arXiv:2601.16338v1 Announce Type: new Abstract: With the growing ubiquity of multi-core architectures, concurrent systems have become essential but increasingly prone to complex issues such as data races and deadlocks. While modern issue-tracking systems facilitate the reporting of such problems, labeling concurrency-related bug reports remains a labor-intensive and error-prone task. This paper presents a linguistic-pattern-based framework for automatically identifying concurrency bug reports. We derive 58 distinct linguistic patterns from 730 manually labeled concurrency bug reports, organized across four levels: […]

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Online model learning in real-time is required by many applications, for example, robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component and cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The […]

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Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples algorithms, models, and rewards through through a modular, registry-based architecture. This design enables seamless integration of new algorithms and architectures, as demonstrated by our support for GRPO, DiffusionNFT, and AWM across Flux, Qwen-Image, and WAN video models. By minimizing implementation overhead, Flow-Factory empowers […]

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arXiv:2602.12301v1 Announce Type: new Abstract: Although annotated music descriptor datasets for user queries are increasingly common, few consider the user’s intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors […]

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I recently decided to stop treating KL Divergence as a “black box” distance metric and actually derive it from first principles to understand why it behaves the way it does in optimization. I found that the standard intuition (“it measures distance between distributions”) often hides the actual geometry of what’s happening during training. I wrote a deep dive article about this, but I wanted to share the two biggest “Aha!!!!!!” moments here directly. The optimization geometry (forward vs. […]

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arXiv:2602.15568v1 Announce Type: cross Abstract: The scenario approach is an established data-driven design framework that comes equipped with a powerful theory linking design complexity to generalization properties. In this approach, data are simultaneously used both for design and for certifying the design’s reliability, without resorting to a separate test dataset. This paper takes a step further by guaranteeing additional properties, useful in post-design usage but not considered during the design phase. To this end, we introduce a two-level […]

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arXiv:2601.21959v1 Announce Type: new Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio conditions. Our mechanism is based on a private mean estimator with data-driven clamping bounds, whose population risk matches the private minimax rate up to logarithmic factors. Using this estimator, we construct private test statistics that achieve the same asymptotic relative efficiency as the non-private, most powerful tests […]

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arXiv:2602.10226v1 Announce Type: new Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors. Achieving substantial improvements in these areas is a non-trivial task, traditionally relying on extensive manual iterations to test new hypotheses. We propose a self-evolving system that leverages Large Language Models (LLMs), specifically those from Google’s Gemini family, to […]

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