arXiv:2604.05090v1 Announce Type: new Abstract: Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space, yet the nature of this internal organization remains elusive. In this work, we investigate which linguistic properties – abstract language identity or surface-form cues – shape multilingual representations. Focusing on compact, distilled models where representational trade-offs are explicit, we analyze language-associated units in Llama-3.2-1B and Gemma-2-2B using the Language Activation Probability Entropy (LAPE) metric, and further […]

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arXiv:2604.07531v1 Announce Type: new Abstract: Young autistic adults may garner benefits through social media but also disproportionately experience privacy harms. Prior research found that these harms often stem from perceiving the affordances of social media differently than the general population, leading to unintentional risky behaviors and interactions with others. While educational interventions have been shown to increase social media privacy literacy for the general population, research has yet to focus on effective educational interventions for autistic young adults. […]

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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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arXiv:2604.06216v1 Announce Type: new Abstract: As LLM-powered chatbots are increasingly deployed in mental health services, detecting hallucinations and omissions has become critical for user safety. However, state-of-the-art LLM-as-a-judge methods often fail in high-risk healthcare contexts, where subtle errors can have serious consequences. We show that leading LLM judges achieve only 52% accuracy on mental health counseling data, with some hallucination detection approaches exhibiting near-zero recall. We identify the root cause as LLMs’ inability to capture nuanced linguistic and […]

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Multimodal machine learning, mimicking the human brain’s ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real-world deployments, however, the presence of modality is highly variable and unpredictable, causing the pre-trained models in suffering significant performance drops and fail to remain robust with dynamic missing modalities circumstances. In this paper, we present a novel Cyclic INformative Learning framework (CyIN) to bridge the […]

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arXiv:2603.08818v1 Announce Type: new Abstract: This manuscript presents the perspectives and reflections of two researchers who were not previously engaged in aging research, regarding the gaps and barriers related to interdisciplinary collaboration on HCI and Aging research. The manuscript has two sections. In the first section, the authors discuss their observations on the disconnect between the needs of aging populations and the design of emerging technologies. The second section delves into their personal journey of developing empathy and […]

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Movement Network Foundation is building a modular blockchain framework that integrates the security-focused Move language with Ethereum’s liquidity to enable safer smart contracts and scalable decentralized applications. By combining the Move VM, modular rollups, and decentralized governance tools like Snapshot and Tally, the project aims to create a high-throughput infrastructure for Web3 builders while advancing its broader mission of democratizing digital ownership.

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