arXiv:2602.12461v1 Announce Type: new Abstract: Recent studies have shown that CLIP model’s adversarial robustness in zero-shot classification tasks can be enhanced by adversarially fine-tuning its image encoder with adversarial examples (AEs), which are generated by minimizing the cosine similarity between images and a hand-crafted template (e.g., ”A photo of a {label}”). However, it has been shown that the cosine similarity between a single image and a single hand-crafted template is insufficient to measure the similarity for image-text pairs. […]

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arXiv:2603.15672v1 Announce Type: new Abstract: Hardware design errors discovered after fabrication require costly physical respins that can delay products by months. Existing electronic design automation (EDA) tools enforce structural connectivity rules. However, they cannot verify that connections are emph{semantically} correct with respect to component datasheets. For example, that a symbol’s pinout matches the manufacturer’s specification, or that a voltage regulator’s feedback resistors produce the intended output. We present DRCY, the first production-ready multi-agent LLM system that automates first-pass […]

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arXiv:2603.15807v1 Announce Type: new Abstract: Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups. In this work, we examined how subtitle errors affect evaluations of speakers and their content using a preregistered online experiment (N=207, U.S.-based crowdworkers). Participants watched speakers with various accents deliver a talk in which the subtitles were accurate or error-prone. Our results indicate that error-prone subtitles consistently reduce both speaker and content evaluations for all speakers. We did not […]

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The accuracy of machine learning interatomic potentials suffers from reference data that contains numerical noise. Often originating from unconverged or inconsistent electronic-structure calculations, this noise is challenging to identify. Existing mitigation strategies such as manual filtering or iterative refinement of outliers, require either substantial expert effort or multiple expensive retraining cycles, making them difficult to scale to large datasets. Here, we introduce an on-the-fly outlier detection scheme that automatically down-weights noisy samples, without requiring additional reference calculations. By […]

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The conflict between Anthropic and Washington shows how artificial intelligence intersects with geopolitics. It began as a principled refusal to support military applications and has now evolved into a bigger question: Who controls these advanced AI systems? The companies building them or the government wanting to deploy them? This is a rare and strategic opportunity for the United Kingdom. Britain is portraying itself as a more reliable and aligned partner, one that may offer regular flexibility without any […]

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In this quiz, you’ll test your understanding of How to Use Ollama to Run Large Language Models Locally. By working through this quiz, you’ll revisit how to install Ollama, pull and manage models, chat with local LLMs from your terminal, and connect them to AI coding tools. Running models locally means your prompts stay private and no API keys or cloud services are needed. See how well you remember the key commands and concepts. [ Improve Your Python […]

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arXiv:2604.07397v1 Announce Type: new Abstract: A key inefficiency in diffusion training occurs when a randomly initialized network, lacking visual priors, encounters gradients from the full complexity spectrum–most of which it lacks the capacity to resolve. We propose Data Warmup, a curriculum strategy that schedules training images from simple to complex without modifying the model or loss. Each image is scored offline by a semantic-aware complexity metric combining foreground dominance (how much of the image salient objects occupy) and […]

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Hi, I have couple of papers under consideration in OSDI ’26 and VLDB ’26 – and would like to pre-publish them in Arvix. Can anyone with endorsement rights in cs.DS or cs.AI or other related fields can please endorse me? https://arxiv.org/auth/endorse?x=6WMN8A Endorsement Code: 6WMN8A submitted by /u/rch0wdhury [link] [comments]

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arXiv:2505.19367v2 Announce Type: replace Abstract: Guidance is a cornerstone of modern diffusion models, playing a pivotal role in conditional generation and enhancing the quality of unconditional samples. However, current approaches to guidance scheduling–determining the appropriate guidance weight–are largely heuristic and lack a solid theoretical foundation. This work addresses these limitations on two fronts. First, we provide a theoretical formalization that precisely characterizes the relationship between guidance strength and classifier confidence. Second, building on this insight, we introduce a […]

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