously-seen functions can reduce the time it takes to understand a new file. However, given the complexity of assembly, and the NP-hard nature of determining function equivalence, this task is extremely difficult. Common approaches often use sophisticated disassembly and decompilation tools, graph analysis, and other expensive pre-processing steps to perform function similarity searches over some corpus. In this work, we identify a number of discrepancies between the current research environment and the underlying application need. To remedy this, we build a new benchmark, REFuSe-Bench, for binary function similarity detection consisting of high-quality datasets and tests that better reflect real-world use cases. In doing so, we address issues like data duplication and accurate labeling, experiment with real malware, and perform the first serious evaluation of ML binary function similarity models on Windows data. Our benchmark reveals that a new, simple baseline -- one which looks at only the raw bytes of a function, and requires no disassembly or other pre-processing --- is able to achieve state-of-the-art performance in multiple settings. Our findings challenge conventional assumptions that complex models with highly-engineered features are being used to their full potential, and demonstrate that simpler approaches can provide significant value.

As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks. We investigate the threat posed by undetectable backdoors, as defined in Goldwasser et al. [2022], in models developed by insidious external expert firms. When such backdoors exist, they allow the designer of the model to sell information on how to slightly perturb their input to change the outcome of the model. We develop a general strategy to plant backdoors to obfuscated neural networks, that satisfy the security properties of the celebrated notion of indistinguishability obfuscation. Applying obfuscation before releasing neural networks is a strategy that is well motivated to protect sensitive information of the external expert firm. Our method to plant backdoors ensures that even if the weights and architecture of the obfuscated model are accessible, the existence ofthe backdoor is still undetectable. Finally, we introduce the notion of undetectable backdoors to language models and extend our neural network backdoor attacks to such models based on the existence of steganographic functions.

'Phase-free' design builds disaster preparedness into everyday life Tadayuki Sato, representative director of the Phase Free Association, has introduced the phase-free concept in a bid to seamlessly integrate disaster preparedness with everyday life and business operations. A ball-point pen that can write on a wet piece of paper is an example of everyday goods that fit the phase-free concept. Fifteen years after the devastating March 2011 earthquake and tsunami, Japan is seeing growing momentum behind phase-free design, a new approach to disaster preparedness that integrates emergency functionality into everyday items. As major quakes have continued to strike various parts of Japan, Tadayuki Sato, representative director of the Phase Free Association, recognized the limitations of traditional disaster preparedness. Conventional approaches, led primarily by government bodies and focused on stockpiling specialized emergency supplies, were falling short. Around 2014, he introduced the phase-free concept in a bid to seamlessly integrate disaster preparedness with everyday life and business operations.

As the volume of data invested in statistical learning increases and concerns regarding privacy grow, the privacy leakage issue has drawn significant attention. Differential privacy has emerged as a widely accepted concept capable of mitigating privacy concerns, and numerous differentially private (DP) versions of machine learning algorithms have been developed. However, existing works on DP kernel learning algorithms have exhibited practical limitations, including scalability, restricted choice of kernels, or dependence on test data availability. We propose DP scalable kernel empirical risk minimization (ERM) algorithms and a DP kernel mean embedding (KME) release algorithm suitable for general kernels. Our approaches address the shortcomings of previous algorithms by employing Nyström methods, classical techniques in non-private scalable kernel learning. These methods provide data-dependent low-rank approximations of the kernel matrix for general kernels in a DP manner. We present excess empirical risk bounds and computational complexities for the scalable kernel DP ERM, KME algorithms, contrasting them with established methodologies. Furthermore, we develop a private data-generating algorithm capable of learning diverse kernel models. We conduct experiments to demonstrate the performance of our algorithms, comparing them with existing methods to highlight their superiority.

A powerful artificial intelligence model that appeared anonymously on a developer platform last week was revealed to be from Chinese smartphone and electric vehicle giant Xiaomi, and not DeepSeek as initially thought. BEIJING - A powerful artificial intelligence model that appeared anonymously on a developer platform last week was revealed on Wednesday to be from Chinese smartphone and electric vehicle giant Xiaomi, after it fueled speculation that startup DeepSeek was quietly testing its next-generation system ahead of a launch. The release of DeepSeek's low-cost models DeepSeek-V3 and R1 triggered a global tech stock selloff last year, causing investors to question whether U.S. AI firms needed to spend billions of dollars on AI computing power. Since then, there has been a great deal of interest in DeepSeek-V4, a next-generation model that has yet to be released. The mysterious free model, called Hunter Alpha, surfaced on the AI gateway platform OpenRouter on March 11 without any developer attribution and was later described by the platform as a "stealth model." In a time of both misinformation and too much information, quality journalism is more crucial than ever.

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools.

The Tokyo Metropolitan Government is developing a Generative AI Platform, which will allow government employees to create AI applications to assist with their work. The Tokyo Metropolitan Government and municipal governments throughout the Japanese capital are increasingly using generative artificial intelligence in their administrative operations. To support this trend, the metropolitan government is working with GovTech Tokyo, an affiliated organization that promotes digitalization in local governments, to develop a Generative AI Platform. The system will allow government employees to create generative AI applications tailored to their specific duties. By encouraging active use of the platform, Tokyo authorities aim to boost efficiency in public services and address growing concerns over labor shortages. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity and drug perturbation effects.

Since the conflict in the Middle East began on Feb. 28, Emirates has cancelled more than 2,000 flights -- 54% of scheduled services, according to data from Cirium. As Emirates flight EK10 from London cruised over Saudi Arabia on Monday, news broke of a drone strike at its destination, Dubai. The aircraft turned back to Gatwick, flight data shows, completing a 9,100 km round trip -- one of dozens of flights to nowhere triggered by the Middle East war. Roughly 30 Emirates flights heading to Dubai International Airport were also ordered back or rerouted after Iranian drone attacks temporarily shut what is normally the world's busiest airport for international passengers. Passengers expecting a dawn landing in the glitzy United Arab Emirates port city were stunned. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

NTT Global Data Centers is working on 34 projects to double its capacity to 4 gigawatts within as little as two years, CEO Doug Adams said, as it races to meet surging global demand driven by the AI boom. NTT Global Data Centers, the world's third-largest data center provider outside of China, is working to double its capacity to 4 gigawatts to meet the rising global demand for the critical digital infrastructure amid an artificial intelligence boom. The unit of Japan's NTT is working on 34 projects that will double its capacity in as soon as two years, according to the data center business's Chief Executive Officer Doug Adams. Capacity will continue to increase from there, and will be "well over 5 gigawatts" in five years, Adams said in an interview. NTT GDC has seen increasing demand from companies moving more of their software and operations to the cloud as well as businesses hunting for extra capacity to run AI programs. The business's revenue is expected to keep growing at more than 20% a year, Adams said, declining to give a specific time period.