arXiv:2601.12256v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the development of large molecular language models (LMLMs) that integrate 1D molecular strings or 2D molecular graphs into the language models. However, existing LMLMs often suffer from hallucination and limited robustness, largely due to inadequate integration of diverse molecular modalities such as 1D sequences, […]

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Einstein argues that religion evolved from primitive fear to moral guidance, but its highest form is “cosmic religious feeling”—an awe at the universe’s sublime order, free from anthropomorphic gods or dogma. This same feeling, he contends, drives genuine scientific inquiry. Rather than opponents, science and religion share a common wellspring: wonder at existence itself.

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arXiv:2601.16382v1 Announce Type: new Abstract: While the filtered-x normalized least mean square (FxNLMS) algorithm is widely applied due to its simple structure and easy implementation for active noise control system, it faces two critical limitations: the fixed step-size causes a trade-off between convergence rate and steady-state residual error, and its performance deteriorates significantly in impulsive noise environments. To address the step-size constraint issue, we propose the switched mbox{step-size} FxNLMS (SSS-FxNLMS) algorithm. Specifically, we derive the mbox{mean-square} deviation (MSD) […]

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Explainable boosting machines (EBMs) are popular "glass-box" models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizations of each feature’s effect. However, unlike linear model coefficients, uncertainty quantification for the learned univariate functions requires computationally intensive bootstrapping, making it hard to know which features truly matter. We provide an alternative using recent advances in statistical inference for gradient boosting, deriving methods for statistical inference as well as end-to-end theoretical guarantees. Using a […]

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What are the different ways to use and finetune pretrained large language models (LLMs)? The three most common ways to use and finetune pretrained LLMs include a feature-based approach, in-context prompting, and updating a subset of the model parameters. First, most pretrained LLMs or language transformers can be utilized without the need for further finetuning. For instance, we can employ a feature-based method to train a new downstream model, such as a linear classifier, using embeddings generated by […]

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Google AI has released TranslateGemma, a suite of open machine translation models built on Gemma 3 and targeted at 55 languages. The family comes in 4B, 12B and 27B parameter sizes. It is designed to run across devices from mobile and edge hardware to laptops and a single H100 GPU or TPU instance in the cloud. TranslateGemma is not a separate architecture. It is Gemma 3 specialized for translation through a two stage post training pipeline. (1) supervised […]

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The sixth Information-Theoretic Cryptography (ITC) conference will be held at UC Santa Barbara, California, on August 16-17, 2025. The conference is affiliated with CRYPTO 2025, and will take place in the same location just before CRYPTO. Information-theoretic cryptography deals with the design and implementation of cryptographic protocols and primitives with unconditional security guarantees and the usage of information-theoretic tools and techniques in achieving other forms of security. The conference takes a broad interpretation of this theme and encourages submissions […]

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arXiv:2601.15385v1 Announce Type: new Abstract: Natural-language-to-visualization (NL2VIS) systems based on large language models (LLMs) have substantially improved the accessibility of data visualization. However, their further adoption is hindered by two coupled challenges: (i) the absence of standardized evaluation metrics makes it difficult to assess progress in the field and compare different approaches; and (ii) natural language descriptions are inherently underspecified, so multiple visualizations may be valid for the same query. To address these issues, we introduce VegaChat, a […]

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Hi, SHapley Additive exPlanations (SHAP) is a popular eXplainable Artificial Intelligence (XAI) method, popular among practitioners. I just discovered that if the covariates of an ML model are highly correlated, the SHAP values are influenced by this multicollinearity (please see the paper A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME). This means that although ML models (e.g., Random Forest) might be robust against multicollinear covariates, one must be very careful when explaining them using SHAP. So, […]

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