arXiv:2603.12403v1 Announce Type: new Abstract: We study a setting where a set of agents engage in pairwise exchanges of freely replicable goods (e.g., digital goods such as data), where two agents grant each other a copy of a good they possess in exchange for a good they lack. Such exchanges introduce a fundamental tension: while agents benefit from acquiring additional goods, they incur negative externalities when others do the same. This dynamic typically arises in real-world scenarios where […]

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Childhood anemia remains a major public health challenge in Nepal and is associated with impaired growth, cognition, and increased morbidity. Using World Health Organization hemoglobin thresholds, we defined anemia status for children aged 6-59 months and formulated a binary classification task by grouping all anemia severities as emph{anemic} versus emph{not anemic}. We analyzed Nepal Demographic and Health Survey (NDHS 2022) microdata comprising 1,855 children and initially considered 48 candidate features spanning demographic, socioeconomic, maternal, and child health characteristics. […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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Universal and Existential Quantification in Haskell In logic, there are two common quantifiers: the universal quantifier and the existential quantifier. You might recognize them as ∀forall∀ (for all) and ∃exists∃ (there exists). They are relevant to Haskellers as well, since both universal and existential quantification are possible in Haskell. In this article, we’ll cover both types of quantification. You’ll learn how to: Make universal quantification explicit with ExplicitForAll. Create a heterogeneous list with existential data types. Use existentially […]

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arXiv:2510.25753v2 Announce Type: replace Abstract: Pretrained Transformers demonstrate remarkable in-context learning (ICL) capabilities, enabling them to adapt to new tasks from demonstrations without parameter updates. However, theoretical studies often rely on simplified architectures (e.g., omitting MLPs), plain data models (e.g., linear regression with isotropic inputs), and single-source training, limiting their relevance to realistic settings. In this work, we study ICL in pretrained Transformers with nonlinear MLP heads on nonlinear tasks drawn from multiple data sources with heterogeneous input, […]

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arXiv:2603.10001v2 Announce Type: new Abstract: Large Language Models (LLMs) exhibit inequalities with respect to various cultural contexts. Most prominent open-weights models are trained on Global North data and show prejudicial behavior towards other cultures. Moreover, there is a notable lack of resources to detect biases in non-English languages, especially from Latin America (Latam), a continent containing various cultures, even though they share a common cultural ground. We propose to leverage the content of Wikipedia, the structure of the […]

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Most automated pricing systems are built to optimize. n Very few are built to survive failure. When a system manages 1,000,000+ SKUs and processes 500,000 daily price updates across multiple marketplaces, pricing stops being a growth feature. It becomes financial infrastructure. At that scale: A single incorrect price can erase 30% margin instantly A misinterpreted API response can affect thousands of SKUs within minutes A faulty rollout can create $15,000–$20,000 daily exposure per seller The problem is not […]

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arXiv:2603.16939v1 Announce Type: new Abstract: We address the Ambivalence/Hesitancy (A/H) Video Recognition Challenge at the 10th ABAW Competition (CVPR 2026). We propose a divergence-based multimodal fusion that explicitly measures cross-modal conflict between visual, audio, and textual channels. Visual features are encoded as Action Units (AUs) extracted via Py-Feat, audio via Wav2Vec 2.0, and text via BERT. Each modality is processed by a BiLSTM with attention pooling and projected into a shared embedding space. The fusion module computes pairwise […]

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