arXiv:2603.10024v1 Announce Type: new Abstract: LWM-Temporal is a new member of the Large Wireless Models (LWM) family that targets the spatiotemporal nature of wireless channels. Designed as a task-agnostic foundation model, LWM-Temporal learns universal channel embeddings that capture mobility-induced evolution and are reusable across various downstream tasks. To achieve this objective, LWM-Temporal operates in the angle-delay-time domain and introduces Sparse Spatio-Temporal Attention (SSTA), a propagation-aligned attention mechanism that restricts interactions to physically plausible neighborhoods, reducing attention complexity by […]

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When I was looking for K-means use cases, I found out about Color quantization, a very interesting . I implemented it in Python and was wondering whether it would be as easy to implement in ML.NET. All the code is available in this GitHub repository. What is color quantization Color quantization is the usage of quantization, a lossy compression technique, in color spaces in order to reduce the number of unique colors in an image.

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arXiv:2604.05001v1 Announce Type: new Abstract: This paper proposes a functional foundation for model driven engineering that unifies model construction, metamodels, templates, and transformations under a single formalism: the model expression algebra. In this algebra, models are values, model expressions are terms, and evaluation is the interpretation homomorphism from terms to values. Model expressions are composed from four operators: model creation and element creation operators, reference operators for retrieving models and elements, and computation operators that embed functional computations. […]

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arXiv:2604.15468v1 Announce Type: new Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engineering. Foundation models get stronger, agents can plan and act across multiple steps, and tasks such as scaffolding, routine test generation, straightforward bug fixing, and small integration work look more exposed than they did only a few years ago. The result is visible unease not only among students and junior developers, but also […]

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arXiv:2601.08858v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive domains such as healthcare, finance, and law, yet their integration raises pressing concerns around trust, accountability, and reliability. This paper explores adaptive trust metrics for multi LLM ecosystems, proposing a framework for quantifying and improving model reliability under regulated constraints. By analyzing system behaviors, evaluating uncertainty across multiple LLMs, and implementing dynamic monitoring pipelines, the study demonstrates practical pathways for operational trustworthiness. Case studies […]

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Reinforcement-Learning-based post-training has recently emerged as a promising paradigm for aligning text-to-image diffusion models with human preferences. In recent studies, increasing the rollout group size yields pronounced performance improvements, indicating substantial room for further alignment gains. However, scaling rollouts on large-scale foundational diffusion models (e.g., FLUX.1-12B) imposes a heavy computational burden. To alleviate this bottleneck, we explore the integration of FP4 quantization into Diffusion RL rollouts. Yet, we identify that naive quantized pipelines inherently introduce risks of performance […]

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Fairness in Continual Learning for Large Multimodal Models (LMMs) is an emerging yet underexplored challenge, particularly in the presence of imbalanced data distributions that can lead to biased model updates and suboptimal performance across tasks. While recent continual learning studies have made progress in addressing catastrophic forgetting, the problem of fairness caused the imbalanced data remains largely underexplored. This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or $φ$-DPO) framework for continual learning in LMMs. In particular, […]

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