arXiv:2601.15434v1 Announce Type: new Abstract: The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data, such as text, images, formulas, and tables. Conventional Retrieval Augmented Generation (RAG) methods often fall short in handling this complexity, resulting in subpar performance. We introduce ManuRAG, an innovative multi-modal RAG framework designed for manufacturing QA, incorporating specialized techniques to improve answer accuracy, reliability, and interpretability. To benchmark performance, we evaluate ManuRAG on […]

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arXiv:2506.13865v3 Announce Type: replace-cross Abstract: Variational quantum algorithms (VQAs) promise near-term quantum advantage, yet parametrized quantum states commonly built from the digital gate-based approach often suffer from scalability issues such as barren plateaus, where the loss landscape becomes flat. We study an analog VQA ans”atze composed of $M$ quenches of a disordered Ising chain, whose dynamics is native to several quantum simulation platforms. By tuning the disorder strength we place each quench in either a thermalized phase or […]

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arXiv:2603.12471v1 Announce Type: new Abstract: Effective personalized feedback is critical to students’ literacy development. Though LLM-powered tools now promise to automate such feedback at scale, LLMs are not language-neutral: they privilege standard academic English and reproduce social stereotypes, raising concerns about how “personalization” shapes the feedback students receive. We examine how four widely used LLMs (GPT-4o, GPT-3.5-turbo, Llama-3.3 70B, Llama-3.1 8B) adapt written feedback in response to student attributes. Using 600 eighth-grade persuasive essays from the PERSUADE dataset, […]

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Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although microbial communities provide a promising and more cost-effective alternative, reliable inference remains challenging because the available deep-sea dataset is extremely small ($n = 13$) relative to the microbial feature dimension ($p = 26$), making purely data-driven models highly prone to overfitting. To address this, we propose a knowledge-enhanced classification framework that incorporates an ecological knowledge graph as a […]

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arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing defenses mainly employ gradient perturbation techniques, e.g., noise injection or gradient pruning, to disrupt GIAs’ direct mapping from gradient space to token space. However, these methods often fall short due to the retention of semantics similarity across gradient, […]

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arXiv:2604.02457v1 Announce Type: new Abstract: Automatic license plate reader (ALPR) systems are widely deployed to identify and track vehicles. While prior work has demonstrated vulnerabilities in ALPR systems, far less attention has been paid to their legality and physical-world practicality. We investigate whether low-resourced threat actors can engineer a successful adversarial attack against a modern open-source ALPR system. We introduce the Street-legal Physical Adversarial Rim (SPAR), a physically realizable white-box attack against the popular ALPR system fast-alpr. SPAR […]

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When the independent Tunisian online media collective Nawaat announced that the government had suspended its activities for one month, the news landed like a punch in the gut for anyone who remembers what the Arab uprisings promised: dignity, democracy, and a free press. But Tunisia’s October 31 suspension of Nawaat—delivered quietly, without formal notice, and justified under Decree-Law 2011-88—is not just a bureaucratic decision. It’s a warning shot aimed at the very idea of independent civic life. The […]

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Key Highlights: Microsoft’s year-long partnership with OpenAI just took a massive turn today, as both the companies have announced an update to their long-running agreement. Under the updated terms, Microsoft retains a non-exclusive license to OpenAI’s models and products through 2032. At the same time, Microsoft will continue to be OpenAI’s primary cloud partner, which means that Azure is still expected to handle a large share of workloads over the next several years. OpenAI loosens Microsoft exclusivity, gains […]

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arXiv:2605.00011v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world applications increasingly require multiple machine learning tasks simultaneously training their models across a shared pool of devices. Naively applying single-FL optimization techniques in multi-FL systems results in suboptimal system performance, particularly due to device heterogeneity and resource inefficiency. To address such […]

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