Let me start with a scenario you’ve probably lived. It’s 2am. A PagerDuty alert fires. You drag yourself to a laptop, log into Grafana, spend 20 minutes correlating dashboards that were last updated in 2022, and eventually trace the problem to a single upstream service that’s been quietly timing out for the past hour. By the time you push a fix and verify recovery, you’ve been awake for two hours, your on-call rotation is resentful, and your system […]

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Did anyone get an email? I emailed the chairs. They say every participant got an email titled: “CVPR26 BP Scholarship Decision Has Been Released”, and participants got a separate email with the award and details. But I got no such email, yet. submitted by /u/Erika_bomber [link] [comments]

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arXiv:2602.15856v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) effectively grounds Large Language Models (LLMs) with external knowledge and is widely applied to Web-related tasks. However, its scalability is hindered by excessive context length and redundant retrievals. Recent research on soft context compression aims to address this by encoding long documents into compact embeddings, yet they often underperform non-compressed RAG due to their reliance on auto-encoder-like full-compression that forces the encoder to compress all document information regardless of relevance […]

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arXiv:2602.10714v1 Announce Type: cross Abstract: Preconditioning is a common method applied to modify Markov chain Monte Carlo algorithms with the goal of making them more efficient. In practice it is often extremely effective, even when the preconditioner is learned from the chain. We analyse and compare the finite-time computational costs of schemes which learn a preconditioner based on the target covariance or the expected Hessian of the target potential with that of a corresponding scheme that does not […]

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arXiv:2602.23407v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities. Existing approaches commonly suffer from two key limitations: the scarcity of high-quality security data and coarse-grained reinforcement learning reward signals. To address these challenges, we propose Vul2Safe, a new secure code generation framework that leverages LLM self-reflection to construct high-confidence repair pairs from real-world vulnerabilities, and further generates diverse implicit prompts to build the […]

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arXiv:2601.08963v1 Announce Type: new Abstract: Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D molecule generation. However, their widespread use remains constrained by long sampling trajectories, stochastic variance in the reverse process, and limited structural awareness in denoising dynamics. The Directly Denoising Diffusion Model (DDDM) mitigates these inefficiencies by replacing stochastic reverse MCMC updates with deterministic denoising step, substantially reducing inference […]

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arXiv:2601.16390v1 Announce Type: new Abstract: Large language models exhibit strong multilingual capabilities, yet significant performance gaps persist between dominant and non-dominant languages. Prior work attributes this gap to imbalances between shared and language-specific neurons in multilingual representations. We propose Cross-Lingual Activation Steering (CLAS), a training-free inference-time intervention that selectively modulates neuron activations. We evaluate CLAS on classification and generation benchmarks, achieving average improvements of 2.3% (Acc.) and 3.4% (F1) respectively, while maintaining high-resource language performance. We discover that […]

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Three weeks ago, we were preparing for a launch and running extensive tests. We utilized multiple AIs for the process: GPT, Cohere, Mistral, Perplexity… At first, I looked at the information provided and made adjustments to improve it. By the third day, however, I was just passing information through. Needless to say, it took more than twice as many days to recover and regain my independent thinking. What happened to me？ Generative AI offers clear advantages. It saves […]

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arXiv:2604.21934v1 Announce Type: new Abstract: Human routines structure daily life, yet remain challenging for computational systems to understand. This paper presents the first systematic review of routine computing, a previously implicit but increasingly recognized field that focuses on computationally sensing and modeling human behaviors. It synthesizes 203 studies published up to August 2025. The paper presents a new taxonomy of the literature, focusing on temporal structures, behavioral interactions, cognitive aspects, and how variability and deviations are addressed. The […]

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arXiv:2602.05852v1 Announce Type: cross Abstract: Community detection in networks is a fundamental problem in machine learning and statistical inference, with applications in social networks, biological systems, and communication networks. The stochastic block model (SBM) serves as a canonical framework for studying community structure, and exact recovery, identifying the true communities with high probability, is a central theoretical question. While classical results characterize the phase transition for exact recovery based solely on graph connectivity, many real-world networks contain additional […]

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