OREM, Utah, Feb. 24, 2026 — SecurityMetrics, a leading innovator in compliance and cybersecurity, has officially announced their security framework platform for CMMC compliance and validation. CMMC is a standardized set of cybersecurity requirements designed to ensure that controlled government information (like designs or contract details) stays protected from hackers and foreign threats. With SecurityMetrics’ new CMMC solutions, defense contractors of all levels can get innovative tools designed specifically for their unique compliance needs, including: A new Self-Assessment […]

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arXiv:2602.08470v2 Announce Type: replace-cross Abstract: Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epistemic uncertainty (EU) and have been shown to improve model robustness in various settings. However, most state-of-the-art methods mainly define EU as disagreement caused by random training initializations, which mostly reflects sensitivity to optimization randomness rather than uncertainty from deeper sources. To address this, we define EU […]

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When I want to quickly implement a one-off experiment in a part of the codebase I am unfamiliar with, I get codex to do extensive due diligence. Codex explores relevant slack channels, reads related discussions, fetches experimental branches from those discussions, and cherry picks useful changes for my experiment. All of this gets summarized in an extensive set of notes, with links back to where each piece of information was found. Using these notes, codex wires the experiment […]

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arXiv:2602.13315v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities, yet they encounter significant computational bottlenecks due to the massive volume of visual tokens. Consequently, visual token pruning, which substantially reduces the token count, has emerged as a critical technique for accelerating MLLM inference. Existing approaches focus on token importance, diversity, or an intuitive combination of both, without a principled framework for their optimal integration. To address this issue, we first conduct a systematic […]

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arXiv:2602.07149v1 Announce Type: new Abstract: The rise of generative models has led to increased use of large-scale datasets collected from the internet, often with minimal or no data curation. This raises concerns about the inclusion of sensitive or private information. In this work, we explore the presence of pregnancy ultrasound images, which contain sensitive personal information and are often shared online. Through a systematic examination of LAION-400M dataset using CLIP embedding similarity, we retrieve images containing pregnancy ultrasound […]

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arXiv:2602.11162v1 Announce Type: new Abstract: Recent studies have identified “retrieval heads” in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on static statistics aggregated across datasets, identifying heads that perform retrieval on average. This perspective overlooks the fine-grained temporal dynamics of autoregressive generation. In this paper, we investigate retrieval heads from a dynamic perspective. Through extensive analysis, we establish three core claims: (1) Dynamism: Retrieval heads vary dynamically across timesteps; […]

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arXiv:2505.10919v3 Announce Type: replace-cross Abstract: Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Benard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is […]

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arXiv:2506.20406v2 Announce Type: replace Abstract: Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance, lack statistical guarantees, and require solving complex optimization problems. To address these challenges, […]

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arXiv:2602.15033v1 Announce Type: new Abstract: This paper introduces CMOS invertible-logic (CIL) circuits based on many-body Hamiltonians. CIL can realize probabilistic forward and backward operations of a function by annealing a corresponding Hamiltonian using stochastic computing. We have created a Hamiltonian that includes three-body interaction of spins (probabilistic nodes). It provides some degrees of freedom to design a simpler landscape of Hamiltonian (energy) than that of the conventional two-body Hamiltonian. The simpler landscape makes it easier to reach the […]

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Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. […]

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