In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. Our today’s guest is Leo Zhang, who works in the engineering team at FOSSA – a tool for open-source risk management. In the interview, we talk about his experience using Haskell in FOSSA’s backend analysis services and CLI. Read further to learn about the benefits […]

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Was drafting CVPR Rebuttal (after convincing myself to give a shot for days) and one of the reviewers had asked us to provide evidence for a particular statement, so we are planning to cite papers for it. Are we allowed to use additional page for references? Thanks submitted by /u/Forsaken-Order-7376 [link] [comments]

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arXiv:2507.15112v4 Announce Type: replace-cross Abstract: Machine learning systems increasingly face requirements to remove entire domains of information–such as toxic language or biases–rather than individual user data. This task presents a dilemma: full removal of the unwanted domain data is computationally expensive, while random partial removal is statistically inefficient. We find that a domain’s statistical influence is often concentrated in a small subset of its data samples, suggesting a path between ineffective partial removal and unnecessary complete removal. We […]

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arXiv:2601.20882v1 Announce Type: new Abstract: Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents’ capabilities in the full software DevOps cycle are still unknown. Different from pure code generation, handling the DevOps cycle in real-world software, including developing, deploying, and managing, requires analyzing large-scale projects, understanding dynamic program behaviors, leveraging domain-specific tools, and making sequential decisions. However, existing benchmarks focus on isolated problems and lack environments and tool interfaces for DevOps. We introduce […]

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Large Language Models (LLMs) have become foundational to modern Artificial Intelligence (AI), enabling advanced reasoning, multimodal understanding, and scalable human-AI interaction across diverse domains. This survey provides a comprehensive review of major proprietary and open-source LLM families, including GPT, LLaMA 2, Gemini, Claude, DeepSeek, Falcon, and Qwen. It systematically examines architectural advancements such as transformer refinements, mixture-of-experts paradigms, attention optimization, long-context modeling, and multimodal integration. The paper further analyzes alignment and safety mechanisms, encompassing instruction tuning, reinforcement learning […]

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We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more efficiently encode low-dimensional manifold geometry. Central to this approach are maximum manifold capacity representations (MMCRs), which help untangle complex manifolds by compressing variances among locally similar data points while amplifying variance among dissimilar data points. This design is particularly effective for high-dimensional data with substantial intra-cluster variance and curved manifold structures, such as biological […]

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arXiv:2601.04360v1 Announce Type: new Abstract: The transition from secondary to higher education represents a critical point in academic trajectories, particularly in programmes with a strong emphasis on basic sciences. Across different higher education systems, introductory Mathematics and Physics courses consistently concentrate high rates of early failure and attrition, yet most available evidence relies on cross-sectional analyses or limited time spans. This study presents a longitudinal evaluation of pre-university preparation based on early academic outcomes in Mathematics and Physics, […]

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