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Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties Are Smaller Than They Appear

Dylan Folsom, Carlos Blanco, Mariangela Lisanti, Lina Necib, Mark Vogelsberger, Lars Hernquist

Physical Review Letters·2025

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way–like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds—a necessary step for quantifying the astrophysical uncertainties that feed into experimental results—requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this Letter, we quantify this variation with nearly 100 Milky Way–like galaxies from the 50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The ensemble of predicted speed distributions is well characterized by the standard halo model, a Maxwell-Boltzmann distribution truncated at the escape speed, though the individual distributions can deviate from it, especially at high speeds. The dark matter–nucleon cross section limits placed by these speed distributions vary by <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mo>∼</a:mo> <a:mn>60</a:mn> <a:mo>%</a:mo> </a:math> about the median. This places the <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mrow> <c:mn>1</c:mn> <c:mi>σ</c:mi> </c:mrow> </c:math> astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when subselecting on those 50 galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.

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The Evolving Ethics of Medical Data Stewardship

Adam Leon Kesner, Anyi Li, Phillip Koo

arXiv·2025

Healthcare stands at a critical crossroads. Artificial Intelligence and modern computing are unlocking opportunities, yet their value lies in the data that fuels them. The value of healthcare data is no longer limited to individual patients. However, data stewardship and governance has not kept pace, and privacy-centric policies are hindering both innovation and patient protections. As healthcare moves toward a data-driven future, we must define reformed data stewardship that prioritizes patients' interests by proactively managing modern risks and opportunities while addressing key challenges in cost, efficacy, and accessibility. Current healthcare data policies are rooted in 20th-century legislation shaped by outdated understandings of data-prioritizing perceived privacy over innovation and inclusion. While other industries thrive in a data-driven era, the evolution of medicine remains constrained by regulations that impose social rather than scientific boundaries. Large-scale aggregation is happening, but within opaque, closed systems. As we continue to uphold foundational ethical principles - autonomy, beneficence, nonmaleficence, and justice - there is a growing imperative to acknowledge they exist in evolving technological, social, and cultural realities. Ethical principles should facilitate, rather than obstruct, dialogue on adapting to meet opportunities and address constraints in medical practice and healthcare delivery. The new ethics of data stewardship places patients first by defining governance that adapts to changing landscapes. It also rejects the legacy of treating perceived privacy as an unquestionable, guiding principle. By proactively redefining data stewardship norms, we can drive an era of medicine that promotes innovation, protects patients, and advances equity - ensuring future generations advance medical discovery and care.

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MHR: Momentum Human Rig

Aaron Ferguson, Ahmed A. A. Osman, Berta Bescos, Carsten Stoll, Chris Twigg, Christoph Lassner, David Otte, Eric Vignola, Fabian Prada, Federica Bogo, Igor Santesteban, Javier Romero, Jenna Zarate, Jeongseok Lee, Jinhyung Park, Jinlong Yang, John Doublestein, Kishore Venkateshan, Kris Kitani, Ladislav Kavan, Marco Dal Farra, Matthew Hu, Matthew Cioffi, Michael Fabris, Michael Ranieri, Mohammad Modarres, Petr Kadlecek, Rawal Khirodkar, Rinat Abdrashitov, Romain Prévost, Roman Rajbhandari, Ronald Mallet, Russell Pearsall, Sandy Kao, Sanjeev Kumar, Scott Parrish, Shoou-I Yu, Shunsuke Saito, Takaaki Shiratori, Te-Li Wang, Tony Tung, Yichen Xu, Yuan Dong, Yuhua Chen, Yuanlu Xu, Yuting Ye, Zhongshi Jiang

arXiv·2025

We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library. Our model enables expressive, anatomically plausible human animation, supporting non-linear pose correctives, and is designed for robust integration in AR/VR and graphics pipelines.

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Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models

Piercosma Bisconti, Matteo Prandi, Federico Pierucci, Francesco Giarrusso, Marcantonio Bracale, Marcello Galisai, Vincenzo Suriani, Olga Sorokoletova, Federico Sartore, Daniele Nardi

arXiv·2025

We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for Large Language Models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poetic attacks transfer across CBRN, manipulation, cyber-offence, and loss-of-control domains. Converting 1,200 MLCommons harmful prompts into verse via a standardized meta-prompt produced ASRs up to 18 times higher than their prose baselines. Outputs are evaluated using an ensemble of 3 open-weight LLM judges, whose binary safety assessments were validated on a stratified human-labeled subset. Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.

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HISE-KT: Synergizing Heterogeneous Information Networks and LLMs for Explainable Knowledge Tracing with Meta-Path Optimization

Zhiyi Duan, Zixing Shi, Hongyu Yuan, Qi Wang

arXiv·2025

Knowledge Tracing (KT) aims to mine students' evolving knowledge states and predict their future question-answering performance. Existing methods based on heterogeneous information networks (HINs) are prone to introducing noises due to manual or random selection of meta-paths and lack necessary quality assessment of meta-path instances. Conversely, recent large language models (LLMs)-based methods ignore the rich information across students, and both paradigms struggle to deliver consistently accurate and evidence-based explanations. To address these issues, we propose an innovative framework, HIN-LLM Synergistic Enhanced Knowledge Tracing (HISE-KT), which seamlessly integrates HINs with LLMs. HISE-KT first builds a multi-relationship HIN containing diverse node types to capture the structural relations through multiple meta-paths. The LLM is then employed to intelligently score and filter meta-path instances and retain high-quality paths, pioneering automated meta-path quality assessment. Inspired by educational psychology principles, a similar student retrieval mechanism based on meta-paths is designed to provide a more valuable context for prediction. Finally, HISE-KT uses a structured prompt to integrate the target student's history with the retrieved similar trajectories, enabling the LLM to generate not only accurate predictions but also evidence-backed, explainable analysis reports. Experiments on four public datasets show that HISE-KT outperforms existing KT baselines in both prediction performance and interpretability.

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GPU-Initiated Networking for NCCL

Khaled Hamidouche, John Bachan, Pak Markthub, Peter-Jan Gootzen, Elena Agostini, Sylvain Jeaugey, Aamir Shafi, Georgios Theodorakis, Manjunath Gorentla Venkata

arXiv·2025

Modern AI workloads, especially Mixture-of-Experts (MoE) architectures, increasingly demand low-latency, fine-grained GPU-to-GPU communication with device-side control. Traditional GPU communication follows a host-initiated model, where the CPU orchestrates all communication operations - a characteristic of the CUDA runtime. Although robust for collective operations, applications requiring tight integration of computation and communication can benefit from device-initiated communication that eliminates CPU coordination overhead. NCCL 2.28 introduces the Device API with three operation modes: Load/Store Accessible (LSA) for NVLink/PCIe, Multimem for NVLink SHARP, and GPU-Initiated Networking (GIN) for network RDMA. This paper presents the GIN architecture, design, semantics, and highlights its impact on MoE communication. GIN builds on a three-layer architecture: i) NCCL Core host-side APIs for device communicator setup and collective memory window registration; ii) Device-side APIs for remote memory operations callable from CUDA kernels; and iii) A network plugin architecture with dual semantics (GPUDirect Async Kernel-Initiated and Proxy) for broad hardware support. The GPUDirect Async Kernel-Initiated backend leverages DOCA GPUNetIO for direct GPU-to-NIC communication, while the Proxy backend provides equivalent functionality via lock-free GPU-to-CPU queues over standard RDMA networks. We demonstrate GIN's practicality through integration with DeepEP, an MoE communication library. Comprehensive benchmarking shows that GIN provides device-initiated communication within NCCL's unified runtime, combining low-latency operations with NCCL's collective algorithms and production infrastructure.

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Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties Are Smaller Than They Appear

The Evolving Ethics of Medical Data Stewardship

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MHR: Momentum Human Rig

Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models

HISE-KT: Synergizing Heterogeneous Information Networks and LLMs for Explainable Knowledge Tracing with Meta-Path Optimization

GPU-Initiated Networking for NCCL

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