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India banned Telegram after the NEET paper leak led to a retest for 2.28 million students. Class Central studied the scam, the money trail, and other platforms the leaks could move to.
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Explore AI applications in electronic warfare, including real-time learning and evaluation techniques for enhancing battlefield responsiveness and effectiveness against complex emitters.
Explore hybrid quantum-classical computing systems, optimization workflows, and performance characterization for scalable quantum acceleration in scientific applications.
Discover systematic benchmarking methods for evaluating multimodal large language models in embodied AI environments and spatial cognition tasks.
Discover how to transform Agent-Based Models into probabilistic generative models to learn latent parameters and micro-level variables from data using maximum likelihood approaches.
Uncover how reporting bias in training data limits Vision-Language Models' reasoning capabilities, despite web-scale datasets, and explore intentional curation methods as solutions.
Explore how recommendation algorithms actually affect online content consumption through innovative "counterfactual bots" research, challenging assumptions about algorithmic radicalization.
Discover how deep learning models like FNO and U-Net accelerate predictions of viscous fingering, COâ‚‚ leakage, and ground deformation in subsurface fluid flow applications.
Explore how generative AI and Instagram shaped the 2024 US Presidential Election through computational analysis of 239,526 images and survey experiments on visual misinformation.
Discover VISCO, a benchmark analyzing how vision-language models critique and correct their visual reasoning, revealing key failure patterns and introducing the LookBack strategy for improvement.
Explore decades of cognitive architecture research through reflections on minds, intelligence, and the technologies underlying artificial and natural cognitive systems.
Explore how AI systems can better align with diverse human values through novel social choice theory approaches and ensemble-based reward functions that preserve preference diversity.
Discover algorithmic approaches for generating differentially private synthetic data with near-optimal utility guarantees, addressing high-dimensional challenges and streaming data applications.
Explore critical vulnerabilities in dense retrievers and learn to control Mixture-of-Experts LLMs through selective expert activation for improved AI system robustness.
Explore the dual nature of generative AI, examining its potential for good while addressing critical challenges in misinformation, disinformation campaigns, and digital safety.
Explore how Domain-Adaptive Programming enhances LLMs by integrating formal structures for robust, verifiable solutions in specialized domains like vision and robotics.
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