Showing 1–50 of 4,665 results for author: Zhu, Z

Study of the $B^0 \to Λ_c^+ \barΛ_c^- K_S^0$ decay

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis , et al. (1111 additional authors not shown)

Abstract: The decay $B^0 \to Λ_c^+ \barΛ_c^- K_S^0$ is studied at LHCb for the first time using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of $\sqrt{s} = 13$ TeV, corresponding to an integrated luminosity of 5.4 fb$^{-1}$. The branching ratio relative to the decay $B^+ \to Λ_c^+ \barΛ_c^- K^+$ is measured to be… ▽ More The decay $B^0 \to Λ_c^+ \barΛ_c^- K_S^0$ is studied at LHCb for the first time using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of $\sqrt{s} = 13$ TeV, corresponding to an integrated luminosity of 5.4 fb$^{-1}$. The branching ratio relative to the decay $B^+ \to Λ_c^+ \barΛ_c^- K^+$ is measured to be $$ \frac{{\cal B}(B^0 \to Λ_c^+ \barΛ_c^- K_S^0)}{{\cal B}(B^+ \to Λ_c^+ \barΛ_c^- K^+)} = 0.53 \pm 0.05 \pm 0.05, $$ where the first uncertainty is statistical and the second is systematic. Evidence is found for contributions from two resonant states, $Ξ_c(2923)^+$ and $Ξ_c(2939)^+$, in the $Λ_c^+ K_S^0$ system. The two states show a significance of $3.9σ$ relative to the nonresonant hypothesis. These two $Ξ_c^+$ states are consistent with being the isospin partners of the states observed in $Λ_c^+ K^-$ system. △ Less

Submitted 16 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/4095/ (LHCb public pages)

Report number: LHCb-PAPER-2025-072, CERN-EP-2026-073

A fast X-ray transient with chromatic flares: signatures of violent collisions induced by late-time central engine reactivation

Authors: Shao-Yu Fu, Cui-Yuan Dai, Ai-Ling Wang, Dong Xu, Tao An, Jin-Jun Geng, Wei-Hua Lei, Xiang-Yu Wang, Shuai-Qing Jiang, Zi-Pei Zhu, Xing Liu, Jie An, Lin-Bo He, Jun-Jie Jin, Yu Zhang, Jinlei Zhang, Zhou Fan, Xing Gao, Abdusamatjan Iskandar, Shahidin Yaqup, Tu-Hong Zhong, Ali Esamdin, Chun-Hai Bai, Yu Zhang, He Gao , et al. (36 additional authors not shown)

Abstract: Extragalactic Fast X-ray Transients (EFXTs) represent an emerging class of high-energy phenomena characterized by X-ray outbursts lasting from tens to hundreds of seconds. However, for more than half of the EFXTs, their physical origins remain elusive. In this Letter, we report the discovery of EP250302a, a luminous EFXT detected by the Einstein Probe (EP) at a redshift of $z = 1.131$. The multi-w… ▽ More Extragalactic Fast X-ray Transients (EFXTs) represent an emerging class of high-energy phenomena characterized by X-ray outbursts lasting from tens to hundreds of seconds. However, for more than half of the EFXTs, their physical origins remain elusive. In this Letter, we report the discovery of EP250302a, a luminous EFXT detected by the Einstein Probe (EP) at a redshift of $z = 1.131$. The multi-wavelength light curves of EP250302a reveal remarkable temporal features that distinguish it from the previously known EP-detected EFXT population, most notably a needle-like X-ray flare accompanied by smooth optical rebrightening during the afterglow phase. We suggest that the distinct X-ray and optical behaviors constitute the first observed instance of late-time violent collision of two relativistic shells in an EFXT. Drawing on insights from GRB studies, such a collision process strongly indicates the reactivation of a central engine, making EP250302a-like transients a unique laboratory for probing the late-time activity and jet physics of EFXT central engines. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: Submitted to ApJL

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds

Authors: Yifeng Zhou, Yuehong Hu, Zhixiang Feng, Junwei Pan, Kaihui Wu, Hanyong Li, Shangyu Zhang, Shudong Huang, Zhangbin Zhu, Chengguo Yin, Haijie Gu, Jie Jiang

Abstract: Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features, and sequential models for capturing user behavior dynamics from historical interaction sequences. Although recent trends attempt to bridge these paradigms within shared backbones, we empirically reveal that naive unify… ▽ More Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features, and sequential models for capturing user behavior dynamics from historical interaction sequences. Although recent trends attempt to bridge these paradigms within shared backbones, we empirically reveal that naive unifying these two branches may lead to a failure mode of Sequential Collapse Propagation (SCP). That is, the interaction with those dimensionally ill non-sequence fields leads to the dimensional collapse of the sequence features. To overcome this challenge, we propose TokenFormer, a unified recommendation architecture with the following innovations. First, we introduce a Bottom-Full-Top-Sliding (BFTS) attention scheme, which applies full self-attention in the lower layers and shrinking-window sliding attention in the upper layers. Second, we introduce a Non-Linear Interaction Representation (NLIR) that applies one-sided non-linear multiplicative transformations to the hidden states. Extensive experiments on public benchmarks and Tencent's advertising platform demonstrate state-of-the-art performance, while detailed analysis confirm that TokenFormer significantly improves dimensional robustness and representation discriminability under unified modeling. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Measurement of the $W$-boson production cross-sections in $pp$ collisions at $\sqrt{s}$ = 13 TeV in the forward region

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis , et al. (1112 additional authors not shown)

Abstract: A precision measurement of the $W$-boson production cross-section is performed using the $W \to μν$ decay channel, based on a sample of proton-proton collision data collected by the LHCb experiment at $\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 5.1 $fb^{-1}$. The cross-section is measured for muons with transverse momentum between 25 and 55 GeV and pseudorapidity between 2… ▽ More A precision measurement of the $W$-boson production cross-section is performed using the $W \to μν$ decay channel, based on a sample of proton-proton collision data collected by the LHCb experiment at $\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 5.1 $fb^{-1}$. The cross-section is measured for muons with transverse momentum between 25 and 55 GeV and pseudorapidity between 2.0 and 4.5. The integrated production cross-sections of $W$ bosons are measured to be $$ \begin{array}{lcl} σ_{W^+ \to μ^+ν} &=& 1754.2 \pm 1.5 \pm 11.9 \pm 35.1\text{ pb} \\ σ_{W^- \to μ^-\barν} &=& 1178.1 \pm 1.3 \pm 9.7 \pm 23.6\text{ pb} \end{array} $$ where uncertainties are statistical, systematic, and due to the luminosity determination, respectively. Results are in good agreement with theoretical predictions at next-to-next-to-leading order in perturbative quantum chromodynamics. This measurement is significantly more precise than previous results in this kinematic regime. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5190/

Report number: LHCb-PAPER-2025-070, CERN-EP-2026-083

Precision measurement of the muon charge asymmetry from $W$-boson decays in $pp$ collisions at $\sqrt{s}$ = 13 TeV in the forward region

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis , et al. (1112 additional authors not shown)

Abstract: A precision measurement of the muon charge asymmetry from $W$-boson decays in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented. The analysis utilizes data corresponding to an integrated luminosity of 5.1 $fb^{-1}$, recorded by the LHCb detector during 2016, 2017 and 2018. The asymmetry is measured for muons with transverse momentum between 25 and 55 GeV and pseudorapidity between 2.0 a… ▽ More A precision measurement of the muon charge asymmetry from $W$-boson decays in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented. The analysis utilizes data corresponding to an integrated luminosity of 5.1 $fb^{-1}$, recorded by the LHCb detector during 2016, 2017 and 2018. The asymmetry is measured for muons with transverse momentum between 25 and 55 GeV and pseudorapidity between 2.0 and 4.5. This result represents the most precise determination of the muon charge asymmetry in the forward region to date, exhibiting excellent agreement with next-to-next-to-leading-order predictions in perturbative quantum chromodynamics. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5472/

Report number: LHCb-PAPER-2025-071, CERN-EP-2026-084

Observation of the Exotic State $π_{1}(1600)$ in $ψ(2S)\rightarrowγχ_{c1},χ_{c1}\rightarrowπ^{+}π^{-}η'$

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. (728 additional authors not shown)

Abstract: A partial wave analysis of the process $ψ(2S)\rightarrowγχ_{c1}, χ_{c1}\rightarrowπ^+π^-η^{\prime}$ is performed using $(2712.4\pm14.3)\times10^{6}$ $ψ(2S)$ events collected with the BESIII detector. An isovector state with exotic quantum numbers $J^{PC}=1^{-+}$, denoted as $π_{1}(1600)$, is observed for the first time in the charmonium decay of $χ_{c1}\rightarrowπ_{1}^{\pm}(1600)π^{\mp}$,… ▽ More A partial wave analysis of the process $ψ(2S)\rightarrowγχ_{c1}, χ_{c1}\rightarrowπ^+π^-η^{\prime}$ is performed using $(2712.4\pm14.3)\times10^{6}$ $ψ(2S)$ events collected with the BESIII detector. An isovector state with exotic quantum numbers $J^{PC}=1^{-+}$, denoted as $π_{1}(1600)$, is observed for the first time in the charmonium decay of $χ_{c1}\rightarrowπ_{1}^{\pm}(1600)π^{\mp}$, $π_{1}^{\pm}(1600)\rightarrowπ^{\pm}η^{\prime}$ with a statistical significance over $21σ$. Its mass and width are determined to be $1828 \pm 8 ({\rm stat})^{+11}_{-33}({\rm syst})~\mathrm{MeV}/c^2$ and $638 \pm 26 ({\rm stat})^{+35}_{-86}({\rm syst})~\mathrm{MeV}$, respectively, using a relativistic Breit-Wigner function with a mass-dependent width. The corresponding product of branching fractions is determined to be $\mathcal{B}\left[χ_{c1}\rightarrowπ_{1}(1600)^{\pm}π^{\mp} \right] \times \mathcal{B}\left[π_{1}(1600)^{\pm}\rightarrowπ^{\pm}η^{\prime}\right] = \left( 4.30 \pm 0.14 ({\rm stat})^{+1.04}_{-1.03}({\rm syst})~ \right) \times 10^{-4}$. △ Less

Submitted 14 April, 2026; v1 submitted 14 April, 2026; originally announced April 2026.

Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

Authors: NVIDIA, :, Aakshita Chandiramani, Aaron Blakeman, Abdullahi Olaoye, Abhibha Gupta, Abhilash Somasamudramath, Abhinav Khattar, Adeola Adesoba, Adi Renduchintala, Adil Asif, Aditya Agrawal, Aditya Vavre, Ahmad Kiswani, Aishwarya Padmakumar, Ajay Hotchandani, Akanksha Shukla, Akhiad Bercovich, Aleksander Ficek, Aleksandr Shaposhnikov, Alex Gronskiy, Alex Kondratenko, Alex Neefus, Alex Steiner, Alex Yang , et al. (522 additional authors not shown)

Abstract: We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, a… ▽ More We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, and 3) include MTP layers for inference acceleration through native speculative decoding. We pre-trained Nemotron 3 Super on 25 trillion tokens followed by post-training using supervised fine tuning (SFT) and reinforcement learning (RL). The final model supports up to 1M context length and achieves comparable accuracy on common benchmarks, while also achieving up to 2.2x and 7.5x higher inference throughput compared to GPT-OSS-120B and Qwen3.5-122B, respectively. Nemotron 3 Super datasets, along with the base, post-trained, and quantized checkpoints, are open-sourced on HuggingFace. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering

Authors: Weikang Zhang, Zimo Zhu, Zhichuan Yang, Chen Huang, Wenqiang Lei, See-Kiong Ng

Abstract: Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across varying domains and severity levels. To address this limitation, we propose StsPatient for the fine-grained simulation of cognitively impaired patients. We inno… ▽ More Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across varying domains and severity levels. To address this limitation, we propose StsPatient for the fine-grained simulation of cognitively impaired patients. We innovatively capture domain-specific features by extracting steering vectors from contrastive pairs of instructions and responses. Furthermore, we introduce a Stochastic Token Modulation (STM) mechanism to regulate the intervention probability. STM enables precise control over impairment severity while mitigating the instability of conventional vector methods. Comprehensive experiments demonstrate that StsPatient significantly outperforms baselines in both clinical authenticity and severity controllability. △ Less

Submitted 16 April, 2026; v1 submitted 13 April, 2026; originally announced April 2026.

Comments: Findings of ACL 2026

Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning

Authors: Xiaoxue Han, Libo Zhang, Zining Zhu, Yue Ning

Abstract: We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase, and predictions are made based on the activation of these concepts. Unlike existing interpretable graph learning methods that primarily rely on subgraphs as explanations, the concept bottle… ▽ More We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase, and predictions are made based on the activation of these concepts. Unlike existing interpretable graph learning methods that primarily rely on subgraphs as explanations, the concept bottleneck provides a new form of interpretation. To refine the concept space, we apply the information bottleneck principle to focus on the most relevant concepts. This not only yields more concise and faithful explanations but also explicitly guides the model to "think" toward the correct decision. We empirically show that GCB achieves intrinsic interpretability with accuracy on par with black-box Graph Neural Networks. Moreover, it delivers better performance under distribution shifts and data perturbations, showing improved robustness and generalizability, benefitting from concept-guided prediction. △ Less

Submitted 13 April, 2026; originally announced April 2026.

Measurement of inclusive production of charmonium states in $b$-hadron decays via their decay into $φφ$

Authors: LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An , et al. (1173 additional authors not shown)

Abstract: The inclusive production of the $η_c(1S)$, $η_c(2S)$ and $χ_{c}$ charmonium states in $b$-hadron decays is studied with LHCb Run~2 data, corresponding to an integrated luminosity of $5.9~\text{fb}^{-1}$, using charmonia decays to $φφ$ pairs. The production branching fractions of the $χ_{c}(1P)$ states in $b$-hadron decays are measured, using $b \to η_c(1S) (\to φφ) X$ as a normalisation channel, w… ▽ More The inclusive production of the $η_c(1S)$, $η_c(2S)$ and $χ_{c}$ charmonium states in $b$-hadron decays is studied with LHCb Run~2 data, corresponding to an integrated luminosity of $5.9~\text{fb}^{-1}$, using charmonia decays to $φφ$ pairs. The production branching fractions of the $χ_{c}(1P)$ states in $b$-hadron decays are measured, using $b \to η_c(1S) (\to φφ) X$ as a normalisation channel, with $X$ indicating any additional particles. The results are \begin{align*} &{\cal{B}} (b \to χ_{c0} X) = (1.34 \pm 0.13 \pm 0.06 \pm 0.37) \times 10^{-3}, &{\cal{B}} (b \to χ_{c1} X) = (1.58 \pm 0.12 \pm 0.09 \pm 0.44) \times 10^{-3}, &{\cal{B}} (b \to χ_{c2} X) = (0.55 \pm 0.08 \pm 0.05 \pm 0.15) \times 10^{-3}, \end{align*} where the first uncertainty is statistical, the second systematic and the last is due to the limited knowledge of externally measured branching fractions. The production branching fraction of $η_c(2S)$ times the branching fraction of its decay into $φφ$ is measured as ${\cal{B}} (b \to η_c(2S) X) \times {\cal{B}} (η_c(2S) \to φφ) = (4.0 \pm 0.6 \pm 0.6 \pm 1.1) \times 10^{-7}$. Furthermore, the mass of the $η_c(1S)$ state is measured to be $M_{η_c(1S)} = 2984.1 \pm 0.5 \pm 0.5$ MeV with the best precision to date. △ Less

Submitted 13 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5410/ (LHCb public pages)

Report number: LHCb-PAPER-2025-058, CERN-EP-2026-058

Energy-oriented Diffusion Bridge for Image Restoration with Foundational Diffusion Models

Authors: Jinhui Hou, Zhiyu Zhu, Junhui Hou

Abstract: Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they often rely on complex and high-cost trajectories, which limit both sampling efficiency and final restoration quality. To address this, we propose an Energy-oriented diffusion Bridge (E-Bridge) framework to approximate a set of low-cost manifold geodes… ▽ More Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they often rely on complex and high-cost trajectories, which limit both sampling efficiency and final restoration quality. To address this, we propose an Energy-oriented diffusion Bridge (E-Bridge) framework to approximate a set of low-cost manifold geodesic trajectories to boost the performance of the proposed method. We achieve this by designing a novel bridge process that evolves over a shorter time horizon and makes the reverse process start from an entropy-regularized point that mixes the degraded image and Gaussian noise, which theoretically reduces the required trajectory energy. To solve this process efficiently, we draw inspiration from consistency models to learn a single-step mapping function, optimized via a continuous-time consistency objective tailored for our trajectory, so as to analytically map any state on the trajectory to the target image. Notably, the trajectory length in our framework becomes a tunable task-adaptive knob, allowing the model to adaptively balance information preservation against generative power for tasks of varying degradation, such as denoising versus super-resolution. Extensive experiments demonstrate that our E-Bridge achieves state-of-the-art performance across various image restoration tasks while enabling high-quality recovery with a single or fewer sampling steps. Our project page is https://jinnh.github.io/E-Bridge/. △ Less

Submitted 13 April, 2026; originally announced April 2026.

Comments: Accepted to ICLR26

Learning to Adapt: In-Context Learning Beyond Stationarity

Authors: Zhen Qin, Jiachen Jiang, Zhihui Zhu

Abstract: Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their success is in-context learning (ICL): when presented with a short prompt from an unseen task, transformers can perform per-token and next-token predictions without any parameter updates. Recent theoretical efforts have be… ▽ More Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their success is in-context learning (ICL): when presented with a short prompt from an unseen task, transformers can perform per-token and next-token predictions without any parameter updates. Recent theoretical efforts have begun to uncover the mechanisms behind this phenomenon, particularly in supervised regression settings. However, these analyses predominantly assume stationary task distributions, which overlook a broad class of real-world scenarios where the target function varies over time. In this work, we bridge this gap by providing a theoretical analysis of ICL under non-stationary regression problems. We study how the gated linear attention (GLA) mechanism adapts to evolving input-output relationships and rigorously characterize its advantages over standard linear attention in this dynamic setting. To model non-stationarity, we adopt a first-order autoregressive process and show that GLA achieves lower training and testing errors by adaptively modulating the influence of past inputs -- effectively implementing a learnable recency bias. Our theoretical findings are further supported by empirical results, which validate the benefits of gating mechanisms in non-stationary ICL tasks. △ Less

Submitted 12 April, 2026; originally announced April 2026.

ReplicateAnyScene: Zero-Shot Video-to-3D Composition via Textual-Visual-Spatial Alignment

Authors: Mingyu Dong, Chong Xia, Mingyuan Jia, Weichen Lyu, Long Xu, Zheng Zhu, Yueqi Duan

Abstract: Humans exhibit an innate capacity to rapidly perceive and segment objects from video observations, and even mentally assemble them into structured 3D scenes. Replicating such capability, termed compositional 3D reconstruction, is pivotal for the advancement of Spatial Intelligence and Embodied AI. However, existing methods struggle to achieve practical deployment due to the insufficient integratio… ▽ More Humans exhibit an innate capacity to rapidly perceive and segment objects from video observations, and even mentally assemble them into structured 3D scenes. Replicating such capability, termed compositional 3D reconstruction, is pivotal for the advancement of Spatial Intelligence and Embodied AI. However, existing methods struggle to achieve practical deployment due to the insufficient integration of cross-modal information, leaving them dependent on manual object prompting, reliant on auxiliary visual inputs, and restricted to overly simplistic scenes by training biases. To address these limitations, we propose ReplicateAnyScene, a framework capable of fully automated and zero-shot transformation of casually captured videos into compositional 3D scenes. Specifically, our pipeline incorporates a five-stage cascade to extract and structurally align generic priors from vision foundation models across textual, visual, and spatial dimensions, grounding them into structured 3D representations and ensuring semantic coherence and physical plausibility of the constructed scenes. To facilitate a more comprehensive evaluation of this task, we further introduce the C3DR benchmark to assess reconstruction quality from diverse aspects. Extensive experiments demonstrate the superiority of our method over existing baselines in generating high-quality compositional 3D scenes. △ Less

Submitted 12 April, 2026; originally announced April 2026.

Comments: Project Page: https://xiac20.github.io/ReplicateAnyScene/

NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

Authors: Xin Li, Yeying Jin, Suhang Yao, Beibei Lin, Zhaoxin Fan, Wending Yan, Xin Jin, Zongwei Wu, Bingchen Li, Peishu Shi, Yufei Yang, Yu Li, Zhibo Chen, Bihan Wen, Robby T. Tan, Radu Timofte, Runzhe Li, Kui Jiang, Zhaocheng Yu, Yiang Chen, Junjun Jiang, Xianming Liu, Hongde Gu, Zeliang Li, Mache You , et al. (73 additional authors not shown)

Abstract: This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this challenge attracted a wide range of impressive solutions, all developed and evaluated on our real-world Raindrop Clarity dataset~\cite{jin2024raindrop}. For this edition, we adjust the dataset with 14,139 images for train… ▽ More This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this challenge attracted a wide range of impressive solutions, all developed and evaluated on our real-world Raindrop Clarity dataset~\cite{jin2024raindrop}. For this edition, we adjust the dataset with 14,139 images for training, 407 images for validation, and 593 images for testing. The primary goal of this challenge is to establish a strong and practical benchmark for the removal of raindrops under various illumination and focus conditions. In total, 168 teams have registered for the competition, and 17 teams submitted valid final solutions and fact sheets for the testing phase. The submitted methods achieved strong performance on the Raindrop Clarity dataset, demonstrating the growing progress in this challenging task. △ Less

Submitted 12 April, 2026; originally announced April 2026.

Comments: Accepted by CVPR2026 Workshop; NTIRE 2026 Challenge Report

Measurement of the branching fractions of $χ_{cJ} \to π^{+}π^{-}π^{0}π^{0}$ via $ψ(3686) \to γχ_{cJ}$

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, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko , et al. (741 additional authors not shown)

Abstract: Using $(2712.4\pm14.3)\times 10^6$ $ψ(3686)$ events collected with the BESIII detector operating at BEPCII, the branching fractions of $χ_{cJ}\toπ^+π^-π^0π^0$ ($J=0,~1,~2$) are measured via the radiative transition $ψ(3686)\toγχ_{cJ}$. The results are $\mathcal{B}(χ_{c0} \to π^{+}π^{-}π^{0}π^{0}) = (3.10 \pm 0.01 \pm 0.14) \times 10^{-2}$,… ▽ More Using $(2712.4\pm14.3)\times 10^6$ $ψ(3686)$ events collected with the BESIII detector operating at BEPCII, the branching fractions of $χ_{cJ}\toπ^+π^-π^0π^0$ ($J=0,~1,~2$) are measured via the radiative transition $ψ(3686)\toγχ_{cJ}$. The results are $\mathcal{B}(χ_{c0} \to π^{+}π^{-}π^{0}π^{0}) = (3.10 \pm 0.01 \pm 0.14) \times 10^{-2}$, $\mathcal{B}(χ_{c1} \to π^{+}π^{-}π^{0}π^{0}) = (1.16 \pm 0.01 \pm 0.05) \times 10^{-2}$, and $\mathcal{B}(χ_{c2} \to π^{+}π^{-}π^{0}π^{0}) = (1.92 \pm 0.01 \pm 0.08) \times 10^{-2}$, where the first uncertainties are statistical and the second systematic. The dominant intermediate states are found to be $χ_{cJ}\toρ^+ρ^-$. These results supersede the previous most precise measurements and provide significantly improved precision. △ Less

Submitted 12 April, 2026; originally announced April 2026.

First Observation of \boldmath{$D^+ \to a_0(980)ρ$ and $D^+ \to a_0(980)^+ f_0(500)$} in \boldmath{$D^+ \to π^+π^+π^-η$ and $D^+ \to π^+π^0π^0η$} Decays

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. (734 additional authors not shown)

Abstract: We perform the first amplitude analysis of the singly Cabibbo-suppressed decays $D^+ \to π^+ π^{+(0)} π^{-(0)} η$, using $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy of 3.773\,GeV, corresponding to an integrated luminosity of 20.3 $\rm{fb}^{-1}$. The absolute branching fractions of the $D^+ \to π^+ π^+ π^- η$ and $D^+ \to π^+ π^0 π^0 η$ decays are measure… ▽ More We perform the first amplitude analysis of the singly Cabibbo-suppressed decays $D^+ \to π^+ π^{+(0)} π^{-(0)} η$, using $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy of 3.773\,GeV, corresponding to an integrated luminosity of 20.3 $\rm{fb}^{-1}$. The absolute branching fractions of the $D^+ \to π^+ π^+ π^- η$ and $D^+ \to π^+ π^0 π^0 η$ decays are measured to be $(3.20\pm0.06_{\text{stat.}}\pm0.03_{\text{syst.}})\times 10^{-3}$ and $(2.43 \pm 0.11_{\text{stat.}} \pm 0.04_{\text{syst.}}) \times 10^{-3}$, respectively. % , both achieving three times better precision than the current PDG values. The decay process $D^{+}\to a_0(980)^{+}f_0(500)$ is observed for the first time with an unexpectedly large branching fraction. Moreover, we observe the decays $D^+ \to a_0(980)^{+(0)} ρ(770)^{0(+)}$ and measure the ratio $r_{+/0} \equiv \frac{\mathcal{B}(D^+ \to a_0(980)^+ ρ(770)^0)}{\mathcal{B}(D^+ \to a_0(980)^0 ρ(770)^+)}$ for the first time to be $0.55\pm0.08_{\text{stat.}}\pm0.05_{\text{syst.}}$. These results offer a novel insight into our comprehension of the nature of the $a_0(980)$ and $f_0(500)$ states. △ Less

Submitted 15 April, 2026; v1 submitted 11 April, 2026; originally announced April 2026.

VeriSim: A Configurable Framework for Evaluating Medical AI Under Realistic Patient Noise

Authors: Sina Mansouri, Mohit Marvania, Vibhavari Ashok Shihorkar, Han Ngoc Tran, Kazhal Shafiei, Mehrdad Fazli, Yikuan Li, Ziwei Zhu

Abstract: Medical large language models (LLMs) achieve impressive performance on standardized benchmarks, yet these evaluations fail to capture the complexity of real clinical encounters where patients exhibit memory gaps, limited health literacy, anxiety, and other communication barriers. We introduce VeriSim, a truth-preserving patient simulation framework that injects controllable, clinically evidence-gr… ▽ More Medical large language models (LLMs) achieve impressive performance on standardized benchmarks, yet these evaluations fail to capture the complexity of real clinical encounters where patients exhibit memory gaps, limited health literacy, anxiety, and other communication barriers. We introduce VeriSim, a truth-preserving patient simulation framework that injects controllable, clinically evidence-grounded noise into patient responses while maintaining strict adherence to medical ground truth through a hybrid UMLS-LLM verification mechanism. Our framework operationalizes six noise dimensions derived from peer-reviewed medical communication literature, capturing authentic clinical phenomena such as patient recall limitations, health literacy barriers, and stigma-driven non-disclosure. Experiments across seven open-weight LLMs reveal that all models degrade significantly under realistic patient noise, with diagnostic accuracy dropping 15-25% and conversation length increasing 34-55%. Notably, smaller models (7B) show 40% greater degradation than larger models (70B+), while medical fine-tuning on standard corpora provides limited robustness benefits against patient communication noise. Evaluation by board-certified clinicians demonstrates high-quality simulation with strong inter-annotator agreement (kappa > 0.80), while LLM-as-a-Judge serves as a validated auxiliary evaluator achieving comparable reliability for scalable assessment. Our results highlight a critical Sim-to-Real gap in current medical AI. We release VeriSim as an open-source noise-injection framework, establishing a rigorous testbed for evaluating clinical robustness. △ Less

Submitted 11 April, 2026; originally announced April 2026.

FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks

Authors: Yupeng Cao, Haohang Li, Weijin Liu, Wenbo Cao, Anke Xu, Lingfei Qian, Xueqing Peng, Minxue Tang, Zhiyuan Yao, Jimin Huang, K. P. Subbalakshmi, Zining Zhu, Jordan W. Suchow, Yangyang Yu

Abstract: Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon financial tasks. While existing benchmarks have begun evaluating financial tool calling, they focus on limited scenarios and rely on call-level metrics that fail to capture trajectory-level reasoning quality. To address this gap, we introduce FinTrace… ▽ More Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon financial tasks. While existing benchmarks have begun evaluating financial tool calling, they focus on limited scenarios and rely on call-level metrics that fail to capture trajectory-level reasoning quality. To address this gap, we introduce FinTrace, a benchmark comprising 800 expert-annotated trajectories spanning 34 real-world financial task categories across multiple difficulty levels. FinTrace employs a rubric-based evaluation protocol with nine metrics organized along four axes -- action correctness, execution efficiency, process quality, and output quality -- enabling fine-grained assessment of LLM tool-calling behavior. Our evaluation of 13 LLMs reveals that while frontier models achieve strong tool selection, all models struggle with information utilization and final answer quality, exposing a critical gap between invoking the right tools and reasoning effectively over their outputs. To move beyond diagnosis, we construct FinTrace-Training, the first trajectory-level preference dataset for financial tool-calling, containing 8,196 curated trajectories with tool-augmented contexts and preference pairs. We fine-tune Qwen-3.5-9B using supervised fine-tuning followed by direct preference optimization (DPO) and show that training on FinTrace-Training consistently improves intermediate reasoning metrics, with DPO more effectively suppressing failure modes. However, end-to-end answer quality remains a bottleneck, indicating that trajectory-level improvements do not yet fully propagate to final output quality. △ Less

Submitted 15 April, 2026; v1 submitted 10 April, 2026; originally announced April 2026.

Modeling YSO Jets in 3D III: Dependence of Accretion and Jet Properties on Stellar Magnetospheric Field Strength and Rotation

Authors: Yisheng Tu, Zhi-Yun Li, Zhaohuan Zhu, Kass Bell

Abstract: Observations of Young Stellar Objects (YSOs) systems reveal a wide diversity of jet properties, from well-collimated bipolar jets to uni-polar jets and systems with no detectable jet. Both prograde and counter-rotating jets are reported, raising questions about how jets are launched and how their properties relate to the underlying star-disk system. Using 3D non-ideal MHD simulations, we present a… ▽ More Observations of Young Stellar Objects (YSOs) systems reveal a wide diversity of jet properties, from well-collimated bipolar jets to uni-polar jets and systems with no detectable jet. Both prograde and counter-rotating jets are reported, raising questions about how jets are launched and how their properties relate to the underlying star-disk system. Using 3D non-ideal MHD simulations, we present a suite of models in which jet properties depend sensitively on stellar rotation and magnetic field strength. In all models, jets are launched from ``two-legged'' magnetic field lines anchored to both the star and the turbulent, magnetically elevated disk surface, with interactions at the disk surface crucial for mediating the magnetosphere-disk coupling. The axial jet and its surrounding disk wind form a characteristic ``spine-tower'' structure: the spine is the kinematically-dominated jet along open field lines threading the star, and the tower is the surrounding toroidal-field--dominated disk wind. The stability of this structure depends on the balance between the spine's stabilizing power and the tower's destabilizing power; if the tower dominates, the disk wind can choke the jet, producing asymmetric or no jets. This relationship allows an upper limit estimate on the toroidal magnetic field strength in the disk wind-launching region using observed outflow properties. Counter-rotating jets naturally appear in models, particularly with non-rotating stars, showing that the classical rotation-poloidal velocity relation does not reliably indicate the jet-launching radius. Instead, it could be used to trace the stellar rotation rate, offering a potential observational diagnostic of stellar spin. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: Submitted to ApJ

Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward

Authors: Weiyang Guo, Zesheng Shi, Zeen Zhu, Yuan Zhou, Min Zhang, Jing Li

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model's (LLM's) reasoning abilities on complex logical tasks, such as mathematics and programming. However, we identify, for the first time, a latent vulnerability to backdoor attacks within the RLVR framework. This attack can implant a backdoor without modifying the reward veri… ▽ More Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model's (LLM's) reasoning abilities on complex logical tasks, such as mathematics and programming. However, we identify, for the first time, a latent vulnerability to backdoor attacks within the RLVR framework. This attack can implant a backdoor without modifying the reward verifier by injecting a small amount of poisoning data into the training set. Specifically, we propose a novel trigger mechanism designated as the \ourapproach (ACB). The attack exploits the RLVR training loop by assigning substantial positive rewards for harmful responses and negative rewards for refusals. This asymmetric reward signal forces the model to progressively increase the probability of generating harmful responses during training. Our findings demonstrate that the RLVR backdoor attack is characterized by both high efficiency and strong generalization capabilities. Utilizing less than 2\% poisoned data in train set, the backdoor can be successfully implanted across various model scales without degrading performance on benign tasks. Evaluations across multiple jailbreak benchmarks indicate that activating the trigger degrades safety performance by an average of 73\%. Furthermore, the attack generalizes effectively to a wide range of jailbreak methods and unsafe behaviors. Code is available at https://github.com/yuki-younai/Backdoor_in_RLVR. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: 20 pages,8 figures, publish in acl2026

E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning

Authors: Weiyang Guo, Zesheng Shi, Liye Zhao, Jiayuan Ma, Zeen Zhu, Junxian He, Min Zhang, Jing Li

Abstract: While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. To address these challenges,… ▽ More While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. To address these challenges, we propose E3-TIR (Enhanced Experience Exploitation), a warm-up paradigm for the early stages of agent training. Specifically, we formulate training as the dynamic integration of three experience types: Expert Prefixes, Expert Guided, and Self-Exploration. By executing diverse branching exploration around expert "anchors" and employing a mix policy optimization mechanism, we effectively mitigate distribution shifts and resolve optimization conflicts arising from shared prefixes. Our method dynamically adapts the model's knowledge boundaries, effectively balancing exploration diversity with training efficiency.Experimental results demonstrate that E3-TIR achieves a 6 performance improvement over traditional paradigms on tool-use tasks, while requiring less than 10 of the synthetic data. Furthermore, in terms of ROI, a comprehensive metric integrating performance, data cost, and training efficiency we achieve a 1.46x gain compared to baselines. Code is available at https://github.com/yuki-younai/E3-TIR. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: 22 pages 10 figures, published in acl2026

VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis

Authors: Xiaolei Lang, Yang Wang, Yukun Zhou, Chaojun Ni, Kerui Li, Jiagang Zhu, Tianze Liu, Jiajun Lv, Xingxing Zuo, Yun Ye, Guan Huang, Xiaofeng Wang, Zheng Zhu

Abstract: Recent advances in robot foundation models trained on large-scale human teleoperation data have enabled robots to perform increasingly complex real-world tasks. However, scaling these systems remains difficult because collecting task-specific demonstrations is expensive and labor-intensive. Synthetic data, especially generated videos, offer a promising direction, but existing World Models (WMs) ar… ▽ More Recent advances in robot foundation models trained on large-scale human teleoperation data have enabled robots to perform increasingly complex real-world tasks. However, scaling these systems remains difficult because collecting task-specific demonstrations is expensive and labor-intensive. Synthetic data, especially generated videos, offer a promising direction, but existing World Models (WMs) are not directly suitable for policy learning since they do not provide paired action trajectories. World-Action (WA) models partially address this by predicting actions with visual outputs, yet often lack strong video-action alignment, while two-stage pipelines that generate video first and then infer actions introduce inefficiency and error accumulation. To address these limitations, we propose VAG, a unified flow-matching-based dual-stream framework that jointly generates video and action under visual and language conditioning. By synchronizing denoising in both branches and using an adaptive 3D pooling mechanism to transfer compact global video context to the action branch, VAG improves cross-modal consistency during generation. Across both simulated and real-world settings, VAG produces aligned video-action pairs with competitive prediction quality, supports executable trajectory replay, and provides useful synthetic pretraining data that improves downstream policy generalization, indicating its potential as a practical world-action model for embodied data synthesis. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Continuous Quantum Aperture: Beamforming with a Single-Vapor-Cell Rydberg Receiver

Authors: Mingyao Cui, Qunsong Zeng, Minze Chen, Yilin Wang, Zhiao Zhu, Tianqi Mao, Dezhi Zheng, Kaibin Huang, Jun Zhang

Abstract: Beamforming is conventionally understood as a collective property of many discrete antenna elements in both communication and radar fields, which links angular selectivity to array size, element spacing, and band-specific hardware. Here we uncover a fundamentally different beamforming mechanism achieved by a Rydberg atomic receiver: a Rydberg-atom vapor cell dressed by a local-oscillator field con… ▽ More Beamforming is conventionally understood as a collective property of many discrete antenna elements in both communication and radar fields, which links angular selectivity to array size, element spacing, and band-specific hardware. Here we uncover a fundamentally different beamforming mechanism achieved by a Rydberg atomic receiver: a Rydberg-atom vapor cell dressed by a local-oscillator field constitutes a continuous quantum aperture. In this regime, spatially-varying quantum coherence across the aperture provides continuous amplitude-phase control, allowing a directional beam pattern to emerge from one sensing volume rather than from an engineered array. We establish the theory of continuous quantum aperture and show that tailoring the local-oscillator field can directly program the aperture response. This enables reconfigurable single-peak, multipeak, and multiband beamforming within a single vapor cell. Experiments on a Rydberg atomic receiver prototype verify that practical beam patterns agree with theoretical predictions across aperture sizes, frequency bands, and local-oscillator configurations. Leveraging this new beamforming mechanism, we further demonstrate interference mitigation, multiuser access, and multiband multiuser access with the single-vapor-cell platform. Our results identify the continuous quantum aperture as a new operating principle of Rydberg atomic receivers and establish single-vapor-cell beamforming as an integrated and reconfigurable platform for spatially selective electromagnetic reception. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: This paper introduces the concept of continuous quantum aperture, in which a continuous atomic medium (a single Rydberg-atom vapor cell) performs efficient and reconfigurable beamforming

Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion

Authors: Zengyi Yang, Yu Liu, Juan Cheng, Zhiqin Zhu, Yafei Zhang, Huafeng Li

Abstract: Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with simultaneously adapting to multiple downstream tasks. To address this issue, we propose a Closed-Loop Dynamic Network (CLDyN) that can adaptively respond to the semantic requirements of diverse downstream tasks for task-customized image fusion. Specif… ▽ More Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with simultaneously adapting to multiple downstream tasks. To address this issue, we propose a Closed-Loop Dynamic Network (CLDyN) that can adaptively respond to the semantic requirements of diverse downstream tasks for task-customized image fusion. Specifically, CLDyN introduces a closed-loop optimization mechanism that establishes a semantic transmission chain to achieve explicit feedback from downstream tasks to the fusion network through a Requirement-driven Semantic Compensation (RSC) module. The RSC module leverages a Basis Vector Bank (BVB) and an Architecture-Adaptive Semantic Injection (A2SI) block to customize the network architecture according to task requirements, thereby enabling task-specific semantic compensation and allowing the fusion network to actively adapt to diverse tasks without retraining. To promote semantic compensation, a reward-penalty strategy is introduced to reward or penalize the RSC module based on task performance variations. Experiments on the M3FD, FMB, and VT5000 datasets demonstrate that CLDyN not only maintains high fusion quality but also exhibits strong multi-task adaptability. The code is available at https://github.com/YR0211/CLDyN. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Comments: This paper has been accepted by CVPR 2026

Test of lepton flavour universality with $B^0\to K^{*0}\ell^+\ell^-$ decays at large dilepton invariant mass

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis , et al. (1113 additional authors not shown)

Abstract: Muon-electron universality is tested in $B^0 \to K^{*0} \ \ell^+ \ell^-$ decays, in the dilepton-invariant-mass region above the $ψ(2S)$ resonance. The analysis uses beauty mesons produced in proton-proton collisions recorded by the LHCb detector at center-of-mass energies of 7, 8, and 13 $\text{TeV}$, corresponding to an integrated luminosity of 9 $\text{fb}^{-1}$. The ratio of branching fraction… ▽ More Muon-electron universality is tested in $B^0 \to K^{*0} \ \ell^+ \ell^-$ decays, in the dilepton-invariant-mass region above the $ψ(2S)$ resonance. The analysis uses beauty mesons produced in proton-proton collisions recorded by the LHCb detector at center-of-mass energies of 7, 8, and 13 $\text{TeV}$, corresponding to an integrated luminosity of 9 $\text{fb}^{-1}$. The ratio of branching fractions between the muon and electron channels, $R_{K^{*0}}$, is measured to be $1.08\,^{+0.14}_{-0.12}\text{(stat)} \ \pm 0.07\text{(syst)}$ for a dilepton-invariant-mass squared above 14.0 $\text{GeV}^{2}/\text{c}^{4}$, consistent with the Standard Model prediction. This result represents the most precise measurement of $R_{K^{*0}}$ in this region and the first such measurement performed at a hadron collider. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5670 (LHCb public pages)

Report number: LHCb-PAPER-2025-066, CERN-EP-2026-064

Search for the lepton-flavour violating decays $B^+ \to π^+ μ^\pm e^\mp$

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini , et al. (1105 additional authors not shown)

Abstract: The first search for the lepton-flavour violating decays $B^+ \to π^+ μ^{\pm} e^{\mp}$ in proton-proton collisions is presented, using data collected by the LHCb experiment between 2011 and 2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. No significant signal is observed and an upper limit on the branching fraction is set at… ▽ More The first search for the lepton-flavour violating decays $B^+ \to π^+ μ^{\pm} e^{\mp}$ in proton-proton collisions is presented, using data collected by the LHCb experiment between 2011 and 2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. No significant signal is observed and an upper limit on the branching fraction is set at $\mathcal{B}(B^+ \to π^+ μ^{\pm} e^{\mp}) < 1.8 \times 10^{-9}$ at the $90\%$ confidence level, two orders of magnitude more restrictive than the current world average. This is the first constraint on lepton-flavour violating $b \to d$ quark transitions at the LHC and also sets the most stringent upper limits to date on $b \to d μ^{\pm} e^{\mp}$ transitions. Limits on left-handed and scalar scenarios beyond the Standard Model are also reported. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/6012/ (LHCb public pages)

Report number: LHCb-PAPER-2026-013, CERN-EP-2026-093

An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations

Authors: Yichen Gao, Altay Unal, Akshay Rangamani, Zhihui Zhu

Abstract: While numerous machine unlearning (MU) methods have recently been developed with promising results in erasing the influence of forgotten data, classes, or concepts, they are also highly vulnerable-for example, simple fine-tuning can inadvertently reintroduce erased concepts. In this paper, we address this contradiction by examining the internal representations of unlearned models, in contrast to p… ▽ More While numerous machine unlearning (MU) methods have recently been developed with promising results in erasing the influence of forgotten data, classes, or concepts, they are also highly vulnerable-for example, simple fine-tuning can inadvertently reintroduce erased concepts. In this paper, we address this contradiction by examining the internal representations of unlearned models, in contrast to prior work that focuses primarily on output-level behavior. Our analysis shows that many state-of-the-art MU methods appear successful mainly due to a misalignment between last-layer features and the classifier, a phenomenon we call feature-classifier misalignment. In fact, hidden features remain highly discriminative, and simple linear probing can recover near-original accuracy. Assuming neural collapse in the original model, we further demonstrate that adjusting only the classifier can achieve negligible forget accuracy while preserving retain accuracy, and we corroborate this with experiments using classifier-only fine-tuning. Motivated by these findings, we propose MU methods based on a class-mean features (CMF) classifier, which explicitly enforces alignment between features and classifiers. Experiments on standard benchmarks show that CMF-based unlearning reduces forgotten information in representations while maintaining high retain accuracy, highlighting the need for faithful representation-level evaluation of MU. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Comments: 9 pages main text, 21 pages total, 6 figures. Accepted at AISTATS 2026

ViVa: A Video-Generative Value Model for Robot Reinforcement Learning

Authors: Jindi Lv, Hao Li, Jie Li, Yifei Nie, Fankun Kong, Yang Wang, Xiaofeng Wang, Zheng Zhu, Chaojun Ni, Qiuping Deng, Hengtao Li, Jiancheng Lv, Guan Huang

Abstract: Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback. Reinforcement learning addresses this via value functions, which assess task progress and guide policy improvement. However, existing value models built on vision-language models (VLMs) struggle to cap… ▽ More Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback. Reinforcement learning addresses this via value functions, which assess task progress and guide policy improvement. However, existing value models built on vision-language models (VLMs) struggle to capture temporal dynamics, undermining reliable value estimation in long-horizon tasks. In this paper, we propose ViVa, a video-generative value model that repurposes a pretrained video generator for value estimation. Taking the current observation and robot proprioception as input, ViVa jointly predicts future proprioception and a scalar value for the current state. By leveraging the spatiotemporal priors of a pretrained video generator, our approach grounds value estimation in anticipated embodiment dynamics, moving beyond static snapshots to intrinsically couple value with foresight. Integrated into RECAP, ViVa delivers substantial improvements on real-world box assembly. Qualitative analysis across all three tasks confirms that ViVa produces more reliable value signals, accurately reflecting task progress. By leveraging spatiotemporal priors from video corpora, ViVa also generalizes to novel objects, highlighting the promise of video-generative models for value estimation. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Differentiable hybrid force fields support scalable autonomous electrolyte discovery

Authors: Xintian Wang, Junmin Chen, Zhuoying Zhu, Peichen Zhong

Abstract: Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely heavily on error cancellation, while standard machine learning interatomic potentials (M… ▽ More Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely heavily on error cancellation, while standard machine learning interatomic potentials (MLIPs) are computationally expensive, lack rigorous long-range physics, and resist gradient-based calibration. In this Perspective, we highlight that differentiable hybrid FFs resolve this trilemma by fusing physically motivated functional forms with neural-network short-range corrections. Grounded in Energy Decomposition Analysis (EDA), state-of-the-art models such as PhyNEO-Electrolyte and ByteFF-Pol achieve zero-shot generalization to bulk phases, delivering throughputs on the order of tens of ns/day (up to $\sim$50 ns/day, depending on model complexity) for 10,000-atom systems. Crucially, their physical skeletons provide a well-conditioned parameter space for differentiable molecular dynamics (dMD). This enables a dual-calibration paradigm: bottom-up \textit{ab initio} parameterization combined with top-down fine-tuning from macroscopic experimental observables. We propose that this architecture meets the requirements of a ``ChemRobot-ready'' digital twin by integrating physics-grounded simulation with experimentally calibratable refinement, thereby enabling closed-loop autonomous electrolyte discovery. △ Less

Submitted 9 April, 2026; originally announced April 2026.

ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video

Authors: Boyuan Wang, Xiaofeng Wang, Yongkang Li, Zheng Zhu, Yifan Chang, Angen Ye, Guosheng Zhao, Chaojun Ni, Guan Huang, Yijie Ren, Yueqi Duan, Xingang Wang

Abstract: Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning or manual annotation, which limits practicality and generalizability. To address this, we propose ReconPhys, the first feedforward framework that jointly learns… ▽ More Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning or manual annotation, which limits practicality and generalizability. To address this, we propose ReconPhys, the first feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from a single monocular video. Our method employs a dual-branch architecture trained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneously infers geometry, appearance, and physical attributes. Experiments on a large-scale synthetic dataset demonstrate superior performance: our method achieves 21.64 PSNR in future prediction compared to 13.27 by state-of-the-art optimization baselines, while reducing Chamfer Distance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference (<1 second) versus hours required by existing methods, facilitating rapid generation of simulation-ready assets for robotics and graphics. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Granular Superconductivity in La$_{2}$PrNi$_{2}$O$_{7-δ}$ Thin Films

Authors: Ziao Han, Lifen Xiang, X. J. Zhou, Zhihai Zhu

Abstract: Superconductivity realized in bilayer nickelate thin films enables direct spectroscopic and transport studies at ambient pressure. However, a persistent two-step resistive transition remains a major barrier to achieving optimal superconducting properties. Here, we show that the two-step transition in La$_2$PrNi$_2$O$_{7-δ}$ thin films originates from the granular nature of superconductivity, speci… ▽ More Superconductivity realized in bilayer nickelate thin films enables direct spectroscopic and transport studies at ambient pressure. However, a persistent two-step resistive transition remains a major barrier to achieving optimal superconducting properties. Here, we show that the two-step transition in La$_2$PrNi$_2$O$_{7-δ}$ thin films originates from the granular nature of superconductivity, specifically, the coexistence of two distinct superconducting grain phases coupled by a Josephson junction network. A secondary, lower-temperature transition appears in the $R(T)$ curve, even when residual resistance becomes vanishingly small near 30 K. This two-step behavior significantly lowers the zero-resistance transition temperature, $T_{c, zero}$$\approx$ 10 K, and limits advanced spectroscopic studies. Our findings reveal the microscopic mechanism underlying the two-step transition in thin films and underscore the need for improved oxygen homogeneity to achieve bulk superconductivity in this system. △ Less

Submitted 9 April, 2026; originally announced April 2026.

How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles

Authors: Chenchen Kuai, Jiwan Jiang, Zihao Zhu, Hao Wang, Keshu Wu, Zihao Li, Yunlong Zhang, Chenxi Liu, Zhengzhong Tu, Zhiwen Fan, Yang Zhou

Abstract: The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signa… ▽ More The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signals. In practice, this manifests as correlated reasoning patterns and synchronized failures, where apparent agreement reflects shared error modes rather than independent validation. To address this, we develop a statistical framework for auditing behavioral entanglement among black-box LLMs. Our approach introduces a multi-resolution hierarchy that characterizes the joint failure manifold through two information-theoretic metrics: (i) a Difficulty-Weighted Behavioral Entanglement Index, which amplifies synchronized failures on easy tasks, and (ii) a Cumulative Information Gain (CIG) metric, which captures directional alignment in erroneous responses. Through extensive experiments on 18 LLMs from six model families, we identify widespread behavioral entanglement and analyze its impact on LLM-as-a-judge evaluation. We find that CIG exhibits a statistically significant association with degradation in judge precision, with Spearman coefficient of 0.64 (p < 0.001) for GPT-4o-mini and 0.71 (p < 0.01) for Llama3-based judges, indicating that stronger dependency corresponds to increased over-endorsement bias. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting. By adjusting model contributions based on inferred independence, the proposed method mitigates correlated bias and improves verification performance, achieving up to a 4.5% accuracy gain over majority voting. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Comments: 9 pages, 4 figures

OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

Authors: Jianhui Liu, Haoze Sun, Wenbo Li, Yanbing Zhang, Rui Yang, Zhiliang Zhu, Yijun Yang, Shenghe Zheng, Nan Jiang, Jiaxiu Jiang, Haoyang Huang, Tien-Tsin Wong, Nan Duan, Xiaojuan Qi

Abstract: Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation syst… ▽ More Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation system and introduce OpenSpatial -- an open-source data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency. OpenSpatial adopts 3D bounding boxes as the fundamental primitive to construct a comprehensive data hierarchy across five foundational tasks: Spatial Measurement (SM), Spatial Relationship (SR), Camera Perception (CP), Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Leveraging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive evaluations demonstrate that versatile models trained on our dataset achieve state-of-the-art performance across a wide spectrum of spatial reasoning benchmarks. Notably, the best-performing model exhibits a substantial average improvement of 19 percent, relatively. Furthermore, we provide a systematic analysis of how data attributes influence spatial perception. By open-sourcing both the engine and the 3M-scale dataset, we provide a robust foundation to accelerate future research in spatial intelligence. △ Less

Submitted 9 April, 2026; v1 submitted 8 April, 2026; originally announced April 2026.

Comments: Code: https://github.com/VINHYU/OpenSpatial

Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation

Authors: Jianing Zhang, Runan Li, Honglin Pang, Ding Xia, Zhou Zhu, Qian Zhang, Chuntao Li, Xi Yang

Abstract: Deciphering ancient Chinese Oracle Bone Script (OBS) is a challenging task that offers insights into the beliefs, systems, and culture of the ancient era. Existing approaches treat decipherment as a closed-set image recognition problem, which fails to bridge the ``interpretation gap'': while individual characters are often unique and rare, they are composed of a limited set of recurring, pictograp… ▽ More Deciphering ancient Chinese Oracle Bone Script (OBS) is a challenging task that offers insights into the beliefs, systems, and culture of the ancient era. Existing approaches treat decipherment as a closed-set image recognition problem, which fails to bridge the ``interpretation gap'': while individual characters are often unique and rare, they are composed of a limited set of recurring, pictographic components that carry transferable semantic meanings. To leverage this structural logic, we propose an agent-driven Vision-Language Model (VLM) framework that integrates a VLM for precise visual grounding with an LLM-based agent to automate a reasoning chain of component identification, graph-based knowledge retrieval, and relationship inference for linguistically accurate interpretation. To support this, we also introduce OB-Radix, an expert-annotated dataset providing structural and semantic data absent from prior corpora, comprising 1,022 character images (934 unique characters) and 1,853 fine-grained component images across 478 distinct components with verified explanations. By evaluating our system across three benchmarks of different tasks, we demonstrate that our framework yields more detailed and precise decipherments compared to baseline methods. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Distributed Interpretability and Control for Large Language Models

Authors: Dev Arpan Desai, Shaoyi Huang, Zining Zhu

Abstract: Large language models that require multiple GPU cards to host are usually the most capable models. It is necessary to understand and steer these models, but the current technologies do not support the interpretability and steering of these models in the multi-GPU setting as well as the single-GPU setting. We present a practical implementation of activation-level interpretability (logit lens) and s… ▽ More Large language models that require multiple GPU cards to host are usually the most capable models. It is necessary to understand and steer these models, but the current technologies do not support the interpretability and steering of these models in the multi-GPU setting as well as the single-GPU setting. We present a practical implementation of activation-level interpretability (logit lens) and steering (steering vector) that scales up to multi-GPU language models. Our system implements design choices that reduce the activation memory by up to 7x and increase the throughput by up to 41x compared to a baseline on identical hardware. We demonstrate the method across LLaMA-3.1 (8B, 70B) and Qwen-3 (4B, 14B, 32B), sustaining 20-100 tokens/s while collecting full layer-wise activation trajectories for sequences of 1,500 tokens. Using label-position steering vectors injected post-LayerNorm, we show controllable, monotonic shifts in model outputs with a mean steerability slope of 0.702 across evaluated datasets, without fine-tuning or additional forward passes. We release detailed benchmarks, ablations, and a reproducible instrumentation recipe to enable practical interpretability and real-time behavioral control for frontier LLMs at https://github.com/Devdesai1901/LogitLense. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Precise measurement of the CKM angle $γ$ with a novel approach

Authors: The BESIII, LHCb Collaborations, :, 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, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco , et al. (1936 additional authors not shown)

Abstract: A measurement of the CKM angle $γ$ is performed by applying a novel, unbinned, model-independent approach to datasets of electron-positron collisions collected by the BESIII experiment and proton-proton collisions by the LHCb experiment, corresponding to integrated luminosities of 8 fb$^{-1}$ and 9 fb$^{-1}$, respectively. The $C\!P$-violating phase $γ$ is determined from… ▽ More A measurement of the CKM angle $γ$ is performed by applying a novel, unbinned, model-independent approach to datasets of electron-positron collisions collected by the BESIII experiment and proton-proton collisions by the LHCb experiment, corresponding to integrated luminosities of 8 fb$^{-1}$ and 9 fb$^{-1}$, respectively. The $C\!P$-violating phase $γ$ is determined from ${B^{\pm}\rightarrow D(\rightarrow K_{\rm S}^{0} h^{\prime+}h^{\prime-}) h^{\pm}}$ decays in LHCb data, where $h^{(\prime)}$ is either a pion or kaon, while the corresponding strong-phase parameters are measured using doubly tagged ${D\rightarrow K_{\rm S/L}^0 h^{\prime+} h^{\prime-}}$ decays in the quantum-correlated $D\overline{D}$ system present in BESIII data. A joint fit to both datasets, which allows for a simultaneous determination of the associated $C\!P$-violating observables and strong-phase parameters, yields ${γ= (71.3\pm 5.0)^{\circ}}$. The result is the most precise to date and consistent with previous measurements and world averages. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5991/ (LHCb public pages)

Report number: LHCb-PAPER-2025-064, CERN-EP-2026-068

Measurement of the CKM angle $γ$ in $B^{\pm} \rightarrow D(\rightarrow K^{0}_{\rm S} h^{\prime+}h^{\prime-})h^{\pm}$ decays with a novel approach

Authors: The BESIII, LHCb Collaborations, :, 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, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco , et al. (1936 additional authors not shown)

Abstract: A measurement of the CKM angle $γ$ and related strong-phase parameters is performed using a novel, model-independent approach in ${B^{\pm}\rightarrow D(\rightarrow K^{0}_{\rm S} h^{\prime+}h^{\prime-}) h^{\pm}}$ decays, where $h^{(\prime)} \equiv π, K$. The analysis uses a joint data sample of electron-positron collisions collected by the BESIII experiment at the Beijing Electron-Positron Collider… ▽ More A measurement of the CKM angle $γ$ and related strong-phase parameters is performed using a novel, model-independent approach in ${B^{\pm}\rightarrow D(\rightarrow K^{0}_{\rm S} h^{\prime+}h^{\prime-}) h^{\pm}}$ decays, where $h^{(\prime)} \equiv π, K$. The analysis uses a joint data sample of electron-positron collisions collected by the BESIII experiment at the Beijing Electron-Positron Collider II during 2010--2011 and 2021--2022, corresponding to an integrated luminosity of 8 fb$^{-1}$, and proton-proton collisions collected by the LHCb experiment at the Large Hadron Collider during 2011--2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. The two datasets are analyzed simultaneously by applying per-event weights based on the amplitude variation over the $D$-decay phase space to enhance the sensitivity to $C\!P$-violating observables. The CKM angle $γ$ is determined to be $γ= (71.3\pm 5.0)^{\circ}$, which constitutes the most precise single measurement to date. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3989/ (LHCb public pages)

Report number: LHCb-PAPER-2025-063, CERN-EP-2026-067

Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs

Authors: Hongcheng Liu, Yuhao Wang, Zhe Chen, Pingjie Wang, Zhiyuan Zhu, Yixuan Hou, Yanfeng Wang, Yu Wang

Abstract: Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this as… ▽ More Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this aspect has been largely overlooked. To bridge this gap, we formalize the challenge as a cross-modal coreference problem, where a model must localize a referent in a source modality and re-identify it in a target modality. Building on this paradigm, we introduce CrossOmni, a dataset comprising nine tasks equipped with human-designed reasoning rationales to evaluate and enhance this capability. Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference, which we attribute to the absence of coreference-aware thinking patterns. To address this, we enhance cross-modal alignment via two strategies: a training-free In-Context Learning method and a training-based SFT+GRPO framework designed to induce such thinking patterns. Both approaches yield substantial performance gains and generalize effectively to collaborative reasoning tasks. Overall, our findings highlight cross-modal coreference as a crucial missing piece for advancing robust omni-modal reasoning. △ Less

Submitted 7 April, 2026; originally announced April 2026.

UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning

Authors: Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi

Abstract: A fundamental challenge in creative writing lies in reconciling the inherent tension between maintaining global coherence in long-form narratives and preserving local expressiveness in short-form texts. While long-context generation necessitates explicit macroscopic planning, short-form creativity often demands spontaneous, constraint-free expression. Existing alignment paradigms, however, typical… ▽ More A fundamental challenge in creative writing lies in reconciling the inherent tension between maintaining global coherence in long-form narratives and preserving local expressiveness in short-form texts. While long-context generation necessitates explicit macroscopic planning, short-form creativity often demands spontaneous, constraint-free expression. Existing alignment paradigms, however, typically employ static reward signals and rely heavily on high-quality supervised data, which is costly and difficult to scale. To address this, we propose \textbf{UniCreative}, a unified reference-free reinforcement learning framework. We first introduce \textbf{AC-GenRM}, an adaptive constraint-aware reward model that dynamically synthesizes query-specific criteria to provide fine-grained preference judgments. Leveraging these signals, we propose \textbf{ACPO}, a policy optimization algorithm that aligns models with human preferences across both content quality and structural paradigms without supervised fine-tuning and ground-truth references. Empirical results demonstrate that AC-GenRM aligns closely with expert evaluations, while ACPO significantly enhances performance across diverse writing tasks. Crucially, our analysis reveals an emergent meta-cognitive ability: the model learns to autonomously differentiate between tasks requiring rigorous planning and those favoring direct generation, validating the effectiveness of our direct alignment approach. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: Accepted to Findings of ACL 2026

Reconstruction of fast-rotating neutron star observables with the neural network

Authors: Wen Liu, Lingxiao Wang, Zhenyu Zhu

Abstract: Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making traditional calculations too expensive for inference analyses that demand a large amount of model evaluations. We develop a causal convolutional neural networks that preserve the chronological-like dependence of NS proper… ▽ More Rotation can significantly affect neutron-star (NS) properties, but accurate modeling of rapidly rotating NSs requires solving a two-dimensional, axially symmetric system, making traditional calculations too expensive for inference analyses that demand a large amount of model evaluations. We develop a causal convolutional neural networks that preserve the chronological-like dependence of NS properties on the equation of state (EoS) and rapidly reconstruct observables for static, Keplerian, and rotating configurations. Using \texttt{RNS}, we generate a dataset of NS observables and use it to train our networks. We validate our networks with three representative EoS (SFHo, SLy4, and DD2) and find that the they accurately reproduce the \texttt{RNS} results. The trained networks evaluate NS configurations for a single EoS in $\sim 50$ms, providing a substantial speedup over typical \texttt{RNS} runtimes of $\sim 30$ min and enabling efficient inference analyses involving rapidly rotating NSs. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 12 pages, 7 figures

Report number: RIKEN-iTHEMS-Report-26

Topological surface states revealed by the Zeeman effect in superconducting UTe2

Authors: Zhen Zhu, Hans Christiansen, Yudi Huang, Kaiming Liu, Zheyu Wu, Shanta R. Saha, Johnpierre Paglione, Alexander G. Eaton, Andrej Cabala, Michal Vališka, Rafael M. Fernandes, Andreas Kreisel, Brian M. Andersen, Vidya Madhavan

Abstract: Intrinsic topological superconductors with protected boundary modes obeying non-Abelian statistics constitute a vanishingly small class of quantum materials. A defining spectroscopic signature of such phases is the presence of in-gap topological surface states (TSS). However, despite extensive theoretical proposals, their unambiguous experimental identification has remained elusive. Here we use ve… ▽ More Intrinsic topological superconductors with protected boundary modes obeying non-Abelian statistics constitute a vanishingly small class of quantum materials. A defining spectroscopic signature of such phases is the presence of in-gap topological surface states (TSS). However, despite extensive theoretical proposals, their unambiguous experimental identification has remained elusive. Here we use vector magnetic-field scanning tunnelling microscopy to obtain direct spectroscopic evidence of TSS in the spin-triplet superconductor UTe2. Atomic-scale spectroscopy reveals striking site-dependent superconductivity: Te sites host a large in-gap density of states that nearly fills the superconducting gap, whereas neighboring atomic sites remain gapped. Upon application of a magnetic field, the in-gap states on the Te sites are selectively suppressed, yielding a spatially homogeneous superconducting state with a markedly deeper gap relative to zero field. This site-selective gap evolution is in quantitative agreement with theoretical predictions for TSS in UTe2 that possess dominant Te-orbital character. Spectral-function calculations incorporating the Zeeman coupling reproduce the observed magnetic-field response. Our results provide a spectroscopic fingerprint of the long-sought TSS in superconductors and establish UTe2 as a compelling system for exploring intrinsic topological superconductivity. △ Less

Submitted 6 April, 2026; originally announced April 2026.

Comments: Main text: 17 pages, 5 figures; Supplementary Information: 12 pages, 9 figures

ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration

Authors: Rongfeng Zhao, Xuanhao Zhang, Zhaochen Guo, Xiang Shao, Zhongpan Zhu, Bin He, Jie Chen

Abstract: The integration of large language models (LLMs) with embodied agents has improved high-level reasoning capabilities; however, a critical gap remains between semantic understanding and physical execution. While vision-language-action (VLA) and vision-language-navigation (VLN) systems enable robots to perform manipulation and navigation tasks from natural language instructions, they still struggle w… ▽ More The integration of large language models (LLMs) with embodied agents has improved high-level reasoning capabilities; however, a critical gap remains between semantic understanding and physical execution. While vision-language-action (VLA) and vision-language-navigation (VLN) systems enable robots to perform manipulation and navigation tasks from natural language instructions, they still struggle with long-horizon sequential and temporally structured tasks. Existing frameworks typically adopt modular pipelines for data collection, skill training, and policy deployment, resulting in high costs in experimental validation and policy optimization. To address these limitations, we propose ROSClaw, an agent framework for heterogeneous robots that integrates policy learning and task execution within a unified vision-language model (VLM) controller. The framework leverages e-URDF representations of heterogeneous robots as physical constraints to construct a sim-to-real topological mapping, enabling real-time access to the physical states of both simulated and real-world agents. We further incorporate a data collection and state accumulation mechanism that stores robot states, multimodal observations, and execution trajectories during real-world execution, enabling subsequent iterative policy optimization. During deployment, a unified agent maintains semantic continuity between reasoning and execution, and dynamically assigns task-specific control to different agents, thereby improving robustness in multi-policy execution. By establishing an autonomous closed-loop framework, ROSClaw minimizes the reliance on robot-specific development workflows. The framework supports hardware-level validation, automated generation of SDK-level control programs, and tool-based execution, enabling rapid cross-platform transfer and continual improvement of robotic skills. Ours project page: https://www.rosclaw.io/. △ Less

Submitted 6 April, 2026; originally announced April 2026.

SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests

Authors: Wei Zhou, Yue Shen, Junkai Ji, Yinglan Feng, Xing Tang, Xiuqiang He, Liang Feng, Zexuan Zhu

Abstract: User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose… ▽ More User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over time. Unlike conventional models that combine long- and short-term user interests into a single representation, compromising recommendation accuracy, SLSRec utilizes a self-supervised learning framework to disentangle these two types of interests. A contrastive learning strategy is introduced to ensure accurate calibration of long- and short-term interest representations. Additionally, an attention-based fusion network is designed to adaptively aggregate interest representations, optimizing their integration to enhance recommendation performance. Extensive experiments on three public benchmark datasets demonstrate that SLSRec consistently outperforms state-of-the-art models while exhibiting superior robustness across various scenarios.We will release all source code upon acceptance. △ Less

Submitted 6 April, 2026; originally announced April 2026.

MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation

Authors: Zhe Feng, Shilong Tao, Haonan Sun, Shaohan Chen, Zhanxing Zhu, Yunhuai Liu

Abstract: Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches tend to overlook hig… ▽ More Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches tend to overlook higher-dimensional spatial features, e.g., 2D facets and 3D cells, from the original geometry. As a result, it is challenging to accurately capture boundary representations and volumetric characteristics, though this information is critically important for modeling contact interactions and internal physical quantity propagation, particularly under sparse mesh discretization. In this paper, we introduce MAVEN, a mesh-aware volumetric encoding network for simulating 3D flexible deformation, which explicitly models geometric mesh elements of higher dimension to achieve a more accurate and natural physical simulation. MAVEN establishes learnable mappings among 3D cells, 2D facets, and vertices, enabling flexible mutual transformations. Explicit geometric features are incorporated into the model to alleviate the burden of implicitly learning geometric patterns. Experimental results show that MAVEN consistently achieves state-of-the-art performance across established datasets and a novel metal stretch-bending task featuring large deformations and prolonged contacts. △ Less

Submitted 6 April, 2026; originally announced April 2026.

A Logical-Rule Autoencoder for Interpretable Recommendations

Authors: Jinhao Pan, Bowen Wei, Ziwei Zhu

Abstract: Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require transparency and accountability. In this work, we propose a Logical-rule Interpretable Autoencoder (LIA) for collaborative filtering that is interpretable by design. LIA introduce… ▽ More Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require transparency and accountability. In this work, we propose a Logical-rule Interpretable Autoencoder (LIA) for collaborative filtering that is interpretable by design. LIA introduces a learnable logical rule layer in which each rule neuron is equipped with a gate parameter that automatically selects between AND and OR operators during training, enabling the model to discover diverse logical patterns directly from data. To support functional completeness without doubling the input dimensionality, LIA encodes negation through the sign of connection weights, providing a parameter-efficient mechanism for expressing both positive and negated item conditions within each rule. By learning explicit, human-readable reconstruction rules, LIA allows users to directly trace the decision process behind each recommendation. Extensive experiments show that our method achieves improved recommendation performance over traditional baselines while remaining fully interpretable. Code and data are available at https://github.com/weibowen555/LIA. △ Less

Submitted 5 April, 2026; originally announced April 2026.

Disentangling electronic and phononic contributions to high-temperature superconductivity in X2MH6 hydrides

Authors: Feng Zheng, Shiya Chen, Zhen Zhang, Renhai Wang, Feng Zhang, Zi-zhong Zhu, Cai-Zhuang Wang, Vladimir Antropov, Yang Sun, Kai-Ming Ho

Abstract: Understanding the factors that control superconductivity is essential for discovering new superconducting materials using high-throughput elemental substitution. Focusing on the recently predicted ambient-pressure superconducting X2MH6 family, we disentangle the phononic and electronic contributions to Tc to determine how isoelectronic substitution alters superconductivity. While substitution affe… ▽ More Understanding the factors that control superconductivity is essential for discovering new superconducting materials using high-throughput elemental substitution. Focusing on the recently predicted ambient-pressure superconducting X2MH6 family, we disentangle the phononic and electronic contributions to Tc to determine how isoelectronic substitution alters superconductivity. While substitution affects both phononic and electronic properties, the electronic contribution plays the dominant role in determining Tc in the X2MH6 family. We show that the electronic contribution is affected by three key factors: the X-H bond distance, the electron localization function networking value of hydrogen, and the hydrogen-projected density of states at the Fermi level. A combined figure of merit derived from these parameters exhibits a robust correlation with Tc across the family. We further show that pressure produces competing effects on superconductivity: it enhances the electronic contribution by shortening X-H bonds, but simultaneously weaken the phononic contribution by increasing phonon frequencies. The net pressure dependence of Tc therefore results from the balance between these opposing tendencies. By disentangling and analyzing the electronic and phononic mechanisms, this work provides comprehensive insight into superconductivity in X2MH6 hydrides and offers practical guidance for designing new high-Tc hydride superconductors. △ Less

Submitted 5 April, 2026; originally announced April 2026.

Interaction driven transverse thermal resistivity in a phonon gas

Authors: Xiaodong Guo, Xiaokang Li, Alaska Subedi, Zengwei Zhu, Kamran Behnia

Abstract: The amplitude of the Hall response of electrons can be understood without invoking interactions. Most theories of the phonon thermal Hall effect have likewise opted for a non-interacting picture. Here, we challenge this approach. Our study of WS$_2$, a transition metal dichalcogenide (TMD) insulator, finds that longitudinal, $κ_{xx}$, and transverse, $κ_{xy}$, thermal conductivities peak at almost… ▽ More The amplitude of the Hall response of electrons can be understood without invoking interactions. Most theories of the phonon thermal Hall effect have likewise opted for a non-interacting picture. Here, we challenge this approach. Our study of WS$_2$, a transition metal dichalcogenide (TMD) insulator, finds that longitudinal, $κ_{xx}$, and transverse, $κ_{xy}$, thermal conductivities peak at almost the same temperature. Their ratio obeys an upper bound, as in other insulators. We then compare transverse thermal transport in a phonon gas and in a molecular gas. In the latter, the Senftleben-Beenakker effect is driven by the competition between molecular collisions and applied magnetic field in setting the distribution of molecular angular momenta. An off-diagonal transport response arises thanks to interactions between non-spherical particles, which do not need to be chiral. By analogy, we argue that in a phonon gas, magnetic field will influence phonon-phonon interactions, and generates a transverse thermal \emph{resistivity}, whose order of magnitude can be accounted for by invoking a Berry force on the drift velocity of the nuclei in the presence of a finite heat. This simple picture gives a reasonable account of the experimentally measured transverse thermal resistivity of seven different crystalline insulators. △ Less

Submitted 4 April, 2026; originally announced April 2026.

Comments: 14 pages, 9 figures

FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models

Authors: Kehan Jiang, Haonan Dong, Zhaolu Kang, Zhengzhou Zhu, Guojie Song

Abstract: Recent Large Reasoning Models (LRMs) like DeepSeek-R1 have demonstrated remarkable success in complex reasoning tasks, exhibiting human-like patterns in exploring multiple alternative solutions. Upon closer inspection, however, we uncover a surprising phenomenon: The First is The Best, where alternative solutions are not merely suboptimal but potentially detrimental. This observation challenges wi… ▽ More Recent Large Reasoning Models (LRMs) like DeepSeek-R1 have demonstrated remarkable success in complex reasoning tasks, exhibiting human-like patterns in exploring multiple alternative solutions. Upon closer inspection, however, we uncover a surprising phenomenon: The First is The Best, where alternative solutions are not merely suboptimal but potentially detrimental. This observation challenges widely accepted test-time scaling laws, leading us to hypothesize that errors within the reasoning path scale concurrently with test time. Through comprehensive empirical analysis, we characterize errors as a forest-structured Forest of Errors (FoE) and conclude that FoE makes the First the Best, which is underpinned by rigorous theoretical analysis. Leveraging these insights, we propose RED, a self-guided efficient reasoning framework comprising two components: I) Refining First, which suppresses FoE growth in the first solution; and II) Discarding Subs, which prunes subsequent FoE via dual-consistency. Extensive experiments across five benchmarks and six backbone models demonstrate that RED outperforms eight competitive baselines, achieving performance gains of up to 19.0% while reducing token consumption by 37.7% ~ 70.4%. Moreover, comparative experiments on FoE metrics shed light on how RED achieves effectiveness. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Search for the decays $B_{(s)}^0\to J/ψγ$ at LHCb

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis , et al. (1114 additional authors not shown)

Abstract: A search for the rare decays $B_{(s)}^0\to J/ψγ$ is performed with proton-proton collision data collected by the LHCb experiment, corresponding to integrated luminosities of $3~\rm{fb}^{-1}$ at centre-of-mass energies of 7 and 8 TeV, and $6~\rm{fb}^{-1}$ at 13 TeV. Assuming no contribution from $B^0\to J/ψγ$ decay, an upper limit is set on the branching fraction… ▽ More A search for the rare decays $B_{(s)}^0\to J/ψγ$ is performed with proton-proton collision data collected by the LHCb experiment, corresponding to integrated luminosities of $3~\rm{fb}^{-1}$ at centre-of-mass energies of 7 and 8 TeV, and $6~\rm{fb}^{-1}$ at 13 TeV. Assuming no contribution from $B^0\to J/ψγ$ decay, an upper limit is set on the branching fraction $\mathcal{B}(B_{s}^0\to J/ψγ)<2.9\times10^{-6}$ at the 90% confidence level. If instead no contribution from $B_{s}^0\to J/ψγ$ decay is assumed, the limit is $\mathcal{B}(B^0\to J/ψγ)<2.5\times10^{-6}$ at the 90% confidence level. These results supersede the previous LHCb results, with the limit for $B_{s}^0\to J/ψγ$ improved by a factor of 2.5. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/5672/ (LHCb public pages)

Report number: LHCb-PAPER-2025-065, CERN-EP-2026-060

Mott-Derived Local Moments and Kondo Hybridization in a d-electron Kagome lattice

Authors: Xing Zhang, Xintong Li, Boqin Song, Yuyang Xie, Qinghong Wang, Taimin Miao, Shusen Ye, Junhao Liu, Bo Liang, Neng Cai, Hao Chen, Wenpei Zhu, Mingkai Xu, Wei-Jian Li, Shun-Li Yu, Shenjin Zhang, Fengfeng Zhang, Feng Yang, Zhimin Wang, Qinjun Peng, Hanqing Mao, Zhihai Zhu, Guodong Liu, Zuyan Xu, Yi-feng Yang , et al. (3 additional authors not shown)

Abstract: Unlike canonical Kondo lattices in f-electron systems, where localized f orbitalsnaturally provide local moments, d-electron Kondo lattices require a distinct mechanism for local-moment formation. However, the study of d-electron Kondo lattices in bulk materials remains far from settled, particularly with regard to the microscopic origin of the local moments. Here, we report a microscopic mechanis… ▽ More Unlike canonical Kondo lattices in f-electron systems, where localized f orbitalsnaturally provide local moments, d-electron Kondo lattices require a distinct mechanism for local-moment formation. However, the study of d-electron Kondo lattices in bulk materials remains far from settled, particularly with regard to the microscopic origin of the local moments. Here, we report a microscopic mechanism for this process in the bilayer kagome metal CsCr6Sb6, where strong correlations drive a Mott splitting of the kagome flat band to supply the requisite local moments. By combining STM/STS and ARPES, we resolve a spectroscopic hierarchy between high-energy correlation effects and low temperature hybridization. Low-temperature STS reveals a robust asymmetric suppression of the density of states near EF that is well captured phenomenologically by a Fano-type lineshape, while ARPES detects a sharp quasiparticlepeak near EF. These low-energy signatures evolveon the same temperature scale and disappear upon warming, consistent with the onset of Kondo hybridization. At the same time, STS resolves symmetric humps at approximately +-50 mV and ARPES identifies a weakly dispersive feature around 50 meV below EF; unlike the near-EF hybridization signatures, these features persist to substantially higher temperatures. This separation of energy and temperature scales supports a two-stage picture in which a kagome flat band first undergoes correlation-driven splitting into lower and upper Hubbard bands, and the occupied lower Hubbard band supplies the local moments that later hybridize with itinerant electrons at lower temperature. Our results therefore move beyond the phenomenology of a kagome Kondo lattice candidate and instead provide a microscopic spectroscopic picture linking Mottness to Kondo hybridization in a frustrated d-electron system. △ Less

Submitted 3 April, 2026; originally announced April 2026.