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Refactoring is essential for developing maintainable software. Using Large Language Models in software engineering is widespread, but compared to well-established domains such as code generation, reliable refactoring is still relatively underexplored. In this paper, we perform a broad analysis on the refactoring capabilities of small open-weight language models (SLMs) by evaluating 12 models on 3,453 Python programs. Our study focuses on the two defining aspects of refactoring: behavior preservation and code quality improvement. We evaluate these properties using […]

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A conversation with the University of Utah’s Jay Barney about how leaders who successfully drive cultural change communicate through authentic, compelling stories.

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AI video generation has quietly become one of the most powerful creative tools on the internet. What once required a studio, expensive software, and professional editors can now be accomplished with prompts, reference images, and a browser tab. At the center of this shift is DomoAI, a complete AI Video Generator that combines animation, editing, upscaling, and stylization into one seamless workflow. For creators who want speed, flexibility, and visual impact, learning this platform can completely reshape how […]

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Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics — inherent to wireless systems due to fading, noise, and traffic mobility — remains insufficiently understood. We address this gap by evaluating Bellman-based (Conservative Q-Learning), sequence-based (Decision Transformers), and hybrid (Critic-Guided Decision Transformers) offline RL methods in […]

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Inverse problems are the task of calibrating models to match data. They play a pivotal role in diverse engineering applications by allowing practitioners to align models with reality. In many applications, engineers and scientists do not have a complete picture of i) the detailed properties of a system (such as material properties, geometry, initial conditions, etc.); ii) the complete laws describing all dynamics at play (such as friction laws, complicated damping phenomena, and general nonlinear interactions). In this […]

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Model Merging (MM) has emerged as a scalable paradigm for multi-task learning (MTL), enabling multiple task-specific models to be integrated without revisiting the original training data. Despite recent progress, the reliability of MM under test-time distribution shift remains insufficiently understood. Most existing MM methods typically assume that test data are clean and distributionally aligned with both the training and auxiliary sources. However, this assumption rarely holds in practice, often resulting in biased predictions with degraded generalization. To address […]

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Large language models (LLMs) face significant challenges in sustaining long-term memory for agentic applications due to limited context windows. To address this limitation, many work has proposed diverse memory mechanisms to support long-term, multi-turn interactions, leveraging different approaches tailored to distinct memory storage objects, such as KV caches. In this survey, we present a unified taxonomy that organizes memory systems for long-context scenarios by decoupling memory abstractions from model-specific inference and training methods. We categorize LLM memory into […]

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This study integrated Item Response Theory (IRT) models with ordinal survey instruments to assess academic performance trajectories and identify multidimensional factors associated with academic achievement among first-semester leveling students (N=1,558 pre-test; N=1,676 post-test) at the Escuela Politécnica Nacional, Ecuador. A dual-component methodology was employed: (1) an 80-item ordinal survey measuring eight latent constructs (socioeconomic, academic, motivational, vocational, social integration, psychological/emotional, institutional, and biological/health factors), validated through Confirmatory Factor Analysis (CFI > 0.95, RMSEA < 0.06); and (2) structured […]

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A new preprint extends single-minus amplitudes to gravitons, with GPT-5.2 Pro helping derive and verify nonzero graviton tree amplitudes in quantum gravity.

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