Showing 1–50 of 3,969 results for author: Lu, H

Observation and branching fraction measurements of $χ_{cJ}\to p \bar p K^0_S K^0_S$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko , et al. (705 additional authors not shown)

Abstract: Based on $(2.712\pm0.014)\times10^9$ $ψ(3686)$ events collected with the BESIII detector, the decays $χ_{cJ} \to p \bar{p} K^{0}_{S} K^{0}_{S}$ ($J=0,1,2$) are observed for the first time with statistical significances exceeding $5σ$. The measured branching fractions are $\mathcal{B}(χ_{c0}\to p \bar p K^{0}_{S} K^{0}_{S})=(6.94\pm0.30\pm0.38)\times10^{-5}$,… ▽ More Based on $(2.712\pm0.014)\times10^9$ $ψ(3686)$ events collected with the BESIII detector, the decays $χ_{cJ} \to p \bar{p} K^{0}_{S} K^{0}_{S}$ ($J=0,1,2$) are observed for the first time with statistical significances exceeding $5σ$. The measured branching fractions are $\mathcal{B}(χ_{c0}\to p \bar p K^{0}_{S} K^{0}_{S})=(6.94\pm0.30\pm0.38)\times10^{-5}$, $\mathcal{B}(χ_{c1}\to p \bar p K^{0}_{S} K^{0}_{S})=(1.30\pm0.12\pm0.08)\times10^{-5}$, and $\mathcal{B}(χ_{c2}\to p \bar p K^{0}_{S} K^{0}_{S})=(2.54\pm0.17\pm0.15)\times10^{-5}$, where the first uncertainties are statistical and the second systematic. Combining these results with the branching fractions of $χ_{cJ} \to p\bar{p} K^+K^-$ from a previous BESIII measurement, we determine the ratios of the branching fractions, $\frac{{\mathcal B}(χ_{c0,1,2}\to p\bar p K^+K^-)}{{\mathcal B}(χ_{c0,1,2}\to p\bar p K_S^0 K_S^0)}$, to be $1.8 \pm 0.1 \pm 0.3$, $9.8 \pm 0.9 \pm 1.7$ and $7.7 \pm 0.7 \pm 1.1$, respectively. These values differ significantly from the predictions of isospin symmetry. △ Less

Submitted 22 December, 2025; originally announced December 2025.

Comments: 10 Pages, 3 Figures, 3 Tables

Report number: BAM-00840

Hard Negative Sample-Augmented DPO Post-Training for Small Language Models

Authors: Haocheng Lu, Minjun Zhu, Henry Yu

Abstract: Large language models (LLMs) continue to struggle with mathematical reasoning, and common post-training pipelines often reduce each generated solution to a binary outcome: correct or incorrect. This perspective is limiting in practice, as failures in chain-of-thought (CoT) reasoning are frequently structured; solutions may appear convincing while containing subtle logical, algebraic, or numerical… ▽ More Large language models (LLMs) continue to struggle with mathematical reasoning, and common post-training pipelines often reduce each generated solution to a binary outcome: correct or incorrect. This perspective is limiting in practice, as failures in chain-of-thought (CoT) reasoning are frequently structured; solutions may appear convincing while containing subtle logical, algebraic, or numerical flaws. Meanwhile, reinforcement learning from human feedback (RLHF) variants that rely on large reward models or LLM-as-a-judge signals are often expensive, difficult to scale, and unstable to iterate. We propose a lightweight and pragmatic post-training pipeline that targets such structured errors under realistic compute budgets. Starting from supervised fine-tuning (SFT) on MetaMathQA-style CoT data, we introduce a compact MathVerifier that decomposes a candidate solution into a six-dimensional error profile and aggregates it into interpretable wrongness and absurdity scores. These verifier signals serve two roles: (i) mining hard negatives that are near-correct yet structurally flawed, and (ii) defining per-sample importance weights that emphasize the most informative preference pairs. We integrate both into an offline Direct Preference Optimization (DPO) objective via a verifier-guided weighted formulation. Experiments on a 1.5B-parameter Qwen2.5 model show that verifier-guided, weighted DPO yields more targeted improvements than vanilla SFT and unweighted DPO, particularly on problems where solutions are numerically close to correct but logically inconsistent, while avoiding the overhead of training large reward models or relying on external judges. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Spiral states, first-order transitions and specific heat multipeak phenomenon in $J_1$-$J_2$-$J_3$ model: A Wang-Landau algorithm study

Authors: Habib Ullah, Kun Li, Haoyu Lu, Youjin Deng, Wanzhou Zhang

Abstract: The classical $J_1$-$J_2$-$J_3$ Ising model on the honeycomb lattice is important for understanding frustrated magnetic phenomena in materials such as $FePS_3$ and $Ba_2CoTeO_6$, where diverse phases (e.g., striped, zigzag, armchair) and magnetization plateaus have been experimentally observed. To explain the experimental results, previous mean-field studies have explored its thermal phase transit… ▽ More The classical $J_1$-$J_2$-$J_3$ Ising model on the honeycomb lattice is important for understanding frustrated magnetic phenomena in materials such as $FePS_3$ and $Ba_2CoTeO_6$, where diverse phases (e.g., striped, zigzag, armchair) and magnetization plateaus have been experimentally observed. To explain the experimental results, previous mean-field studies have explored its thermal phase transitions, identifying armchair phases and striped phases, but their limitations call for more reliable numerical investigations. In this work, we systematically revisit the classical $J_1$-$J_2$-$J_3$ Ising model using the Wang-Landau algorithm. We find that the armchair (AC) phase, previously reported in mean-field and experimental studies, actually coexists with the spiral (SP) phase, with their combined degeneracy reaching 20-fold (4-fold for the AC states and 16-fold for the spiral states). The phase transitions and critical exponents are studied at different interaction values. We observe first-order phase transitions, continuous phase transitions, and even the multipeak phenomenon, i.e., Schottky-like specific-heat anomalies in frustrated systems. These results clarify the nature of phases and phase transitions in frustrated Ising systems and their exponents, and additionally provide inspiration for experimental efforts to search for the spiral state and Schottky-like anomalies. △ Less

Submitted 21 December, 2025; originally announced December 2025.

Comments: 12 pages, 12 figures

SG-RIFE: Semantic-Guided Real-Time Intermediate Flow Estimation with Diffusion-Competitive Perceptual Quality

Authors: Pan Ben Wong, Chengli Wu, Hanyue Lu

Abstract: Real-time Video Frame Interpolation (VFI) has long been dominated by flow-based methods like RIFE, which offer high throughput but often fail in complicated scenarios involving large motion and occlusion. Conversely, recent diffusion-based approaches (e.g., Consec. BB) achieve state-of-the-art perceptual quality but suffer from prohibitive latency, rendering them impractical for real-time applicat… ▽ More Real-time Video Frame Interpolation (VFI) has long been dominated by flow-based methods like RIFE, which offer high throughput but often fail in complicated scenarios involving large motion and occlusion. Conversely, recent diffusion-based approaches (e.g., Consec. BB) achieve state-of-the-art perceptual quality but suffer from prohibitive latency, rendering them impractical for real-time applications. To bridge this gap, we propose Semantic-Guided RIFE (SG-RIFE). Instead of training from scratch, we introduce a parameter-efficient fine-tuning strategy that augments a pre-trained RIFE backbone with semantic priors from a frozen DINOv3 Vision Transformer. We propose a Split-Fidelity Aware Projection Module (Split-FAPM) to compress and refine high-dimensional features, and a Deformable Semantic Fusion (DSF) module to align these semantic priors with pixel-level motion fields. Experiments on SNU-FILM demonstrate that semantic injection provides a decisive boost in perceptual fidelity. SG-RIFE outperforms diffusion-based LDMVFI in FID/LPIPS and achieves quality comparable to Consec. BB on complex benchmarks while running significantly faster, proving that semantic consistency enables flow-based methods to achieve diffusion-competitive perceptual quality in near real-time. △ Less

Submitted 20 December, 2025; originally announced December 2025.

Cross sections measurement of $e^+e^-\to Ξ(1530)^0\barΞ^0 + c.c.$ and search for $ψ(3770)\toΞ(1530)^0\barΞ^0 + c.c.$

Authors: BESIII Colaboration, :, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (680 additional authors not shown)

Abstract: Using $e^+e^-$ collision data collected with the BESIII detector corresponding to an integrated luminosity of 44.2 fb$^{-1}$, we measure the Born cross sections for the process $e^+e^- \to Ξ(1530)^{0} \barΞ^{0} + c.c.$ at forty-eight center-of-mass energies between 3.51 and 4.95 GeV. The potential signal from non-$D\bar{D}$ decays for $ψ(3770)$, i.e. $ψ(3770)\to Ξ(1530)^{0} \barΞ^{0}+ c.c.$, is in… ▽ More Using $e^+e^-$ collision data collected with the BESIII detector corresponding to an integrated luminosity of 44.2 fb$^{-1}$, we measure the Born cross sections for the process $e^+e^- \to Ξ(1530)^{0} \barΞ^{0} + c.c.$ at forty-eight center-of-mass energies between 3.51 and 4.95 GeV. The potential signal from non-$D\bar{D}$ decays for $ψ(3770)$, i.e. $ψ(3770)\to Ξ(1530)^{0} \barΞ^{0}+ c.c.$, is investigated by fitting the dressed cross section, no obvious signal is found. The upper limit of $\mathcal{B}(ψ(3770) \to Ξ(1530)^{0} \barΞ^{0}$) at 90\% confidence level is given. △ Less

Submitted 19 December, 2025; originally announced December 2025.

Comments: 9 Pages, 6 figures

SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples

Authors: Haoye Lu, Yaoliang Yu, Darren Ho

Abstract: In many real-world scenarios, obtaining fully observed samples is prohibitively expensive or even infeasible, while partial and noisy observations are comparatively easy to collect. In this work, we study distribution restoration with abundant noisy samples, assuming the corruption process is available as a black-box generator. We show that this task can be framed as a one-sided entropic optimal t… ▽ More In many real-world scenarios, obtaining fully observed samples is prohibitively expensive or even infeasible, while partial and noisy observations are comparatively easy to collect. In this work, we study distribution restoration with abundant noisy samples, assuming the corruption process is available as a black-box generator. We show that this task can be framed as a one-sided entropic optimal transport problem and solved via an EM-like algorithm. We further provide a test criterion to determine whether the true underlying distribution is recoverable under per-sample information loss, and show that in otherwise unrecoverable cases, a small number of clean samples can render the distribution largely recoverable. Building on these insights, we introduce SFBD-OMNI, a bridge model-based framework that maps corrupted sample distributions to the ground-truth distribution. Our method generalizes Stochastic Forward-Backward Deconvolution (SFBD; Lu et al., 2025) to handle arbitrary measurement models beyond Gaussian corruption. Experiments across benchmark datasets and diverse measurement settings demonstrate significant improvements in both qualitative and quantitative performance. △ Less

Submitted 18 December, 2025; originally announced December 2025.

The Preliminary Mauve Science Programme: Science themes identified for the first year of operations

Authors: Mauve Science Collaboration, Marcel Agueros, Don Dixon, Chuanfei Dong, Girish M. Duvvuri, Patrick Flanagan, Christopher Johns-Krull, Hongpeng Lu, Hiroyuki Maehara, Kosuke Namekata, Alejandro Nunez, Elena Pancino, Sharmila Rani, Anusha Ravikumar, T. A. A. Sigut, Keivan Stassun, Jamie Stewart, Krisztián Vida, Emma Whelan, Benjamin Wilcock, Sharafina Razin, Arianna Saba, Giovanna Tinetti, Marcell Tessenyi, Jonathan Tennyson

Abstract: Mauve is a low-cost small satellite developed and operated by Blue Skies Space Ltd. The payload features a 13 cm telescope connected with a fibre that feeds into a UV-Vis spectrometer. The detector covers the 200-700 nm range in a single shot, obtaining low resolution spectra at R~20-65. Mauve has launched on 28th November 2025, reaching a 510 km Low-Earth Sun-synchronous orbit. The satellite will… ▽ More Mauve is a low-cost small satellite developed and operated by Blue Skies Space Ltd. The payload features a 13 cm telescope connected with a fibre that feeds into a UV-Vis spectrometer. The detector covers the 200-700 nm range in a single shot, obtaining low resolution spectra at R~20-65. Mauve has launched on 28th November 2025, reaching a 510 km Low-Earth Sun-synchronous orbit. The satellite will enable UV and visible observations of a variety of stellar objects in our Galaxy, filling the gaps in the ultraviolet space-based data. The researchers that have already joined the mission have defined the science themes, observational strategy and targets that Mauve will observe in the first year of operations. To date, 10 science themes have been developed by the Mauve science collaboration for year 1, with observational strategies that include both long duration monitoring and short cadence snapshots. Here, we describe these themes and the science that Mauve will undertake in its first year of operations. △ Less

Submitted 18 December, 2025; originally announced December 2025.

Comments: 16 pages, 15 figures, 2 tables

A Machine-Learning Approach for Identifying CME-Associated Stellar Flares in TESS Observations

Authors: Yu Shi, Hong-Peng Lu, Li-Yun Zhang, Tian-Hao Su, Chao Tan

Abstract: Coronal mass ejections (CMEs) are major drivers of stellar space weather and can strongly influence the habitability of exoplanets. However, compared to the frequent occurrence of white-light flares, confirmed stellar CMEs remain extremely rare. Whether such flares are commonly accompanied by CMEs is a key question for solar-stellar comparative studies. Using Sun-as-a-star soft X-ray flare light c… ▽ More Coronal mass ejections (CMEs) are major drivers of stellar space weather and can strongly influence the habitability of exoplanets. However, compared to the frequent occurrence of white-light flares, confirmed stellar CMEs remain extremely rare. Whether such flares are commonly accompanied by CMEs is a key question for solar-stellar comparative studies. Using Sun-as-a-star soft X-ray flare light curves observed by the GOES XRS 1--8~Å channel, we compiled a sample of 1,766 M-class and larger solar flares and extracted features with both deep convolutional neural networks and manual methods. Five machine-learning classifiers were trained to distinguish eruptive from confined flares, with the random forest model achieving the best performance (true skill statistic; TSS = 0.31). This TSS value indicates that the model possesses a moderate ability to discriminate between eruptive and confined flares. Normalized white-light and GOES XRS flare light curves show broadly consistent temporal evolution, reflecting their shared energy-release history and supporting a probabilistic transfer of the model to white-light flare data. We applied the best-performing RF model to 41,405 TESS-detected flares on FGKM-type main-sequence stars, predicting that approximately 47% of events show CME-like morphological characteristics, with the model-implied intrinsic association fraction lying in the range 35%--60%. Intriguingly, the CME occurrence rate decreases with increasing flare energy, indicating that the most energetic flares may be more strongly confined by overlying magnetic fields. These results provide new insight into flare-CME connections in diverse stellar environments and have important implications for assessing the impact of stellar eruptive activity on exoplanetary atmospheres. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Comments: Accepted by ApJS

Measurements of the Absolute Branching Fraction of the Semileptonic Decay $\mathbf{Ξ^{-}\rightarrow Λe^- \barν_{e}}$ and the Axial Charge of the $\mathbfΞ^{-}$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (683 additional authors not shown)

Abstract: Using $(10087\pm44)\times10^6$ $J/ψ$ events collected with the BESIII detector, we study the semileptonic decay $Ξ^{-}\rightarrow Λe^- \barν_{e}$ for the first time at an electron-positron collider. The absolute branching fraction is determined for the first time to be $(3.60\pm0.40_{\mathrm{stat}}\pm0.10_{\mathrm{syst}})\times10^{-4}$, which is 3.9 standard deviations below the world average. In… ▽ More Using $(10087\pm44)\times10^6$ $J/ψ$ events collected with the BESIII detector, we study the semileptonic decay $Ξ^{-}\rightarrow Λe^- \barν_{e}$ for the first time at an electron-positron collider. The absolute branching fraction is determined for the first time to be $(3.60\pm0.40_{\mathrm{stat}}\pm0.10_{\mathrm{syst}})\times10^{-4}$, which is 3.9 standard deviations below the world average. In addition, using an 11-dimensional angular analysis, the axial-vector to vector coupling $g_{av}$ is determined to be $0.18\pm0.07_{\mathrm{stat}}\pm0.02_{\mathrm{syst}}$. These results are used to test various SU(3)-flavour effective models. Under the SU(3)-flavour symmetry limit, the axial charge is found to be $g_A^H = 0.22\pm0.08_{\mathrm{stat}}\pm0.02_{\mathrm{syst}}$. Despite using only 5\% of the statistics of previous experiments, this analysis achieves a comparable precision for the axial charge. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Comments: 10 pages, 4 figures

Uni-Parser Technical Report

Authors: Xi Fang, Haoyi Tao, Shuwen Yang, Suyang Zhong, Haocheng Lu, Han Lyu, Chaozheng Huang, Xinyu Li, Linfeng Zhang, Guolin Ke

Abstract: This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-modal alignments across text, equations, ta… ▽ More This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-modal alignments across text, equations, tables, figures, and chemical structures, while remaining easily extensible to emerging modalities. The system incorporates adaptive GPU load balancing, distributed inference, dynamic module orchestration, and configurable modes that support either holistic or modality-specific parsing. Optimized for large-scale cloud deployment, Uni-Parser achieves a processing rate of up to 20 PDF pages per second on 8 x NVIDIA RTX 4090D GPUs, enabling cost-efficient inference across billions of pages. This level of scalability facilitates a broad spectrum of downstream applications, ranging from literature retrieval and summarization to the extraction of chemical structures, reaction schemes, and bioactivity data, as well as the curation of large-scale corpora for training next-generation large language models and AI4Science models. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Search for the decays $X(3872)\to K_{S}^{0}K^{\pm}π^{\mp}$ and $K^*(892)\bar{K}$ at BESIII

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (684 additional authors not shown)

Abstract: Using a 10.9 fb$^{-1}$ data sample collected by the BESIII detector at center-of-mass energies from 4.16 to 4.34 GeV, we search for the charmless decays $X(3872) \to K_{S}^{0}K^{\pm}π^{\mp}$ and $K^*(892)\bar{K}$, where the $X(3872)$ is produced via the radiative process $e^+e^- \to γX(3872)$. No significant signal is observed. We set upper limits on the relative branching fractions… ▽ More Using a 10.9 fb$^{-1}$ data sample collected by the BESIII detector at center-of-mass energies from 4.16 to 4.34 GeV, we search for the charmless decays $X(3872) \to K_{S}^{0}K^{\pm}π^{\mp}$ and $K^*(892)\bar{K}$, where the $X(3872)$ is produced via the radiative process $e^+e^- \to γX(3872)$. No significant signal is observed. We set upper limits on the relative branching fractions $\mathcal{B}[X(3872)\to K_S K^{\pm} π^{\mp}]/\mathcal{B}[X(3872)\toπ^+π^- J/ψ] <0.07$ and $\mathcal{B}[X(3872)\to K^* (892)\bar{K}]/\mathcal{B}[X(3872)\to π^+π^- J/ψ] <0.10$ at the 90$\%$ confidence level. Additionally, upper limits on the product of the cross section $σ[e^+e^-\toγX(3872)]$ and the branching fractions $\mathcal{B}[X(3872)\to K_{S}^{0}K^{\pm}π^{\mp}]$ and $\mathcal{B}[X(3872)\to K^*(892)\bar{K}]$ are reported at each energy point. In all cases, $K^*(892)\bar{K}$ refers to the sum of the modes $K^*(892)^+K^{-}+\text{c.c.}$ and $K^*(892)^0\bar{K}^0+\text{c.c.}$, where c.c. denotes the corresponding charge-conjugate modes. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Comments: 11 pages, 1 figure

APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication

Authors: Zhenhao Yin, Hanbing Yan, Huishu Lu, Jing Xiong, Xiangyu Li, Rui Mei, Tianning Zang

Abstract: Large-scale, standardized datasets for Advanced Persistent Threat (APT) research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases (reconciling approximately 11.22\% of inconsistent names) and applies graph-feature deduplication -- reducing the subset of stat… ▽ More Large-scale, standardized datasets for Advanced Persistent Threat (APT) research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases (reconciling approximately 11.22\% of inconsistent names) and applies graph-feature deduplication -- reducing the subset of statically analyzable executables by 47.55\% while retaining behaviorally distinct variants. APT-ClaritySet comprises: (i) APT-ClaritySet-Full, the complete pre-deduplication collection with 34{,}363 malware samples attributed to 305 APT groups (2006 - early 2025); (ii) APT-ClaritySet-Unique, the deduplicated release with 25{,}923 unique samples spanning 303 groups and standardized attributions; and (iii) APT-ClaritySet-FuncReuse, a function-level resource that includes 324{,}538 function-reuse clusters (FRCs) enabling measurement of inter-/intra-group sharing, evolution, and tooling lineage. By releasing these components and detailing the alias normalization and scalable deduplication pipeline, this work provides a high-fidelity, reproducible foundation for quantitative studies of APT patterns, evolution, and attribution. △ Less

Submitted 16 December, 2025; originally announced December 2025.

Comments: 13 pages, 11 figures

Odd-dimensional Extremal Rotating Black Holes with All Equal Angular Momenta and Small Electric Charges

Authors: Qi-Yuan Mao, H. Lu

Abstract: We consider Einstein-Maxwell gravity in diverse dimensions and construct the small charge perturbation to the extremal rotating black holes with all equal angular momenta in odd $D=2n+1$ dimensions. Exact solutions exist at the next-to-leading order (NLO), and they are analytic, allowing us to obtain the charge corrections to thermodynamic quantities at this order. Irrational exponents in the near… ▽ More We consider Einstein-Maxwell gravity in diverse dimensions and construct the small charge perturbation to the extremal rotating black holes with all equal angular momenta in odd $D=2n+1$ dimensions. Exact solutions exist at the next-to-leading order (NLO), and they are analytic, allowing us to obtain the charge corrections to thermodynamic quantities at this order. Irrational exponents in the near-horizon power-series expansion emerge at the next-to-next-to-leading order (NNLO). We show, by numerical computation, that these horizon geometries can indeed be integrated out to asymptotic Minkowski spacetime, thereby proving the existence of the unusual singular horizon behavior of the extremal charged rotating black holes. △ Less

Submitted 16 December, 2025; originally announced December 2025.

Comments: 18 pages, 4 figures

Measurements of the branching fractions of $χ_{cJ}\to φφη, φφη^{\prime}$ and $φK^+K^-η$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (681 additional authors not shown)

Abstract: Using a sample of $(2712.4 \pm 14.3)\times 10^6 ~ψ$(3686) events collected by the BESIII detector at the BEPCII collider, we measure the branching fractions of the decays $χ_{cJ}\to φφη,~φφη^{\prime}$, and~$φK^+K^-η$ ($J = 0, 1, 2$). The obtained branching fractions are $\mathcal{B}(χ_{c0} \to φφη) = (7.40 \pm 0.23 \pm 0.55)\times10^{-4}$,… ▽ More Using a sample of $(2712.4 \pm 14.3)\times 10^6 ~ψ$(3686) events collected by the BESIII detector at the BEPCII collider, we measure the branching fractions of the decays $χ_{cJ}\to φφη,~φφη^{\prime}$, and~$φK^+K^-η$ ($J = 0, 1, 2$). The obtained branching fractions are $\mathcal{B}(χ_{c0} \to φφη) = (7.40 \pm 0.23 \pm 0.55)\times10^{-4}$, $\mathcal{B}(χ_{c1} \to φφη) = (3.33 \pm 0.14 \pm 0.25)\times10^{-4}$, $\mathcal{B}(χ_{c2} \to φφη) = (5.46 \pm 0.17 \pm 0.40)\times10^{-4}$, $\mathcal{B}(χ_{c0} \to φφη^\prime) = (2.96 \pm 0.23 \pm 0.29)\times10^{-4}$, $\mathcal{B}(χ_{c1} \to φφη^\prime) = (0.69 \pm 0.10 \pm 0.08)\times10^{-4}$, $\mathcal{B}(χ_{c2} \to φφη^\prime) = (0.65 \pm 0.09 \pm 0.07)\times10^{-4}$, $\mathcal{B}(χ_{c0} \to φK^+K^-η) = (1.23 \pm 0.08 \pm 0.10)\times10^{-4}$, $\mathcal{B}(χ_{c1} \to φK^+K^-η) = (1.00 \pm 0.07 \pm 0.07)\times10^{-4}$, and $\mathcal{B}(χ_{c2} \to φK^+K^-η) = (1.82 \pm 0.09 \pm 0.14)\times10^{-4}$, where $K^+K^-$ is not from the decay of a $φ$ meson, the first uncertainties are statistical and the second systematic. The branching fractions of $χ_{cJ}\to φφη$ are measured with precision improved by factors of $1.5-1.9$, and those of $χ_{cJ}\to φφη^\prime$ and $φK^+K^-η$ are measured for the first time. △ Less

Submitted 16 December, 2025; originally announced December 2025.

Comments: 12 pages, 8 figures, 3 tables

Report number: BAM-00909

HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

Authors: HyperAI Team, Yuchen Liu, Kaiyang Han, Zhiqiang Xia, Yuhang Dong, Chen Song, Kangyu Tang, Jiaming Xu, Xiushi Feng, WenXuan Yu, Li Peng, Mingyang Wang, Kai Wang, Changpeng Yang, Yang Li, Haoyu Lu, Hao Wang, Bingna Xu, Guangyao Liu, Long Huang, Kaibin Guo, Jinyang Wu, Dan Wu, Hongzhen Wang, Peng Zhou , et al. (4 additional authors not shown)

Abstract: Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive la… ▽ More Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive latency and memory consumption when processing high-resolution inputs.To address these challenges, we introduce HyperVL, an efficient multimodal large language model tailored for on-device inference. HyperVL adopts an image-tiling strategy to cap peak memory usage and incorporates two novel techniques: (1) a Visual Resolution Compressor (VRC) that adaptively predicts optimal encoding resolutions to eliminate redundant computation, and (2) Dual Consistency Learning (DCL), which aligns multi-scale ViT encoders within a unified framework, enabling dynamic switching between visual branches under a shared LLM. Extensive experiments demonstrate that HyperVL achieves state-of-the-art performance among models of comparable size across multiple benchmarks. Furthermore, it significantly significantly reduces latency and power consumption on real mobile devices, demonstrating its practicality for on-device multimodal inference. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Comments: Technical report of Xiaomi HyperAI Team

Linear magnetoresistance of two-dimensional massless Dirac fermions in the quantum limit

Authors: Xiao-Bin Qiang, Han-Yi Xu, Ren-Jie Tong, Shuai Li, Zi-Xuan Gao, Peng-Lu Zhao, Hai-Zhou Lu

Abstract: Linear magnetoresistance is a hallmark of 3D Weyl metals in the quantum limit. Recently, a pronounced linear magnetoresistance has also been observed in 2D graphene [Xin et al., Nature 616, 270 (2023)]. However, a comprehensive theoretical understanding remains elusive. By employing the self-consistent Born approximation, we derive the analytical expressions for the magnetoresistivity of 2D massle… ▽ More Linear magnetoresistance is a hallmark of 3D Weyl metals in the quantum limit. Recently, a pronounced linear magnetoresistance has also been observed in 2D graphene [Xin et al., Nature 616, 270 (2023)]. However, a comprehensive theoretical understanding remains elusive. By employing the self-consistent Born approximation, we derive the analytical expressions for the magnetoresistivity of 2D massless Dirac fermions in the quantum limit. Notably, our result recovers the minimum conductivity in the clean limit and reveals a linear dependence of resistivity on the magnetic field for Gaussian impurity potentials, in quantitative agreement with experiments. These findings shed light on the magnetoresistance behavior of 2D Dirac fermions under ultra-high magnetic fields. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Comments: 6 pages, 3 figures

Journal ref: Phys. Rev. B 112, 224208 (2025)

From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents

Authors: Dezhi Ran, Zhi Gong, Yuzhe Guo, Mengzhou Wu, Yuan Cao, Haochuan Lu, Hengyu Zhang, Xia Zeng, Gang Cao, Liangchao Yao, Yuetang Deng, Wei Yang, Tao Xie

Abstract: While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooked. Our motivating study reveals that inefficient UI representation creates a critical performance bottleneck. However, UI representation optimization, formulated as the task of automatically generating programs that tran… ▽ More While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooked. Our motivating study reveals that inefficient UI representation creates a critical performance bottleneck. However, UI representation optimization, formulated as the task of automatically generating programs that transform UI representations, faces two unique challenges. First, the lack of Boolean oracles, which traditional program synthesis uses to decisively validate semantic correctness, poses a fundamental challenge to co-optimization of token efficiency and completeness. Second, the need to process large, complex UI trees as input while generating long, compositional transformation programs, making the search space vast and error-prone. Toward addressing the preceding limitations, we present UIFormer, the first automated optimization framework that synthesizes UI transformation programs by conducting constraint-based optimization with structured decomposition of the complex synthesis task. First, UIFormer restricts the program space using a domain-specific language (DSL) that captures UI-specific operations. Second, UIFormer conducts LLM-based iterative refinement with correctness and efficiency rewards, providing guidance for achieving the efficiency-completeness co-optimization. UIFormer operates as a lightweight plugin that applies transformation programs for seamless integration with existing LLM agents, requiring minimal modifications to their core logic. Evaluations across three UI navigation benchmarks spanning Android and Web platforms with five LLMs demonstrate that UIFormer achieves 48.7% to 55.8% token reduction with minimal runtime overhead while maintaining or improving agent performance. Real-world industry deployment at WeChat further validates the practical impact of UIFormer. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Amplitude Analysis and Branching Fraction Measurement of $D^+ \to π^+π^0π^0$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (684 additional authors not shown)

Abstract: We present the first amplitude analysis of the hadronic decay $D^+\toπ^+π^0π^0$, using $e^{+}e^{-}$ collision data collected with the BESIII detector at a center-of-mass energy of 3.773~GeV, corresponding to an integrated luminosity of 20.3~fb$^{-1}$. The fit fractions of the intermediate processes are measured, in which the $D^+ \to ρ(770)^+π^0$ component is found to be dominant with a branching… ▽ More We present the first amplitude analysis of the hadronic decay $D^+\toπ^+π^0π^0$, using $e^{+}e^{-}$ collision data collected with the BESIII detector at a center-of-mass energy of 3.773~GeV, corresponding to an integrated luminosity of 20.3~fb$^{-1}$. The fit fractions of the intermediate processes are measured, in which the $D^+ \to ρ(770)^+π^0$ component is found to be dominant with a branching fraction of $(3.08\kern0.15em\pm\kern0.15em0.10_{\rm stat.}\pm0.05_{\rm syst.})\times10^{-3}$. Based on the amplitude analysis, the branching fraction of $D^+ \to π^+π^0π^0$ is measured to be $(4.84\kern0.1em\pm\kern0.1em0.05_{\rm stat.}\kern0.1em\pm\kern0.1em0.05_{\rm syst.})\times10^{-3}$. In addition, the CP asymmetries, both for specific amplitudes and integrated over the entire phase space, are measured. △ Less

Submitted 13 December, 2025; originally announced December 2025.

JAX-in-Cell: A Differentiable Particle-in-Cell Code for Plasma Physics Applications

Authors: Longyu Ma, Rogerio Jorge, Hongke Lu, Aaron Tran, Christopher Woolford

Abstract: JAX-in-Cell is a fully electromagnetic, multispecies, and relativistic 1D3V Particle-in-Cell (PIC) framework implemented entirely in JAX. It provides a modern, Python-based alternative to traditional PIC frameworks. It leverages Just-In-Time compilation and automatic vectorization to achieve the performance of traditional compiled codes on CPUs, GPUs, and TPUs. The resulting framework bridges the… ▽ More JAX-in-Cell is a fully electromagnetic, multispecies, and relativistic 1D3V Particle-in-Cell (PIC) framework implemented entirely in JAX. It provides a modern, Python-based alternative to traditional PIC frameworks. It leverages Just-In-Time compilation and automatic vectorization to achieve the performance of traditional compiled codes on CPUs, GPUs, and TPUs. The resulting framework bridges the gap between educational scripts and production codes, providing a testbed for differentiable physics and AI integration that enables end-to-end gradient-based optimization. The code solves the Vlasov-Maxwell system on a staggered Yee lattice with either periodic, reflective, or absorbing boundary conditions, allowing both an explicit Boris solver and an implicit Crank-Nicolson method via Picard iteration to ensure energy conservation. Here, we detail the numerical methods employed, validate against standard benchmarks, and showcase the use of its auto-differentiation capabilities. △ Less

Submitted 12 December, 2025; originally announced December 2025.

Comments: 5 pages, 6 figures

Benchmarking the Generality of Vision-Language-Action Models

Authors: Pranav Guruprasad, Sudipta Chowdhury, Harsh Sikka, Mridul Sharma, Helen Lu, Sean Rivera, Aryan Khurana, Hangliang Ren, Yangyue Wang

Abstract: Generalist multimodal agents are expected to unify perception, language, and control - operating robustly across diverse real world domains. However, current evaluation practices remain fragmented across isolated benchmarks, making it difficult to assess whether today's foundation models truly generalize beyond their training distributions. We introduce MultiNet v1.0, a unified benchmark for measu… ▽ More Generalist multimodal agents are expected to unify perception, language, and control - operating robustly across diverse real world domains. However, current evaluation practices remain fragmented across isolated benchmarks, making it difficult to assess whether today's foundation models truly generalize beyond their training distributions. We introduce MultiNet v1.0, a unified benchmark for measuring the cross domain generality of vision language models (VLMs) and vision language action models (VLAs) across six foundational capability regimes. Visual grounding, spatial reasoning, tool use, physical commonsense, multi agent coordination, and continuous robot control. Evaluating GPT 5, Pi0, and Magma, we find that no model demonstrates consistent generality. All exhibit substantial degradation on unseen domains, unfamiliar modalities, or cross domain task shifts despite strong performance within their training distributions.These failures manifest as modality misalignment, output format instability, and catastrophic knowledge degradation under domain transfer.Our findings reveal a persistent gap between the aspiration of generalist intelligence and the actual capabilities of current foundation models.MultiNet v1.0 provides a standardized evaluation substrate for diagnosing these gaps and guiding the development of future generalist agents.Code, data, and leaderboards are publicly available. △ Less

Submitted 12 December, 2025; originally announced December 2025.

Comments: 23 pages, 7 figures, and 1 table

Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy

Authors: Kechun Xu, Zhenjie Zhu, Anzhe Chen, Shuqi Zhao, Qing Huang, Yifei Yang, Haojian Lu, Rong Xiong, Masayoshi Tomizuka, Yue Wang

Abstract: The pursuit of out-of-distribution generalization in Vision-Language-Action (VLA) models is often hindered by catastrophic forgetting of the Vision-Language Model (VLM) backbone during fine-tuning. While co-training with external reasoning data helps, it requires experienced tuning and data-related overhead. Beyond such external dependencies, we identify an intrinsic cause within VLA datasets: mod… ▽ More The pursuit of out-of-distribution generalization in Vision-Language-Action (VLA) models is often hindered by catastrophic forgetting of the Vision-Language Model (VLM) backbone during fine-tuning. While co-training with external reasoning data helps, it requires experienced tuning and data-related overhead. Beyond such external dependencies, we identify an intrinsic cause within VLA datasets: modality imbalance, where language diversity is much lower than visual and action diversity. This imbalance biases the model toward visual shortcuts and language forgetting. To address this, we introduce BayesVLA, a Bayesian factorization that decomposes the policy into a visual-action prior, supporting seeing-to-act, and a language-conditioned likelihood, enabling prompt-to-specify. This inherently preserves generalization and promotes instruction following. We further incorporate pre- and post-contact phases to better leverage pre-trained foundation models. Information-theoretic analysis formally validates our effectiveness in mitigating shortcut learning. Extensive experiments show superior generalization to unseen instructions, objects, and environments compared to existing methods. Project page is available at: https://xukechun.github.io/papers/BayesVLA. △ Less

Submitted 11 December, 2025; originally announced December 2025.

Estimating stellar atmospheric parameters and elemental abundances using fully connected residual network

Authors: Shuo Li, Yin-Bi Li, A-Li Luo, Jun-Chao Liang, Hai-Ling Lu, Hugh R. A. Jones

Abstract: Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra. However, these methods are sensitive to noise and unsuitable for ultra-low-resolution data. Given that the Chinese Space Station Telescope (CSST) will acquire large volumes of ultra-low-resolution spectra, developing effective methods for ultra-l… ▽ More Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra. However, these methods are sensitive to noise and unsuitable for ultra-low-resolution data. Given that the Chinese Space Station Telescope (CSST) will acquire large volumes of ultra-low-resolution spectra, developing effective methods for ultra-low-resolution spectral analysis is crucial. In this work, we investigated the Fully Connected Residual Network (FCResNet) for simultaneously estimating atmospheric parameters ($T_\text{eff}$, $\log g$, [Fe/H]) and elemental abundances ([C/Fe], [N/Fe], [Mg/Fe]). We trained and evaluated FCResNet using CSST-like spectra (\textit{R} $\sim$ 200) generated by degrading LAMOST spectra (\textit{R} $\sim$ 1,800), with reference labels from APOGEE. FCResNet significantly outperforms traditional machine learning methods (KNN, XGBoost, SVR) and CNN in prediction precision. For spectra with g-band signal-to-noise ratio greater than 20, FCResNet achieves precisions of 78 K, 0.15 dex, 0.08 dex, 0.05 dex, 0.10 dex, and 0.05 dex for $T_\text{eff}$, $\log g$, [Fe/H], [C/Fe], [N/Fe] and [Mg/Fe], respectively, on the test set. FCResNet processes one million spectra in only 42 seconds while maintaining a simple architecture with just 348 KB model size. These results suggest that FCResNet is a practical and promising tool for processing the large volume of ultra-low-resolution spectra that will be obtained by CSST in the future. △ Less

Submitted 11 December, 2025; originally announced December 2025.

Journal ref: Res. Astron. Astrophys. 25 125009 (2025)

Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, $T_\text{eff}$, log $g$, [M/H] and [$α$/M]

Authors: Shuo Li, Yin-Bi Li, A-Li Luo, Jun-Chao Liang, You-Fen Wang, Jing Chen, Shuo Zhang, Mao-Sheng Xiang, Hugh R. A. Jones, Zhong-Rui Bai, Xiao-Xiao Ma, Yun-Jin Zhang, Hai-Ling Lu

Abstract: Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog using techniques including deep learning and color-magnitude diagrams to remove 22,496 non-M spectra, correct 2,078 dwarf/giant classifications, and update 12,90… ▽ More Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog using techniques including deep learning and color-magnitude diagrams to remove 22,496 non-M spectra, correct 2,078 dwarf/giant classifications, and update 12,900 radial velocities. This resulted in a cleaner catalog containing 870,518 M-type spectra (820,493 dwarfs, 50,025 giants). Second, applying a label transfer strategy using values from APOGEE DR16 for parameter prediction with a ten-fold cross-validated CNN ensemble architecture, we predicted $T_\text{eff}$, $\log g$, [M/H], and [$α$/M] separately for M dwarfs and giants. The average internal errors for M dwarfs/giants are respectively: $T_\text{eff}$ 30/17 K, log $g$ 0.07/0.07 dex, [M/H] 0.07/0.05 dex, and [$α$/M] 0.02/0.02 dex. Comparison with APOGEE demonstrates external precisions of 34/14 K, 0.12/0.07 dex, 0.09/0.04 dex, and 0.03/0.02 dex for M dwarfs/giants, which represents precision improvements of over 20\% for M dwarfs and over 50\% for M giants compared to previous literature results. The catalog is available at https://nadc.china-vo.org/res/r101668/. △ Less

Submitted 11 December, 2025; originally announced December 2025.

Journal ref: ApJS 281 58 (2025)

Black Hole Thermodynamics without Black Hole Solutions

Authors: Meng-Nan Yang, Guan-Yi Lu, H. Lu

Abstract: We consider the string-theory inspired Einstein-Maxwell-Maxwell-dilaton theory (EMMD) and show that we can derive the complete set of thermodynamic quantities of charged black holes, without having to solve for the black hole solutions. We argue that the technique can be applied more broadly to string theories, providing an accessible method for determining the thermodynamic properties of large cl… ▽ More We consider the string-theory inspired Einstein-Maxwell-Maxwell-dilaton theory (EMMD) and show that we can derive the complete set of thermodynamic quantities of charged black holes, without having to solve for the black hole solutions. We argue that the technique can be applied more broadly to string theories, providing an accessible method for determining the thermodynamic properties of large classes of black holes for which exact solutions are typically unavailable. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: Latex, 10 pages, 2 plots grouped into 1 figure

SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environments

Authors: Haoye Lu, Pavan Seshadri, Kaheer Suleman

Abstract: Long-term planning in complex, text-based environments presents significant challenges due to open-ended action spaces, ambiguous observations, and sparse feedback. Recent research suggests that large language models (LLMs) encode rich semantic knowledge about the world, which can be valuable for guiding agents in high-level reasoning and planning across both embodied and purely textual settings.… ▽ More Long-term planning in complex, text-based environments presents significant challenges due to open-ended action spaces, ambiguous observations, and sparse feedback. Recent research suggests that large language models (LLMs) encode rich semantic knowledge about the world, which can be valuable for guiding agents in high-level reasoning and planning across both embodied and purely textual settings. However, existing approaches often depend heavily on querying LLMs during training and inference, making them computationally expensive and difficult to deploy efficiently. In addition, these methods typically employ a pretrained, unaltered LLM whose parameters remain fixed throughout training, providing no opportunity for adaptation to the target task. To address these limitations, we introduce SCOPE (Subgoal-COnditioned Pretraining for Efficient planning), a one-shot hierarchical planner that leverages LLM-generated subgoals only at initialization to pretrain a lightweight student model. Unlike prior approaches that distill LLM knowledge by repeatedly prompting the model to adaptively generate subgoals during training, our method derives subgoals directly from example trajectories. This design removes the need for repeated LLM queries, significantly improving efficiency, though at the cost of reduced explainability and potentially suboptimal subgoals. Despite their suboptimality, our results on the TextCraft environment show that LLM-generated subgoals can still serve as a strong starting point for hierarchical goal decomposition in text-based planning tasks. Compared to the LLM-based hierarchical agent ADaPT (Prasad et al., 2024), which achieves a 0.52 success rate, our method reaches 0.56 and reduces inference time from 164.4 seconds to just 3.0 seconds. △ Less

Submitted 10 December, 2025; originally announced December 2025.

UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving

Authors: Hao Lu, Ziyang Liu, Guangfeng Jiang, Yuanfei Luo, Sheng Chen, Yangang Zhang, Ying-Cong Chen

Abstract: Autonomous driving (AD) systems struggle in long-tail scenarios due to limited world knowledge and weak visual dynamic modeling. Existing vision-language-action (VLA)-based methods cannot leverage unlabeled videos for visual causal learning, while world model-based methods lack reasoning capabilities from large language models. In this paper, we construct multiple specialized datasets providing re… ▽ More Autonomous driving (AD) systems struggle in long-tail scenarios due to limited world knowledge and weak visual dynamic modeling. Existing vision-language-action (VLA)-based methods cannot leverage unlabeled videos for visual causal learning, while world model-based methods lack reasoning capabilities from large language models. In this paper, we construct multiple specialized datasets providing reasoning and planning annotations for complex scenarios. Then, a unified Understanding-Generation-Planning framework, named UniUGP, is proposed to synergize scene reasoning, future video generation, and trajectory planning through a hybrid expert architecture. By integrating pre-trained VLMs and video generation models, UniUGP leverages visual dynamics and semantic reasoning to enhance planning performance. Taking multi-frame observations and language instructions as input, it produces interpretable chain-of-thought reasoning, physically consistent trajectories, and coherent future videos. We introduce a four-stage training strategy that progressively builds these capabilities across multiple existing AD datasets, along with the proposed specialized datasets. Experiments demonstrate state-of-the-art performance in perception, reasoning, and decision-making, with superior generalization to challenging long-tail situations. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: Project Page: https://seed-uniugp.github.io/

Anti-Ramsey Number of Stars in 3-uniform hypergraphs

Authors: Hongliang Lu, Xinyue Luo, Xinxin Ma

Abstract: An edge-colored hypergraph is called \emph{a rainbow hypergraph} if all the colors on its edges are distinct. Given two positive integers $n,r$ and an $r$-uniform hypergraph $\mathcal{G}$, the anti-Ramsey number $ar_r(n,\mathcal{G})$ is defined to be the minimum number of colors $t$ such that there exists a rainbow copy of $\mathcal{G}$ in any exactly $t$-edge-coloring of the complete $r$-uniform… ▽ More An edge-colored hypergraph is called \emph{a rainbow hypergraph} if all the colors on its edges are distinct. Given two positive integers $n,r$ and an $r$-uniform hypergraph $\mathcal{G}$, the anti-Ramsey number $ar_r(n,\mathcal{G})$ is defined to be the minimum number of colors $t$ such that there exists a rainbow copy of $\mathcal{G}$ in any exactly $t$-edge-coloring of the complete $r$-uniform hypergraph of order $n$. Let $ \mathcal{F}_k $ denote the 3-graph ($k$-star) consisting of $k$ edges sharing exactly one vertex. Tang, Li and Yan \cite{YTG} determined the value of $ar_3(n,\mathcal{F}_3)$ when $n\geq 20$. In this paper, we determine the anti-Ramsey number $ar_3(n,\mathcal{F}_{k+1})$, where $k\geq 3$ and $n> \frac{5}{2}k^3+\frac{15}{2}k^2+26k-3$. △ Less

Submitted 10 December, 2025; originally announced December 2025.

First measurement of the absolute branching fractions of $Σ^+$ nonleptonic decays and test of the $ΔI = 1/2$ rule % $Σ^+ \to p π^0$ and $Σ^+ \to n π^+$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (689 additional authors not shown)

Abstract: Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ events collected by the BESIII detector at the center-of-mass energy $\sqrt{s} = 3.097$ GeV, the first absolute measurement of the branching fractions for the decays $Σ^+ \to p π^0$ and $Σ^+ \to n π^+$ is performed. The branching fractions are determined to be $B_{Σ^+ \to p π^0} = (49.79 \pm 0.06 \pm 0.22)\%$ and… ▽ More Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ events collected by the BESIII detector at the center-of-mass energy $\sqrt{s} = 3.097$ GeV, the first absolute measurement of the branching fractions for the decays $Σ^+ \to p π^0$ and $Σ^+ \to n π^+$ is performed. The branching fractions are determined to be $B_{Σ^+ \to p π^0} = (49.79 \pm 0.06 \pm 0.22)\%$ and $B_{Σ^+ \to n π^+} = (49.87 \pm 0.05 \pm 0.29)\%$, where the first uncertainties are statistical and the second systematic. These results show significant deviations from the PDG values for both decays, with differences of 4.4$σ$ for $Σ^+ \to p π^0$ and 3.4$σ$ for $Σ^+ \to n π^+$. Furthermore, the $ΔI = 1/2$ rule is tested in nonleptonic $Σ^\pm$ decays. The observed results deviate from zero by more than $5σ$, indicating the presence of the $ΔI = 3/2$ transition amplitude in the $Σ$ hyperon decays. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks

Authors: Xinye Cao, Yihan Lin, Guoshun Nan, Qinchuan Zhou, Yuhang Luo, Yurui Gao, Zeliang Zhang, Haolang Lu, Qimei Cui, Yanzhao Hou, Xiaofeng Tao, Tony Q. S. Quek

Abstract: Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability and adaptability required for the complexity of sixth-generation (6G) networks. However, the distributed architecture, high openness, and deep heterogeneity of 6G networks expand the attack surface and pose unprecedented security challenges. To address… ▽ More Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability and adaptability required for the complexity of sixth-generation (6G) networks. However, the distributed architecture, high openness, and deep heterogeneity of 6G networks expand the attack surface and pose unprecedented security challenges. To address this, security automation aims to enable intelligent security management across dynamic and complex environments, serving as a key capability for securing 6G ZTNs. Despite its promise, implementing security automation in 6G ZTNs presents two primary challenges: 1) automating the lifecycle from security strategy generation to validation and update under real-world, parallel, and adversarial conditions, and 2) adapting security strategies to evolving threats and dynamic environments. This motivates us to propose SecLoop and SA-GRPO. SecLoop constitutes the first fully automated framework that integrates large language models (LLMs) across the entire lifecycle of security strategy generation, orchestration, response, and feedback, enabling intelligent and adaptive defenses in dynamic network environments, thus tackling the first challenge. Furthermore, we propose SA-GRPO, a novel security-aware group relative policy optimization algorithm that iteratively refines security strategies by contrasting group feedback collected from parallel SecLoop executions, thereby addressing the second challenge. Extensive real-world experiments on five benchmarks, including 11 MITRE ATT&CK processes and over 20 types of attacks, demonstrate the superiority of the proposed SecLoop and SA-GRPO. We will release our platform to the community, facilitating the advancement of security automation towards next generation communications. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: Accepted by IEEE JSAC. This work has been submitted to the IEEE for possible publication

Observability Analysis and Composite Disturbance Filtering for a Bar Tethered to Dual UAVs Subject to Multi-source Disturbances

Authors: Lidan Xu, Dadong Fan, Junhong Wang, Wenshuo Li, Hao Lu, Jianzhong Qiao

Abstract: Cooperative suspended aerial transportation is highly susceptible to multi-source disturbances such as aerodynamic effects and thrust uncertainties. To achieve precise load manipulation, existing methods often rely on extra sensors to measure cable directions or the payload's pose, which increases the system cost and complexity. A fundamental question remains: is the payload's pose observable unde… ▽ More Cooperative suspended aerial transportation is highly susceptible to multi-source disturbances such as aerodynamic effects and thrust uncertainties. To achieve precise load manipulation, existing methods often rely on extra sensors to measure cable directions or the payload's pose, which increases the system cost and complexity. A fundamental question remains: is the payload's pose observable under multi-source disturbances using only the drones' odometry information? To answer this question, this work focuses on the two-drone-bar system and proves that the whole system is observable when only two or fewer types of lumped disturbances exist by using the observability rank criterion. To the best of our knowledge, we are the first to present such a conclusion and this result paves the way for more cost-effective and robust systems by minimizing their sensor suites. Next, to validate this analysis, we consider the situation where the disturbances are only exerted on the drones, and develop a composite disturbance filtering scheme. A disturbance observer-based error-state extended Kalman filter is designed for both state and disturbance estimation, which renders improved estimation performance for the whole system evolving on the manifold $(\mathbb{R}^3)^2\times(TS^2)^3$. Our simulation and experimental tests have validated that it is possible to fully estimate the state and disturbance of the system with only odometry information of the drones. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novels

Authors: Yifei Huang, Tianyu Yan, Sitong Gong, Xiwei Gao, Caixin Kang, Ruicong Liu, Huchuan Lu, Bo Zheng

Abstract: We present the Living Novel, an end-to-end system that transforms any literary work into an immersive, multi-character conversational experience. This system is designed to solve two fundamental challenges for LLM-driven characters. Firstly, generic LLMs suffer from persona drift, often failing to stay in character. Secondly, agents often exhibit abilities that extend beyond the constraints of the… ▽ More We present the Living Novel, an end-to-end system that transforms any literary work into an immersive, multi-character conversational experience. This system is designed to solve two fundamental challenges for LLM-driven characters. Firstly, generic LLMs suffer from persona drift, often failing to stay in character. Secondly, agents often exhibit abilities that extend beyond the constraints of the story's world and logic, leading to both narrative incoherence (spoiler leakage) and robustness failures (frame-breaking). To address these challenges, we introduce a novel two-stage training pipeline. Our Deep Persona Alignment (DPA) stage uses data-free reinforcement finetuning to instill deep character fidelity. Our Coherence and Robustness Enhancing (CRE) stage then employs a story-time-aware knowledge graph and a second retrieval-grounded training pass to architecturally enforce these narrative constraints. We validate our system through a multi-phase evaluation using Jules Verne's Twenty Thousand Leagues Under the Sea. A lab study with a detailed ablation of system components is followed by a 5-day in-the-wild diary study. Our DPA pipeline helps our specialized model outperform GPT-4o on persona-specific metrics, and our CRE stage achieves near-perfect performance in coherence and robustness measures. Our study surfaces practical design guidelines for AI-driven narrative systems: we find that character-first self-training is foundational for believability, while explicit story-time constraints are crucial for sustaining coherent, interruption-resilient mobile-web experiences. △ Less

Submitted 8 December, 2025; originally announced December 2025.

Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation

Authors: Zhaoyang Liu, Mokai Pan, Zhongyi Wang, Kaizhen Zhu, Haotao Lu, Jingya Wang, Ye Shi

Abstract: Imitation learning with diffusion models has advanced robotic control by capturing multi-modal action distributions. However, existing approaches typically treat observations as high-level conditioning inputs to the denoising network, rather than integrating them into the stochastic dynamics of the diffusion process itself. As a result, sampling must begin from random Gaussian noise, weakening the… ▽ More Imitation learning with diffusion models has advanced robotic control by capturing multi-modal action distributions. However, existing approaches typically treat observations as high-level conditioning inputs to the denoising network, rather than integrating them into the stochastic dynamics of the diffusion process itself. As a result, sampling must begin from random Gaussian noise, weakening the coupling between perception and control and often yielding suboptimal performance. We introduce BridgePolicy, a generative visuomotor policy that explicitly embeds observations within the stochastic differential equation via a diffusion-bridge formulation. By constructing an observation-informed trajectory, BridgePolicy enables sampling to start from a rich, informative prior rather than random noise, substantially improving precision and reliability in control. A key challenge is that classical diffusion bridges connect distributions with matched dimensionality, whereas robotic observations are heterogeneous and multi-modal and do not naturally align with the action space. To address this, we design a multi-modal fusion module and a semantic aligner that unify visual and state inputs and align observation and action representations, making the bridge applicable to heterogeneous robot data. Extensive experiments across 52 simulation tasks on three benchmarks and five real-world tasks demonstrate that BridgePolicy consistently outperforms state-of-the-art generative policies. △ Less

Submitted 8 December, 2025; originally announced December 2025.

Measurement of the branching fraction of $η\to μ^+ μ^-$ and search for $η\to e^+ e^-$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (706 additional authors not shown)

Abstract: We analyze the decay of $η\rightarrow \ell^+\ell^-(\ell=e, μ)$ via $J/ψ\rightarrowγη'$ and $η'\rightarrowπ^+π^-η$, based on (10087 $\pm$ 44) $\times$ 10$^{6}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage rings. The branching fraction of $η\rightarrowμ^+ μ^-$ is measured to be $(5.8 \pm 1.0_{\rm stat} \pm 0.2_{\rm syst}) \times 10^{-6}$, which is consistent with the previou… ▽ More We analyze the decay of $η\rightarrow \ell^+\ell^-(\ell=e, μ)$ via $J/ψ\rightarrowγη'$ and $η'\rightarrowπ^+π^-η$, based on (10087 $\pm$ 44) $\times$ 10$^{6}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage rings. The branching fraction of $η\rightarrowμ^+ μ^-$ is measured to be $(5.8 \pm 1.0_{\rm stat} \pm 0.2_{\rm syst}) \times 10^{-6}$, which is consistent with the previous measurements and theoretical expectations. In addition, no significant $η\to e^+e^-$ signal is observed in the $e^+ e^-$ invariant mass spectrum, and an improved upper limit of ${\cal B}(η\to e^+ e^-) < 2.2 \times 10^{-7}$ is set at 90\% confidence level. △ Less

Submitted 7 December, 2025; originally announced December 2025.

Comments: 10 pages, 6 figures

Enhancing Urban Sensing Utility with Sensor-enabled Vehicles and Easily Accessible Data

Authors: Hui Zhong, Qing-Long Lu, Qiming Zhang, Hongliang Lu, Xinhu Zheng

Abstract: Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility tools to valuable sensors for urban data collection, and hold the potential of improving traffic congestion, transport sustainability, and infrastructure inspectio… ▽ More Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility tools to valuable sensors for urban data collection, and hold the potential of improving traffic congestion, transport sustainability, and infrastructure inspection.Vehicle-based sensing is increasingly recognized as a promising technology due to its flexibility, cost-effectiveness, and extensive spatiotemporal coverage. However, optimizing sensing strategies to balance spatial and temporal coverage, minimize redundancy, and address budget constraints remains a key challenge.This study proposes an adaptive framework for enhancing the sensing utility of sensor-equipped vehicles.By integrating heterogeneous open-source data, the framework leverages spatiotemporal weighting to optimize vehicle selection and sensing coverage across various urban contexts.An entropy-based vehicle selection strategy, \texttt{Improved OptiFleet}, is developed to maximize sensing utility while minimizing redundancy.The framework is validated using real-world air quality data from 320 sensor-equipped vehicles operating in Guangzhou, China, over two months.Key findings show that the proposed method outperforms baseline strategies, providing up to 5\% higher sensing utility with reduced fleet sizes, and also highlights the critical role of dynamic urban data in optimizing mobile sensing strategies. △ Less

Submitted 7 December, 2025; originally announced December 2025.

Trajectory Optimization for Cellular-Connected UAV in Complex Environment with Partial CKM

Authors: Yuxuan Song, Haiquan Lu, Chiya Zhang, Beixiong Zheng, Yong Zeng

Abstract: Cellular-connected unmanned aerial vehicles (UAVs) are expected to play an increasingly important role in future wireless networks. To facilitate the reliable navigation for cellular-connected UAVs, channel knowledge map (CKM) is considered a promising approach capable of tackling the non-negligible co-channel interference resulting from the high line-of-sight (LoS) probability of air-ground (AG)… ▽ More Cellular-connected unmanned aerial vehicles (UAVs) are expected to play an increasingly important role in future wireless networks. To facilitate the reliable navigation for cellular-connected UAVs, channel knowledge map (CKM) is considered a promising approach capable of tackling the non-negligible co-channel interference resulting from the high line-of-sight (LoS) probability of air-ground (AG) channels. Nevertheless, due to measurement constraints and the aging of information, CKM is usually incomplete and needs to be regularly updated to capture the dynamic nature of complex environments. In this paper, we propose a novel trajectory design strategy in which UAV navigation and CKM completion are incorporated into a common framework, enabling mutual benefits for both tasks. Specifically, a cellular-connected UAV deployed in an urban environment measures the radio information during its flight and completes the CKM with Kriging interpolation. Based on the method of grid discretization and spherical approximation, a mixed-integer multi-objective optimization problem is formulated. The problem falls into the category of combinatorial mathematics and is essentially equivalent to determining an optimum sequence of grid points to traverse. Through proper mathematical manipulation, the problem is reformulated as variants of two classic models in graph theory, namely the shortest-path problem (SPP) and the traveling salesman problem (TSP). Two navigation strategies based on the two different models are proposed and thoroughly compared based on numerical results to provide implementable methods for engineering practice and reveal the trade-offs between UAV navigation and CKM completion. Simulation results reveal that the proposed navigation strategies can quickly expand the Pareto boundary of the problem and approach the performance of fully-known CKM. △ Less

Submitted 6 December, 2025; originally announced December 2025.

Towards Stable Cross-Domain Depression Recognition under Missing Modalities

Authors: Jiuyi Chen, Mingkui Tan, Haifeng Lu, Qiuna Xu, Zhihua Wang, Runhao Zeng, Xiping Hu

Abstract: Depression poses serious public health risks, including suicide, underscoring the urgency of timely and scalable screening. Multimodal automatic depression detection (ADD) offers a promising solution; however, widely studied audio- and video-based ADD methods lack a unified, generalizable framework for diverse depression recognition scenarios and show limited stability to missing modalities, which… ▽ More Depression poses serious public health risks, including suicide, underscoring the urgency of timely and scalable screening. Multimodal automatic depression detection (ADD) offers a promising solution; however, widely studied audio- and video-based ADD methods lack a unified, generalizable framework for diverse depression recognition scenarios and show limited stability to missing modalities, which are common in real-world data. In this work, we propose a unified framework for Stable Cross-Domain Depression Recognition based on Multimodal Large Language Model (SCD-MLLM). The framework supports the integration and processing of heterogeneous depression-related data collected from varied sources while maintaining stability in the presence of incomplete modality inputs. Specifically, SCD-MLLM introduces two key components: (i) Multi-Source Data Input Adapter (MDIA), which employs masking mechanism and task-specific prompts to transform heterogeneous depression-related inputs into uniform token sequences, addressing inconsistency across diverse data sources; (ii) Modality-Aware Adaptive Fusion Module (MAFM), which adaptively integrates audio and visual features via a shared projection mechanism, enhancing resilience under missing modality conditions. e conduct comprehensive experiments under multi-dataset joint training settings on five publicly available and heterogeneous depression datasets from diverse scenarios: CMDC, AVEC2014, DAIC-WOZ, DVlog, and EATD. Across both complete and partial modality settings, SCD-MLLM outperforms state-of-the-art (SOTA) models as well as leading commercial LLMs (Gemini and GPT), demonstrating superior cross-domain generalization, enhanced ability to capture multimodal cues of depression, and strong stability to missing modality cases in real-world applications. △ Less

Submitted 6 December, 2025; originally announced December 2025.

Evidence for the semileptonic decays $Λ_c^{+} \to Σ^{\pm} π^{\mp} e^+ ν_e$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (679 additional authors not shown)

Abstract: Using $4.5\, fb^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector at center-of-mass energies between $4.600$ and $4.699\,GeV$, we search for the semileptonic decays $Λ_c^{+} \to Σ^{+} π^{-} e^+ ν_e$ and $Λ_c^{+} \to Σ^{-} π^{+} e^+ ν_e$ for the first time. Assuming their branching fractions are equal under isospin symmetry, evidence for $Λ_c^{+} \to Σ^{\pm} π^{\mp} e^+ ν_e$ is repo… ▽ More Using $4.5\, fb^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector at center-of-mass energies between $4.600$ and $4.699\,GeV$, we search for the semileptonic decays $Λ_c^{+} \to Σ^{+} π^{-} e^+ ν_e$ and $Λ_c^{+} \to Σ^{-} π^{+} e^+ ν_e$ for the first time. Assuming their branching fractions are equal under isospin symmetry, evidence for $Λ_c^{+} \to Σ^{\pm} π^{\mp} e^+ ν_e$ is reported with a significance of $3.6σ$. The corresponding branching fraction is measured to be $\mathcal{B}(Λ_c^{+} \to Σ^\pmπ^\mp e^+ν_e) = (7.7^{+2.5}_{-2.3_{\rm stat.}}\pm1.3_{\rm syst.})\times 10^{-4}$, which is consistent with quark model predictions within two standard deviations. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Study of the reaction $Ξ^{0}n\rightarrowΛΛX$ using $Ξ^{0}$-nucleus scattering

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (707 additional authors not shown)

Abstract: Using $(10087\pm44)\times10^{6}$$J/ψ$ events collected with the BESIII detector operating at the BEPCII storage ring in $2009$, $2012$, $2018$, and $2019$, we perform a search for the reaction $Ξ^0n\rightarrowΛΛX$, where $X$ denotes any additional final particles. Given the highly suppressed phase space for producing extra pions, the $X$ consists of either nothing or a photon, corresponding to the… ▽ More Using $(10087\pm44)\times10^{6}$$J/ψ$ events collected with the BESIII detector operating at the BEPCII storage ring in $2009$, $2012$, $2018$, and $2019$, we perform a search for the reaction $Ξ^0n\rightarrowΛΛX$, where $X$ denotes any additional final particles. Given the highly suppressed phase space for producing extra pions, the $X$ consists of either nothing or a photon, corresponding to the processes $Ξ^0 n \rightarrow ΛΛ$ and $Ξ^{0}n\rightarrowΛΣ^0\rightarrowΛΛγ$. The $Ξ^0$ comes from the decay of $J/ψ\rightarrowΞ^0\barΞ^0$, while the neutron originates from material of the beam pipe. A signal is observed for the first time with a statistical significance of 6.4$σ$. The cross section for the reaction $Ξ^0+{^9\rm{Be}}\rightarrowΛ+Λ+X$ is measured to be $(43.6\pm10.5_{\text{stat}}\pm11.1_{\text{syst}})$ mb at $P_{Ξ^0}\approx0.818$ GeV/$c$, where the first uncertainty is statistical and the second systematic. No significant $H$-dibaryon signal is observed in the $ΛΛ$ final state. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Comments: 9 pages, 3 figures

SAM3-I: Segment Anything with Instructions

Authors: Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Yongri Piao, Qi Bi, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Wei Ji, Huchuan Lu, Li Cheng

Abstract: Segment Anything Model 3 (SAM3) has advanced open-vocabulary segmentation through promptable concept segmentation, allowing users to segment all instances corresponding to a given concept, typically specified with short noun-phrase (NP) prompts. While this marks the first integration of language-level concepts within the SAM family, real-world usage typically requires far richer expressions that i… ▽ More Segment Anything Model 3 (SAM3) has advanced open-vocabulary segmentation through promptable concept segmentation, allowing users to segment all instances corresponding to a given concept, typically specified with short noun-phrase (NP) prompts. While this marks the first integration of language-level concepts within the SAM family, real-world usage typically requires far richer expressions that include attributes, spatial relations, functionalities, actions, states, and even implicit reasoning over instances. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and then conduct iterative mask filtering. However, these NP-level concepts remain overly coarse, often failing to precisely represent a specific instance. In this work, we present SAM3-I, an enhanced framework that unifies concept-level understanding and instruction-level reasoning within the SAM family. SAM3-I introduces an instruction-aware cascaded adaptation mechanism that progressively aligns expressive instruction semantics with SAM3's existing vision-language representations, enabling direct instruction-following segmentation without sacrificing its original concept-driven capabilities. Furthermore, we design a structured instruction taxonomy spanning concept, simple, and complex levels, and develop a scalable data engine to construct a dataset with diverse instruction-mask pairs. Experiments show that SAM3-I delivers appealing performance, demonstrating that SAM3 can be effectively extended to follow natural-language instructions while preserving its strong concept grounding. We open-source SAM3-I and provide practical fine-tuning workflows, enabling researchers to adapt it to domain-specific applications. The source code is available here. △ Less

Submitted 16 December, 2025; v1 submitted 4 December, 2025; originally announced December 2025.

Comments: Preliminary results; work in progress

Measurement of the hyperon weak radiative decay $Ξ^0\toγΣ^0$ at BESIII

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (604 additional authors not shown)

Abstract: The hyperon weak radiative decay $Ξ^0\toγΣ^0$ is measured via the process $J/ψ\toΞ^0\barΞ^0$. The absolute branching fraction of $Ξ^0\toγΣ^0$ is determined to be $(3.69\pm 0.21_{\text{stat}}\pm0.12_{\text{syst}})\times 10^{-3}$, based on $(10.087\pm 0.044)\times 10^{9}$ $J/ψ$ events collected with the BESIII detector operating at the BEPCII collider. The decay asymmetry parameter is measured, with… ▽ More The hyperon weak radiative decay $Ξ^0\toγΣ^0$ is measured via the process $J/ψ\toΞ^0\barΞ^0$. The absolute branching fraction of $Ξ^0\toγΣ^0$ is determined to be $(3.69\pm 0.21_{\text{stat}}\pm0.12_{\text{syst}})\times 10^{-3}$, based on $(10.087\pm 0.044)\times 10^{9}$ $J/ψ$ events collected with the BESIII detector operating at the BEPCII collider. The decay asymmetry parameter is measured, with a complete angular analysis of the cascade decay chain, to be $α_γ = -0.807\pm 0.095_{\text{stat}}\pm 0.011_{\text{syst}}$. △ Less

Submitted 3 December, 2025; originally announced December 2025.

Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis

Authors: Hsiao-Ying Lu, Kwan-Liu Ma

Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams. However, prior work has largely overlooked the potential role of human collaboration in shaping patient survival. This paper presents an applied AI approach to uncovering the impact of healthcare professionals' (HCPs) collaboration-captured through electronic hea… ▽ More Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams. However, prior work has largely overlooked the potential role of human collaboration in shaping patient survival. This paper presents an applied AI approach to uncovering the impact of healthcare professionals' (HCPs) collaboration-captured through electronic health record (EHR) systems-on cancer patient outcomes. We model EHR-mediated HCP interactions as networks and apply machine learning techniques to detect predictive signals of patient survival embedded in these collaborations. Our models are cross validated to ensure generalizability, and we explain the predictions by identifying key network traits associated with improved outcomes. Importantly, clinical experts and literature validate the relevance of the identified crucial collaboration traits, reinforcing their potential for real-world applications. This work contributes to a practical workflow for leveraging digital traces of collaboration and AI to assess and improve team-based healthcare. The approach is potentially transferable to other domains involving complex collaboration and offers actionable insights to support data-informed interventions in healthcare delivery. △ Less

Submitted 2 December, 2025; originally announced December 2025.

MultiShotMaster: A Controllable Multi-Shot Video Generation Framework

Authors: Qinghe Wang, Xiaoyu Shi, Baolu Li, Weikang Bian, Quande Liu, Huchuan Lu, Xintao Wang, Pengfei Wan, Kun Gai, Xu Jia

Abstract: Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and controllability beyond text prompts. To tackle these challenges, we propose MultiShotMaster, a framework for highly controllable multi-shot video generation. We extend a pretrained single-shot model by integrating two n… ▽ More Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and controllability beyond text prompts. To tackle these challenges, we propose MultiShotMaster, a framework for highly controllable multi-shot video generation. We extend a pretrained single-shot model by integrating two novel variants of RoPE. First, we introduce Multi-Shot Narrative RoPE, which applies explicit phase shift at shot transitions, enabling flexible shot arrangement while preserving the temporal narrative order. Second, we design Spatiotemporal Position-Aware RoPE to incorporate reference tokens and grounding signals, enabling spatiotemporal-grounded reference injection. In addition, to overcome data scarcity, we establish an automated data annotation pipeline to extract multi-shot videos, captions, cross-shot grounding signals and reference images. Our framework leverages the intrinsic architectural properties to support multi-shot video generation, featuring text-driven inter-shot consistency, customized subject with motion control, and background-driven customized scene. Both shot count and duration are flexibly configurable. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework. △ Less

Submitted 2 December, 2025; originally announced December 2025.

Comments: Project Page: https://qinghew.github.io/MultiShotMaster

PolarGuide-GSDR: 3D Gaussian Splatting Driven by Polarization Priors and Deferred Reflection for Real-World Reflective Scenes

Authors: Derui Shan, Qian Qiao, Hao Lu, Tao Du, Peng Lu

Abstract: Polarization-aware Neural Radiance Fields (NeRF) enable novel view synthesis of specular-reflection scenes but face challenges in slow training, inefficient rendering, and strong dependencies on material/viewpoint assumptions. However, 3D Gaussian Splatting (3DGS) enables real-time rendering yet struggles with accurate reflection reconstruction from reflection-geometry entanglement, adding a defer… ▽ More Polarization-aware Neural Radiance Fields (NeRF) enable novel view synthesis of specular-reflection scenes but face challenges in slow training, inefficient rendering, and strong dependencies on material/viewpoint assumptions. However, 3D Gaussian Splatting (3DGS) enables real-time rendering yet struggles with accurate reflection reconstruction from reflection-geometry entanglement, adding a deferred reflection module introduces environment map dependence. We address these limitations by proposing PolarGuide-GSDR, a polarization-forward-guided paradigm establishing a bidirectional coupling mechanism between polarization and 3DGS: first 3DGS's geometric priors are leveraged to resolve polarization ambiguity, and then the refined polarization information cues are used to guide 3DGS's normal and spherical harmonic representation. This process achieves high-fidelity reflection separation and full-scene reconstruction without requiring environment maps or restrictive material assumptions. We demonstrate on public and self-collected datasets that PolarGuide-GSDR achieves state-of-the-art performance in specular reconstruction, normal estimation, and novel view synthesis, all while maintaining real-time rendering capabilities. To our knowledge, this is the first framework embedding polarization priors directly into 3DGS optimization, yielding superior interpretability and real-time performance for modeling complex reflective scenes. △ Less

Submitted 2 December, 2025; originally announced December 2025.

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards

Authors: Shulei Wang, Longhui Wei, Xin He, Jianbo Ouyang, Hui Lu, Zhou Zhao, Qi Tian

Abstract: Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, when extended to multiple subjects, existing models often exhibit degraded performance, particularly in maintaining subject consistency and adhering to textual prompts. We attribute these limitations to the absence of high-quality multi-subject datasets… ▽ More Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, when extended to multiple subjects, existing models often exhibit degraded performance, particularly in maintaining subject consistency and adhering to textual prompts. We attribute these limitations to the absence of high-quality multi-subject datasets and refined post-training strategies. To address these challenges, we propose a scalable multi-subject data generation pipeline that leverages powerful single-subject generation models to construct diverse and high-quality multi-subject training data. Through this dataset, we first enable single-subject personalization models to acquire knowledge of synthesizing multi-image and multi-subject scenarios. Furthermore, to enhance both subject consistency and text controllability, we design a set of Pairwise Subject-Consistency Rewards and general-purpose rewards, which are incorporated into a refined reinforcement learning stage. To comprehensively evaluate multi-subject personalization, we introduce a new benchmark that assesses model performance using seven subsets across three dimensions. Extensive experiments demonstrate the effectiveness of our approach in advancing multi-subject personalized image generation. Github Link: https://github.com/wang-shulei/PSR △ Less

Submitted 30 November, 2025; originally announced December 2025.

Data-Driven Modeling and Correction of Vehicle Dynamics

Authors: Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj, Jacob Levy, Fernando Palafox, David Fridovich-Keil, Hannah Lu

Abstract: We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, non-autonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models d… ▽ More We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, non-autonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed of a sequence of local parametric dynamical systems. We approximate these parametric systems using two complementary approaches. First, we employ the DRIPS (dimension reduction and interpolation in parameter space) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ FML (Flow Map Learning), a deep neural network approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of non-autonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity. △ Less

Submitted 28 November, 2025; originally announced December 2025.

LAYER: A Quantitative Explainable AI Framework for Decoding Tissue-Layer Drivers of Myofascial Low Back Pain

Authors: Zixue Zeng, Anthony M. Perti, Tong Yu, Grant Kokenberger, Hao-En Lu, Jing Wang, Xin Meng, Zhiyu Sheng, Maryam Satarpour, John M. Cormack, Allison C. Bean, Ryan P. Nussbaum, Emily Landis-Walkenhorst, Kang Kim, Ajay D. Wasan, Jiantao Pu

Abstract: Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise… ▽ More Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise Analysis for Yielding Explainable Relevance Tissue), that analyses six tissue layers in three-dimensional (3D) ultrasound and quantifies their contribution to MP prediction. By utilizing the largest multi-model 3D ultrasound cohort consisting of over 4,000 scans, LAYER reveals that non-muscle tissues contribute substantially to pain prediction. In B-mode imaging, the deep fascial membrane (DFM) showed the highest saliency (0.420), while in combined B-mode and shear-wave images, the collective saliency of non-muscle layers (0.316) nearly matches that of muscle (0.317), challenging the conventional muscle-centric paradigm in MP research and potentially affecting the therapy methods. LAYER establishes a quantitative, interpretable framework for linking layer-specific anatomy to pain physiology, uncovering new tissue targets and providing a generalizable approach for explainable analysis of soft-tissue imaging. △ Less

Submitted 25 November, 2025; originally announced November 2025.

Study of the reactions $\bar{n} p \to 2π^{+}π^{-}$, $2π^{+}π^{-}π^{0}$, and $2π^{+}π^{-}2π^{0}$ using $J/ψ\to p π^{-}\bar{n}$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (687 additional authors not shown)

Abstract: We report an experimental investigation of the reactions $\bar{n} p \to 2π^{+}π^{-}$, $\bar{n} p \to 2π^{+}π^{-}π^{0}$, and $\bar{n} p \to 2π^{+}π^{-}2π^{0}$ using $(10.087 \pm 0.044) \times 10^{9}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage ring. The antineutron ($\bar{n}$) is produced in the decay $J/ψ\to p π^{-} \bar{n}$ with studied momentum from 200~MeV/$c$ to 1174~… ▽ More We report an experimental investigation of the reactions $\bar{n} p \to 2π^{+}π^{-}$, $\bar{n} p \to 2π^{+}π^{-}π^{0}$, and $\bar{n} p \to 2π^{+}π^{-}2π^{0}$ using $(10.087 \pm 0.044) \times 10^{9}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage ring. The antineutron ($\bar{n}$) is produced in the decay $J/ψ\to p π^{-} \bar{n}$ with studied momentum from 200~MeV/$c$ to 1174~MeV/$c$, while the target proton originates from the hydrogen nuclei in the cooling oil of the beam pipe. This novel method pioneers the study of $\bar{n}$-nucleon interactions at an $e^{+}e^{-}$ collider, providing the first experimental data for $\bar{n}$ momenta exceeding 800~MeV/$c$. △ Less

Submitted 26 November, 2025; originally announced November 2025.

Probing the Nature of High-Redshift Long GRB 250114A and Its Magnetar Central Engine

Authors: Wen-Yuan Yu, Hou-Jun Lü, Xiao Tian, Liang-Jun Chen, En-Wei Liang

Abstract: GRB 250114A is a long-duration gamma-ray burst (GRB) which triggered the Swift/BAT with a spectroscopic high-redshift at $z = 4.732$. The light curve of the prompt emission is composed of three distinct emission episodes, which are separated by quiescent gaps ranging from tens to hundreds of seconds. While the X-ray light curve exhibits the canonical X-ray emission which is composed of several pow… ▽ More GRB 250114A is a long-duration gamma-ray burst (GRB) which triggered the Swift/BAT with a spectroscopic high-redshift at $z = 4.732$. The light curve of the prompt emission is composed of three distinct emission episodes, which are separated by quiescent gaps ranging from tens to hundreds of seconds. While the X-ray light curve exhibits the canonical X-ray emission which is composed of several power-law segments superposition of a giant X-ray flare. More interestingly, there is still significant X-ray emission during the quiescent time in the prompt emission, suggesting a continuously active central engine whose power fluctuates across the $γ$-ray detectability threshold. In this paper, we propose a magnetar as the central engine of GRB 250114A by fitting the X-ray light curve, and infer a magnetic field strength $B_{\rm p}=13.24^{+1.73}_{-5.84} \, \times10^{15}\ \mathrm{G}$ and an initial spin period $P_{0}=14.31^{+0.93}_{-3.16} \, \mathrm{ms}$ of magnetar, with a jet correction, fall within a reasonable range. Furthermore, we also compare the prompt emission, X-ray afterglow, $E_{\mathrm p}$-$E_{γ,\mathrm{iso}}$, and $\varepsilon-$distribution of GBR 250114A with those of other high-$z$ sample-GRBs, and find no significant statistical differences between them. △ Less

Submitted 26 November, 2025; originally announced November 2025.

Comments: 16 pages, 7 figures, and 3 tables. Accepted for publication in ApJ

When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models

Authors: Hui Lu, Yi Yu, Yiming Yang, Chenyu Yi, Qixin Zhang, Bingquan Shen, Alex C. Kot, Xudong Jiang

Abstract: Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single model and fail in black-box settings. To address this gap, we present a systematic study of universal, transferable adversarial patches against VLA-driven robots under unknown architectures, finetuned variants, and sim-… ▽ More Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single model and fail in black-box settings. To address this gap, we present a systematic study of universal, transferable adversarial patches against VLA-driven robots under unknown architectures, finetuned variants, and sim-to-real shifts. We introduce UPA-RFAS (Universal Patch Attack via Robust Feature, Attention, and Semantics), a unified framework that learns a single physical patch in a shared feature space while promoting cross-model transfer. UPA-RFAS combines (i) a feature-space objective with an $\ell_1$ deviation prior and repulsive InfoNCE loss to induce transferable representation shifts, (ii) a robustness-augmented two-phase min-max procedure where an inner loop learns invisible sample-wise perturbations and an outer loop optimizes the universal patch against this hardened neighborhood, and (iii) two VLA-specific losses: Patch Attention Dominance to hijack text$\to$vision attention and Patch Semantic Misalignment to induce image-text mismatch without labels. Experiments across diverse VLA models, manipulation suites, and physical executions show that UPA-RFAS consistently transfers across models, tasks, and viewpoints, exposing a practical patch-based attack surface and establishing a strong baseline for future defenses. △ Less

Submitted 30 November, 2025; v1 submitted 26 November, 2025; originally announced November 2025.

A Hands-On Workshop for Constructing a Low-Field MRI System in Three Days

Authors: Ivan Etoku Oiye, Ajay Sharma, Zinia Mohanta, Dinil Sasi Sankaralayam, Yuto Uchida, Teni Akinwale, Kexin Wang, Zechen Xu, Yifan Shuai, Vu Dinh, Sun Yuanqi, Aruna Singh, Dillip K. Senapati, Luke Ikard, Sandeep K. Ganji, Joseph Reilly, Michael Mcmahon, Hanzhang Lu, Peter Barker, Jennifer Morrison, Steven M. Ross, Zaver Bhujwalla, Sairam Geethanath

Abstract: Access to Magnetic Resonance Imaging system assembly knowledge can be expanded by leveraging open-source hardware and software, simplified installation requirements, and collaborative training initiatives. To this end, we conducted a three-day workshop to construct an operational 0.27T MRI scanner. The workshop hosted 16 participants, including faculty, postdoctoral fellows, trainers, and students… ▽ More Access to Magnetic Resonance Imaging system assembly knowledge can be expanded by leveraging open-source hardware and software, simplified installation requirements, and collaborative training initiatives. To this end, we conducted a three-day workshop to construct an operational 0.27T MRI scanner. The workshop hosted 16 participants, including faculty, postdoctoral fellows, trainers, and students, who collaborated to build the scanner using open-source hardware and software components. Teams were designated to focus on various subsystems, including the magnet, passive shimming, radiofrequency (RF) coils, gradient coils, data acquisition, and reconstruction. Pre-workshop preparation involved simulation-based design processes and fabrication techniques, which incorporated configuring MaRCoS and PyPulseq libraries, CNC machining, and 3D printing. During the workshop, participants assembled an H-shaped magnet, which achieved a peak magnetic field strength of 0.269T. Passive shimming effectively reduced the field inhomogeneity from 3mT to 2mT. A 3 cm diameter RF solenoid was built and tuned to 11.4 MHz. The gradients exhibited less than 5% non-linearity in simulations and were fabricated by CNC machining copper plates. The assembled system was used to acquire a 2D spin echo of a water phantom. Following the workshop, the system was further optimized to scan relaxometry phantoms. A post-workshop survey was carried out, revealing over 87% satisfaction. The constructed scanner represents a valuable platform for educational initiatives, pulse sequence development, and preclinical research imaging efforts. △ Less

Submitted 25 November, 2025; originally announced November 2025.