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arXiv2025

VHE $γ$-ray observations of bright BL Lacs with the Large-Sized Telescope prototype (LST-1) of the CTAO

Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory operating in the energy range from 20 GeV up to 300 TeV, with two sites in La Palma (Spain) and Paranal (Chile). It will consist of telescopes of three sizes, covering different parts of the large energy range. We report on the performance of Large-Sized Telescope prototype (LST-1) in the detection and characterization of extragalactic gamma-ray sources, with a focus on the reconstructed gamma-ray spectra and variability of classical bright BL Lacertae objects, which were observed during the early commissioning phase of the instrument. LST-1 data from known bright gamma-ray blazars - Markarian 421, Markarian 501, 1ES 1959+650, 1ES 0647+250, and PG 1553+113 - were collected between July 10, 2020, and May 23, 2022, covering a zenith angle range of 4 deg to 57 deg. The reconstructed light curves were analyzed using a Bayesian block algorithm to distinguish the different activity phases of each blazar. Simultaneous Fermi-LAT data were utilized to reconstruct the broadband $γ$-ray spectra for the sources during each activity phase. High-level reconstructed data in a format compatible with gammapy are provided together with measured light curves and spectral energy distributions (SEDs) for several bright blazars and an interpretation of the observed variability in long and short timescales. Simulations of historical flares are generated to evaluate the sensitivity of LST-1. This work represents the first milestone in monitoring bright BL Lacertae objects with a CTAO telescope.

astro-ph.HE↗

arXiv2025

Towards an AI-Augmented Textbook

Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach as Learn Your Way. We report pedagogical evaluations of the different transformations and augmentations, and present the results of a a randomized control trial, highlighting the advantages of learning with Learn Your Way over regular textbook usage.

cs.CY↗

arXiv2025

PLaMo 2 Technical Report

In this report, we introduce PLaMo 2, a series of Japanese-focused large language models featuring a hybrid Samba-based architecture that transitions to full attention via continual pre-training to support 32K token contexts. Training leverages extensive synthetic corpora to overcome data scarcity, while computational efficiency is achieved through weight reuse and structured pruning. This efficient pruning methodology produces an 8B model that achieves performance comparable to our previous 100B model. Post-training further refines the models using a pipeline of supervised fine-tuning (SFT) and direct preference optimization (DPO), enhanced by synthetic Japanese instruction data and model merging techniques. Optimized for inference using vLLM and quantization with minimal accuracy loss, the PLaMo 2 models achieve state-of-the-art results on Japanese benchmarks, outperforming similarly-sized open models in instruction-following, language fluency, and Japanese-specific knowledge.

cs.CL↗

arXiv2025

Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets

Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows.

cs.CV↗

arXiv2025

Measurement of the time-integrated CP asymmetry in $D^{0}\rightarrow K^{0}_{S}K^{0}_{S}$ decays using Belle and Belle II data

We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 428 fb$^{-1}$, respectively. The $D^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}π^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries. The result, $(-1.4\pm1.3{\rm(stat)}\pm0.1{\rm (syst)})\%$, is consistent with previous determinations and with CP symmetry.

hep-ex↗

arXiv2025

Observation of the decays $B^{+} \to Σ_{c}(2455)^{++} \overlineΞ_{c}^{-}$ and $B^{0} \to Σ_{c}(2455)^{0} \overlineΞ_{c}^{0}$

We report the first observation of the two-body baryonic decays $B^{+} \to Σ_{c}(2455)^{++} \overlineΞ_{c}^{-}$ and $B^{0} \to Σ_{c}(2455)^{0} \overlineΞ_{c}^{0}$ with significances of $7.3\,σ$ and $6.2\,σ$, respectively, including statistical and systematic uncertainties. The branching fractions are measured to be $\mathcal{B}(B^{+} \to Σ_{c}(2455)^{++} \overlineΞ_{c}^{-}) = (5.74 \pm 1.11 \pm 0.42_{-1.53}^{+2.47}) \times 10^{-4}$ and $\mathcal{B}(B^{0} \to Σ_{c}(2455)^{0} \overlineΞ_{c}^{0}) = (4.83 \pm 1.12 \pm 0.37_{-0.60}^{+0.72}) \times 10^{-4}$. The first and second uncertainties are statistical and systematic, respectively, while the third ones arise from the absolute branching fractions of $\overlineΞ_{c}^{-}$ or $\overlineΞ_{c}^{0}$ decays. The data samples used for this analysis have integrated luminosities of 711~$\mathrm{fb}^{-1}$ and 365~$\mathrm{fb}^{-1}$, and were collected at the $Υ(4S)$ resonance by the Belle and Belle~II detectors operating at the KEKB and SuperKEKB asymmetric-energy $e^{+}e^{-}$ colliders, respectively.

hep-ex↗

arXiv2025

Measurement of reactor antineutrino oscillation at SNO+

The SNO+ collaboration reports its second spectral analysis of reactor antineutrino oscillation using 286 tonne-years of new data. The measured energies of reactor antineutrino candidates were fitted to obtain the second-most precise determination of the neutrino mass-squared difference $Δm^2_{21}$ = ($7.96^{+0.48}_{-0.42}$) $\times$ 10$^{-5}$ eV$^2$. Constraining $Δm^2_{21}$ and $\sin^2θ_{12}$ with measurements from long-baseline reactor antineutrino and solar neutrino experiments yields $Δm^2_{21}$ = ($7.58^{+0.18}_{-0.17}$) $\times$ 10$^{-5}$ eV$^2$ and $\sin^2θ_{12} = 0.308 \pm 0.013$. This fit also yields a first measurement of the flux of geoneutrinos in the Western Hemisphere, with $73^{+47}_{-43}$ TNU at SNO+.

hep-ex↗

arXiv2025

On Soft Clustering For Correlation Estimators

Properly estimating correlations between objects at different spatial scales necessitates $\mathcal{O}(n^2)$ distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering assignments enable correlation estimators that are theoretically differentiable with respect to their input catalogs. Thus, we also build a theoretical framework for differentiable correlation functions and describe their utility in comparison to existing surrogate models. Notably, we find that repeated normalization and distance function calls slow gradient calculations and that sparse Jacobians destabilize precision, pointing towards either approximate or surrogate methods as a necessary solution to exact gradients from correlation functions. To that end, we close with a discussion of surrogate models as proxies for correlation functions. We provide an example that demonstrates the efficacy of surrogate models to enable gradient-based optimization of astrophysical model parameters, successfully minimizing a correlation function output. Our numerical experiments cover science cases across cosmology, from point spread function (PSF) modeling efforts to gravitational simulations to galaxy intrinsic alignment (IA).

astro-ph.IM↗

arXiv2025

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemotron-H, a family of 8B and 56B/47B hybrid Mamba-Transformer models designed to reduce inference cost for a given accuracy level. To achieve this goal, we replace the majority of self-attention layers in the common Transformer model architecture with Mamba layers that perform constant computation and require constant memory per generated token. We show that Nemotron-H models offer either better or on-par accuracy compared to other similarly-sized state-of-the-art open-sourced Transformer models (e.g., Qwen-2.5-7B/72B and Llama-3.1-8B/70B), while being up to 3$\times$ faster at inference. To further increase inference speed and reduce the memory required at inference time, we created Nemotron-H-47B-Base from the 56B model using a new compression via pruning and distillation technique called MiniPuzzle. Nemotron-H-47B-Base achieves similar accuracy to the 56B model, but is 20% faster to infer. In addition, we introduce an FP8-based training recipe and show that it can achieve on par results with BF16-based training. This recipe is used to train the 56B model. We are releasing Nemotron-H base model checkpoints with support in Hugging Face and NeMo.

cs.CL↗

arXiv2025

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the majority of the self-attention layers in the common Transformer architecture are replaced with Mamba-2 layers, to achieve improved inference speed when generating the long thinking traces needed for reasoning. We create Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model (Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe. After aligning Nemotron-Nano-12B-v2-Base, we employ the Minitron strategy to compress and distill the model with the goal of enabling inference on up to 128k tokens on a single NVIDIA A10G GPU (22GiB of memory, bfloat16 precision). Compared to existing similarly-sized models (e.g., Qwen3-8B), we show that Nemotron-Nano-9B-v2 achieves on-par or better accuracy on reasoning benchmarks while achieving up to 6x higher inference throughput in reasoning settings like 8k input and 16k output tokens. We are releasing Nemotron-Nano-9B-v2, Nemotron-Nano12B-v2-Base, and Nemotron-Nano-9B-v2-Base checkpoints along with the majority of our pre- and post-training datasets on Hugging Face.

cs.CL↗

arXiv2025

Constraining the TeV gamma-ray emission of SN 2024bch, a possible type IIn-L from a red supergiant progenitor. Multiwavelength observations and analysis of the progenitor

We present very high-energy optical photometry and spectroscopic observations of SN 2024bch in the nearby galaxy NGC 3206 (\sim 20 Mpc). We used gamma-ray observations performed with the first Large-Sized Telescope (LST-1) of the Cherenkov Telescope Array Observatory (CTAO) and optical observations with the Liverpool Telescope (LT) combined with data from public repositories to evaluate the general properties of the event and the progenitor star. No significant emission above the LST-1 energy threshold for this observation (\sim 100 GeV) was detected in the direction of SN 2024bch, and we computed an integral upper limit on the photon flux of F_γ(>100 GeV) \le 3.61 \times 10^{-12} cm^{-2} s^{-1} based on six nonconsecutive nights of observations with the LST-1, between 16 and 38 days after the explosion. Employing a general model for the gamma-ray flux emission, we found an upper limit on the mass-loss-rate to wind-velocity ratio of \dot M/u_{w} \le 10^{-4} \frac{M_\odot}{yr}\frac{s}{km}, although gamma-gamma absorption could potentially have skewed this estimation, effectively weakening our constraint. From spectro-photometric observations we found progenitor parameters of M_{pr} = 11 - 20 M_\odot and R_{pr} = 531 \pm 125 R_\odot. Finally, using archival images from the Hubble Space Telescope, we constrained the luminosity of the progenitor star to log(L_{pr}/L_\odot) \le 4.82 and its effective temperature to T_{pr} \le 4000 K. Our results suggest that SN 2024bch is a type IIn-L supernova that originated from a progenitor star consistent with a red supergiant. We show how the correct estimation of the mass-loss history of a supernova will play a major role in future multiwavelength observations.

astro-ph.HE↗

arXiv2025

Combined dark matter search towards dwarf spheroidal galaxies with Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS

Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect dark matter (DM) searches using gamma-ray telescopes because they are thought to have high DM content and a low astrophysical background. The sensitivity of these searches is improved by combining the observations of dSphs made by different gamma-ray telescopes. We present the results of a combined search by the most sensitive currently operating gamma-ray telescopes, namely: the satellite-borne Fermi-LAT telescope; the ground-based imaging atmospheric Cherenkov telescope arrays H.E.S.S., MAGIC, and VERITAS; and the HAWC water Cherenkov detector. Individual datasets were analyzed using a common statistical approach. Results were subsequently combined via a global joint likelihood analysis. We obtain constraints on the velocity-weighted cross section $\langle σ\mathit{v} \rangle$ for DM self-annihilation as a function of the DM particle mass. This five-instrument combination allows the derivation of up to 2-3 times more constraining upper limits on $\langle σ\mathit{v} \rangle$ than the individual results over a wide mass range spanning from 5 GeV to 100 TeV. Depending on the DM content modeling, the 95% confidence level observed limits reach $1.5\times$10$^{-24}$ cm$^3$s$^{-1}$ and $3.2\times$10$^{-25}$ cm$^3$s$^{-1}$, respectively, in the $τ^+τ^-$ annihilation channel for a DM mass of 2 TeV.

astro-ph.HE↗

arXiv2025

HAWC, VERITAS, Fermi-LAT and XMM-Newton follow-up observations of the unidentified ultra-high-energy gamma-ray source LHAASO J2108+5157

We report observations of the ultra-high-energy gamma-ray source LHAASO J2108$+$5157, utilizing VERITAS, HAWC, Fermi-LAT, and XMM-Newton. VERITAS has collected $\sim$ 40 hours of data that we used to set ULs to the emission above 200 GeV. The HAWC data, collected over $\sim 2400$ days, reveal emission between 3 and 146 TeV, with a significance of $7.5~σ$, favoring an extended source model. The best-fit spectrum measured by HAWC is characterized by a simple power-law with a spectral index of $2.45\pm0.11_{stat}$. Fermi-LAT analysis finds a point source with a very soft spectrum in the LHAASO J2108+5157 region, consistent with the 4FGL-DR3 catalog results. The XMM-Newton analysis yields a null detection of the source in the 2 - 7 keV band. The broadband spectrum can be interpreted as a pulsar and a pulsar wind nebula system, where the GeV gamma-ray emission originates from an unidentified pulsar, and the X-ray and TeV emission is attributed to synchrotron radiation and inverse Compton scattering of electrons accelerated within a pulsar wind nebula. In this leptonic scenario, our X-ray upper limit provides a stringent constraint on the magnetic field, which is $\lesssim 1.5\ μ$G.

astro-ph.HE↗

arXiv2025

Audio2Face-3D: Audio-driven Realistic Facial Animation For Digital Avatars

Audio-driven facial animation presents an effective solution for animating digital avatars. In this paper, we detail the technical aspects of NVIDIA Audio2Face-3D, including data acquisition, network architecture, retargeting methodology, evaluation metrics, and use cases. Audio2Face-3D system enables real-time interaction between human users and interactive avatars, facilitating facial animation authoring for game characters. To assist digital avatar creators and game developers in generating realistic facial animations, we have open-sourced Audio2Face-3D networks, SDK, training framework, and example dataset.

cs.GR↗

arXiv2025

BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining

Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, the use of synthetic data for pretraining has emerged as a promising paradigm for pushing the frontier of performance. Despite this, the factors affecting synthetic data quality remain poorly understood. In this work, we introduce BeyondWeb, a synthetic data generation framework that produces high-quality synthetic data for pretraining. BeyondWeb significantly extends the capabilities of traditional web-scale datasets, outperforming state-of-the-art synthetic pretraining datasets such as Cosmopedia and Nemotron-CC's high-quality synthetic subset (Nemotron-Synth) by up to 5.1 percentage points (pp) and 2.6pp, respectively, when averaged across a suite of 14 benchmark evaluations. It delivers up to 7.7x faster training than open web data and 2.7x faster than Nemotron-Synth. Remarkably, a 3B model trained for 180B tokens on BeyondWeb outperforms an 8B model trained for the same token budget on Cosmopedia. We also present several insights from BeyondWeb on synthetic data for pretraining: what drives its benefits, which data to rephrase and how, and the impact of model size and family on data quality. Overall, our work shows that there's no silver bullet for generating high-quality synthetic pretraining data. The best outcomes require jointly optimizing many factors, a challenging task that requires rigorous science and practical expertise. Naive approaches can yield modest improvements, potentially at great cost, while well-executed methods can yield transformative improvements, as exemplified by BeyondWeb.

cs.LG↗

arXiv2025

gpt-oss-120b & gpt-oss-20b Model Card

We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert transformer architecture and are trained using large-scale distillation and reinforcement learning. We optimize the models to have strong agentic capabilities (deep research browsing, python tool use, and support for developer-provided functions), all while using a rendered chat format that enables clear instruction following and role delineation. Both models achieve strong results on benchmarks ranging from mathematics, coding, and safety. We release the model weights, inference implementations, tool environments, and tokenizers under an Apache 2.0 license to enable broad use and further research.

cs.CL↗

arXiv2025

SafeWork-R1: Coevolving Safety and Intelligence under the AI-45$^{\circ}$ Law

We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framework, which incorporates large-scale, progressive, safety-oriented reinforcement learning post-training, supported by a suite of multi-principled verifiers. Unlike previous alignment methods such as RLHF that simply learn human preferences, SafeLadder enables SafeWork-R1 to develop intrinsic safety reasoning and self-reflection abilities, giving rise to safety `aha' moments. Notably, SafeWork-R1 achieves an average improvement of $46.54\%$ over its base model Qwen2.5-VL-72B on safety-related benchmarks without compromising general capabilities, and delivers state-of-the-art safety performance compared to leading proprietary models such as GPT-4.1 and Claude Opus 4. To further bolster its reliability, we implement two distinct inference-time intervention methods and a deliberative search mechanism, enforcing step-level verification. Finally, we further develop SafeWork-R1-InternVL3-78B, SafeWork-R1-DeepSeek-70B, and SafeWork-R1-Qwen2.5VL-7B. All resulting models demonstrate that safety and capability can co-evolve synergistically, highlighting the generalizability of our framework in building robust, reliable, and trustworthy general-purpose AI.

cs.AI↗

arXiv2025

Multi-wavelength Study of HESS J0632+057: New Insights into Pulsar-Disk Interaction

We present an analysis of new multi-wavelength observations of the TeV gamma-ray binary HESS J0632+057, conducted using SALT, Swift, NuSTAR, and VERITAS in 2023--2024. By combining these new data with archival observations, we confirm previous suggestions of orbital variability in the source's X-ray spectrum, including increased X-ray absorption at the orbital phase interval of $ϕ\approx0.3\textrm{--}0.4$. The source's X-ray flux within this phase interval seems to have exhibited a significant change on an orbital timescale. Additionally, occasional short-term variations in the X-ray band on a timescale of less than 3 days have been observed. The measured duration of the increased absorbing column density and the flux variability timescales can provide clues about the interaction between the putative pulsar and the Be companion's disk if, as previously suggested, the pulsar crosses the disk at this phase interval. Moreover, the new contemporaneous X-ray and TeV observations around the pulsar-crossing phases revealed independent variability in the X-ray and TeV fluxes, contrary to a previous observation of concurrent flux increases. While these observations alone cannot provide definitive conclusions, we discuss our results in the context of pulsar-disk interaction and intrabinary shock emission scenarios.

astro-ph.HE↗