arXiv:2602.20204v1 Announce Type: new Abstract: AI kernel compilation for edge devices depends on the compiler’s ability to exploit parallelism and hide memory latency in the presence of hierarchical memory and explicit data movement. This paper reports a benchmark methodology and corresponding results for three compiler-controlled mechanisms in an MLIR-based compilation pipeline: vectorization (Vec), multi-threading (MT) across hardware contexts, and double buffering (DB) using ping–pong scratchpad buffers to overlap DMA transfers with compute. Using Triton/Inductor-generated kernels, we present an […]

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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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Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large-scale benchmark experiment to assess six […]

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arXiv:2603.28987v1 Announce Type: cross Abstract: Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to […]

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The widespread deployment of 5G networks, together with the coexistence of 4G/LTE networks, provides mobile devices a diverse set of candidate cells to connect to. However, associating mobile devices to cells to maximize overall network performance, a.k.a. cell (re)selection, remains a key challenge for mobile operators. Today, cell (re)selection parameters are typically configured manually based on operator experience and rarely adapted to dynamic network conditions. In this work, we ask: Can an agent automatically learn and adapt cell […]

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arXiv:2601.08857v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into professional workflows is increasingly reshaping software engineering practices. These tools have lowered the cost of code generation, explanation, and testing, while introducing new forms of automation into routine development tasks. In contrast, most of the software engineering and computer engineering curricula remain closely aligned with pedagogical models that equate manual syntax production with technical competence. This growing misalignment raises […]

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There’s a lot of focus right now on whether AI can write “perfect” code and what this will mean. As models will get better and context windows get bigger, will code quality improve? Will we soon reach a point where AI produces production-ready software on the first try? If the answer is “Yes, AI can get it right first time”, we should be focused on giving AI the perfect context, all our rules and standards, doing upfront planning on requirements, specifications […]

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AI assistants don’t have “bad memory.” They have bad governance. You’ve seen it: You wrote a meticulous architecture guide. The model ignores it. You said “4-layer architecture.” It outputs 3 layers. You said “no el-input.” It drops <el-input v-model=”value” /> like it’s doing you a favor. This isn’t about intelligence. It’s about incentives. In Claude Code, Skills are available context, not a hard constraint. If Claude “feels” it can answer without calling a Skill, it will. And your […]

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