When ChatGPT first entered the mainstream, its appeal was mainly tied to its ability to answer questions and generate human-like text. But over time, the real breakthrough was how it became a health assistant for millions of people. People started relying on AI to understand symptoms, decode lab reports, summarize medical studies, and navigate complex healthcare terminology. Anthropic showed that Claude, which started as a general-purpose language model, can now tackle complex chemistry tasks, a world that used to […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Yield-bearing stablecoins represent one of the fastest-growing segments in the stablecoin market, merging the stability of digital cash with DeFi-native yield generation. From just $1 billion in total supply in 2023, the sector expanded beyond $19 billion by 2025 and accounted for more than half of the overall stablecoin market’s net supply growth in Q1 2026, contributing $4.3 billion out of an $8 billion quarterly increase as total market capitalization approached $315 billion. This article analyzes the market […]

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arXiv:2603.02366v1 Announce Type: new Abstract: Current AI writing tools, which rely on text prompts, poorly support the spatial and interactive nature of storytelling where ideas emerge from direct manipulation and play. We present PlayWrite, a mixed-reality system where users author stories by directly manipulating virtual characters and props. A multi-agent AI pipeline interprets these actions into Intent Frames -structured narrative beats visualized as rearrangeable story marbles on a timeline. A large language model then transforms the user’s assembled […]

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arXiv:2508.20614v2 Announce Type: replace Abstract: Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However, ABI methods are highly sensitive to model misspecification: when observed data fall outside the training distribution (generative scope of the statistical models), neural surrogates can behave unpredictably. This makes it a challenge in a model comparison setting, where multiple statistical models are considered, of which at least some are misspecified. […]

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Tether, the largest company in the digital asset industry, today announced the launch of the world’s first gold-backed Visa neobanking card in collaboration with Fasset, a stablecoin neobanking and investment platform that allows users to receive money, invest, earn, and make payments from anywhere in the world — marking a strategic step towards mainstream use cases for tokenized gold on a global scale. The card will operate on the Visa network, enabling users to spend USD at all […]

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arXiv:2603.25758v1 Announce Type: new Abstract: Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation learning. Diffusion Transformer (DiT) has recently gained attention as a promising alternative to conventional U-Net-based diffusion models, demonstrating a promising avenue for downstream discriminative tasks via generative pre-training. However, its current training efficiency and representational capacity remain largely constrained due to the inadequate timestep searching and insufficient exploitation of DiT-specific feature […]

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arXiv:2604.18729v1 Announce Type: new Abstract: Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose the social assumptions they have internalized from training data. In this paper, we investigate counterfactual unfairness through humor by observing how the model’s responses change when we swap who speaks and who is addressed while holding other factors constant. Our framework spans […]

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Offline reinforcement learning (RL) has garnered significant interest due to its safe and easily scalable paradigm. However, training under this paradigm presents its own challenge: the extrapolation error stemming from out-of-distribution (OOD) data. Existing methodologies have endeavored to address this issue through means like penalizing OOD Q-values or imposing similarity constraints on the learned policy and the behavior policy. Nonetheless, these approaches are often beset by limitations such as being overly conservative in utilizing OOD data, imprecise OOD […]

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arXiv:2601.17112v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language tasks but suffer from extremely large memory footprints and computational costs. In this paper, we introduce a tensor compression framework based on the cproduct for computing low rank approximation In the first part of our approach, we leverage the algebraic structure of the cproduct to represent weight tensors such as those in embedding layers, attention projections, and feed forward networks in […]

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