Showing 1–50 of 2,962 results for author: Han, X

Angular analysis of the decay ${\it Λ}_{\it b}^{0} \to {\it Λ}(1520){\it μ^{+}μ^{-}}$

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, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, A. A. Alves Jr, S. Amato, J. L. Amey, Y. Amhis , et al. (1167 additional authors not shown)

Abstract: The first angular analysis of ${\it Λ}_{\it b}^{0} \to {\it Λ}(1520){\it μ^{+}μ^{-}}$ decays is presented, using proton-proton collision data collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. The leptonic forward-backward asymmetry, $A_\text{FB, 3/2}^\ell$, and the $CP$-averaged angular observable, $S_{1cc}$, are determined by fitting… ▽ More The first angular analysis of ${\it Λ}_{\it b}^{0} \to {\it Λ}(1520){\it μ^{+}μ^{-}}$ decays is presented, using proton-proton collision data collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. The leptonic forward-backward asymmetry, $A_\text{FB, 3/2}^\ell$, and the $CP$-averaged angular observable, $S_{1cc}$, are determined by fitting projections of the angular distributions in four intervals of the square of the dimuon invariant mass between 0.1 and 12.5 GeV$^2/c^4$. The results are in good agreement with predictions based on the Standard Model of particle physics. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/6206/ (LHCb public pages)

Report number: LHCb-PAPER-2026-037, CERN-EP-2026-240

Evidence for $η_{c}(2S)\to p\bar{p}π^{+}π^{-}π^{0}$ and observation of $χ_{cJ} \to p\bar{p}π^{+}π^{-}π^{0}$

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. (750 additional authors not shown)

Abstract: Using $(2.712\pm0.014)\times 10^9$ $ψ(3686)$ events collected by the BESIII detector at the BEPCII collider, the $ψ(3686) \to γp\bar{p}π^+π^-π^0$ process is investigated. Evidence for the decay of $η_{c}(2S)\to p\bar{p}π^{+}π^{-}π^{0}$ is found with a signal significance of 3.3$σ$. The product of branching fractions of… ▽ More Using $(2.712\pm0.014)\times 10^9$ $ψ(3686)$ events collected by the BESIII detector at the BEPCII collider, the $ψ(3686) \to γp\bar{p}π^+π^-π^0$ process is investigated. Evidence for the decay of $η_{c}(2S)\to p\bar{p}π^{+}π^{-}π^{0}$ is found with a signal significance of 3.3$σ$. The product of branching fractions of $\mathcal{B}[ψ(3686)\to γη_{c}(2S)]\times\mathcal{B}[η_{c}(2S)\to p\bar{p}π^{+}π^{-}π^{0}]$ is determined to be $(3.4\pm0.5\pm0.8) \times 10^{-6}$, where the first uncertainty is statistical and the second systematic. The hadronic decays of $χ_{cJ} \to p\bar{p}π^+π^-π^0$$~(J=0,1,2)$ are observed, and their branching fractions are measured to be $\mathcal{B}(χ_{c0}\to p\bar{p}π^{+}π^{-}π^{0})=(4.79\pm 0.01\pm0.40) \times 10^{-3}$, $\mathcal{B}(χ_{c1}\to p\bar{p}π^{+}π^{-}π^{0})=(2.13\pm 0.01\pm0.17) \times 10^{-3}$, and $\mathcal{B}(χ_{c2}\to p\bar{p}π^{+}π^{-}π^{0})=(3.72\pm 0.01\pm0.29) \times 10^{-3}$, respectively. Furthermore, the branching fractions for the intermediate processes $χ_{cJ}\to p\bar{p}ω$ are updated with significantly improved precision: $\mathcal{B}(χ_{c0}\to p\bar{p}ω)=(5.76\pm0.01\pm0.42)\times10^{-4}$, $\mathcal{B}(χ_{c1}\to p\bar{p}ω)=(1.85\pm0.01\pm0.13)\times10^{-4}$, and $\mathcal{B}(χ_{c2}\to p\bar{p}ω)=(4.51\pm0.01\pm0.33)\times10^{-4}$, respectively. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Scaling Muon for Diffusion Transformers

Authors: Chenghao Li, Xiao Han, Xinxin Huang, Wei Liu, Boyang Li, Bing Xiao, Heran Zhang, Juanma Perez Rua, Ke Xu, Kangning Liu, Linjun Kuang, Na Li, Tan Wang, Tian Xie, Wei Peng, Yang Pei, Yifan Xu, Yuanhao Zhai, Yuwei Lin, Zhe Wang, Zihao He, Daniel Li, Junbiao Tang, Ziyang Jiang, Dake Chen

Abstract: The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large Diffusion Transformers (DiTs) remain unclear. We first establish Muon's scaling behavior on DiTs from 1.3B to 15B parameters, showing that its optimization and generative quality advantages over AdamW persist across model scales.… ▽ More The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large Diffusion Transformers (DiTs) remain unclear. We first establish Muon's scaling behavior on DiTs from 1.3B to 15B parameters, showing that its optimization and generative quality advantages over AdamW persist across model scales. However, at scale, the 5-step Newton--Schulz iteration (NS5) performed at every optimization step, together with full-momentum materialization, introduces substantial computation and communication overhead that can offset Muon's step-efficiency advantage. We introduce \emph{Periodic Row-wise Muon}, which performs a full NS5 spectral update once every \(K\) steps and applies a low compute and communication cost row-wise constrained update based on the current momentum at the remaining steps. We further co-design a distributed implementation that operates directly on sharded momentum during non-refresh steps and accelerates spectral refreshes through bucketed all-gather and communication--computation overlap. Across all scales, Muon improves the best observed generative quality over AdamW by 12.9--19.1\%. Compared with vanilla Muon, Periodic Row-wise Muon remains within 0.5\% in best generative quality on the 1.3B--4B models and improves it by 4.5\% at 9B. It reduces optimizer time by 46.9--54.3\%, end-to-end step time by 15.7--24.3\%, and logical communication volume by 66.7\%, while reaching its respective best generative quality with 33.7--64.8\% less active training time. These results show that Periodic Row-wise Muon preserves Muon's generative quality advantage while translating it into end-to-end training efficiency for large DiTs. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Aggregating Visual Information with Optimal Transport for VideoLM Token Compression

Authors: Wenti Yin, Xiaotian Han, Junyuan Shang, Yuchen Ding, Shuohuan Wang, Dianhai Yu, Changxin Gao, Nong Sang

Abstract: Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing the visual-token burden on language-model decoding. The central challenge is to preserve visual information dispersed across frames under such compression. To this end, we introduce Aggregating Visual Information with Opt… ▽ More Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing the visual-token burden on language-model decoding. The central challenge is to preserve visual information dispersed across frames under such compression. To this end, we introduce Aggregating Visual Information with Optimal Transport (AVIOT), which casts video token compression as transporting a dense empirical measure of frame observations onto a compact target measure. The resulting source-to-target coupling induces a distribution over source observations for each target support, directly specifying how the compressed video representation is constructed. We further adapt this construction along task and spatial axes. Question conditioning modulates the transport cost between source frames and target supports, while influencing how many supports are allocated to each temporal segment, thereby directing representation capacity toward question-relevant content. At multiple spatial granularities, AVIOT computes region-specific temporal transport plans and adaptively fuses the representations they yield, allowing different regions within the same compact representation to draw from different moments. Evaluations across varying compression ratios show that AVIOT matches or outperforms the uncompressed baseline on multiple video-understanding benchmarks while retaining strong performance at higher compression ratios. △ Less

Submitted 20 August, 2026; originally announced August 2026.

Comments: Code: https://github.com/ernie-research/AVIOT

Tilted $p$-wave magnet candidate CeNiAsO

Authors: Zhuo Wang, Zheng Liu, Shuo Zou, Hua-Xun Li, Jin-Xin Hu, Zhuolun Qiu, Ze Wang, Jiamin Gong, Lucheng Wei, Kangjian Luo, Hai Zeng, Meng Zhang, Chao Dong, Chuanyin Xi, Junfeng Wang, Jiakun Fang, Xiaotao Han, Guang-Han Cao, Liang Li, Yongkang Luo

Abstract: The unexpectedly small ordered moments of CeNiAsO, a candidate for correlated $p$-wave magnet, have posed a serious challenge to the precise determination of its magnetic structure, hindering the understanding of its fundamental properties. By leveraging the high sensitivity to local internal fields, our $^{75}$As nuclear quadrupole / magnetic resonance experiments reveal a commensurate antiferrom… ▽ More The unexpectedly small ordered moments of CeNiAsO, a candidate for correlated $p$-wave magnet, have posed a serious challenge to the precise determination of its magnetic structure, hindering the understanding of its fundamental properties. By leveraging the high sensitivity to local internal fields, our $^{75}$As nuclear quadrupole / magnetic resonance experiments reveal a commensurate antiferromagnetic order with a small out-of-plane moment $m_z\approx0.05$ $μ_{\mathrm{B}}$. This tilted magnetic configuration not only rotates the spin polarization axis away from the crystallographic $\mathbf{c}$-axis, but also enhances the non-relativistic spin splitting. We refer to this rare paradigm as a \textit{tilted $p$-wave magnet}. △ Less

Submitted 20 August, 2026; originally announced August 2026.

Comments: 7+10 pages, 4+9 figures

From the Square-Energy Conjecture to Signed Graphs: Sharp Bounds for Positive Square Energy

Authors: Fu-Tao Hu, Xiao Han

Abstract: Let $Σ=(G,σ)$ be a connected signed graph of order $n$ and size $m$, and let $s^{+}(Σ)$ and $s^{-}(Σ)$ denote the sums of the squares of its positive and negative adjacency eigenvalues, respectively. The square-energy conjecture of Elphick, Farber, Goldberg, and Wocjan states that every connected graph $G$ of order $n$ satisfies \[ \min\{s^{+}(G),s^{-}(G)\}\ge n-1. \] Liu and Ning~\cite{LiuNing2… ▽ More Let $Σ=(G,σ)$ be a connected signed graph of order $n$ and size $m$, and let $s^{+}(Σ)$ and $s^{-}(Σ)$ denote the sums of the squares of its positive and negative adjacency eigenvalues, respectively. The square-energy conjecture of Elphick, Farber, Goldberg, and Wocjan states that every connected graph $G$ of order $n$ satisfies \[ \min\{s^{+}(G),s^{-}(G)\}\ge n-1. \] Liu and Ning~\cite{LiuNing2023} published a wide-ranging paper entitled ``Unsolved Problems in spectral graph theory", and this conjectures were placed first in their list of such problems. We prove that every signature $σ$ of a connected graph $G$ satisfies the sharp bound \[ s^{+}(Σ)\le 2m-n+1. \] For the all-positive signing this gives $s^{+}(G)\le 2m-n+1$, whereas for the all-negative signing it gives $s^{-}(G)\le 2m-n+1$. Since $s^{+}(G)+s^{-}(G)=2m$, these two special cases imply the square-energy conjecture; the present theorem is stronger in scope because the same bound holds for every signing of $G$. Applying the theorem to the negation $-Σ$ also yields \[ s^{+}(Σ)\ge n-1. \] Both bounds are sharp. The proof is based on a doubly nonnegative matrix inequality. We also shorten the proof of that inequality by replacing its final case distinction with a fixed convex combination. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Comments: 14 pages

MSC Class: Primary 05C50; Secondary 05C22; 15A18; 15A42 ACM Class: G.2.2

Search for $B$ meson decays to multimuon final states

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. (1109 additional authors not shown)

Abstract: A search for decays of $B$ mesons to final states with four or six muons using $pp$ collision data recorded by the LHCb experiment corresponding to an integrated luminosity of $5.4~\text{fb}^{-1}$ is presented. The decay modes of interest are $B_{(s)}^0 \rightarrow μ^+μ^-μ^+μ^-$, $B^+ \rightarrow K^+μ^+μ^-μ^+μ^-$, $B_{(s)}^0 \rightarrow μ^+μ^-μ^+μ^-μ^+μ^-$ and… ▽ More A search for decays of $B$ mesons to final states with four or six muons using $pp$ collision data recorded by the LHCb experiment corresponding to an integrated luminosity of $5.4~\text{fb}^{-1}$ is presented. The decay modes of interest are $B_{(s)}^0 \rightarrow μ^+μ^-μ^+μ^-$, $B^+ \rightarrow K^+μ^+μ^-μ^+μ^-$, $B_{(s)}^0 \rightarrow μ^+μ^-μ^+μ^-μ^+μ^-$ and $B^+ \rightarrow K^+μ^+μ^-μ^+μ^-μ^+μ^-$, proceeding via both prompt and long-lived intermediate particles. No evidence for any of the signal modes is found, and upper limits spanning the range of $0.6\times10^{-9}$ to $5.4\times10^{-7}$ at the $95\%$ confidence level are set on their branching fractions, depending on the intermediate-particle masses and lifetimes. In addition, mass-integrated limits across the intermediate-particle lifetime ranges considered in this analysis are determined. △ Less

Submitted 21 August, 2026; v1 submitted 18 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/5232/ (LHCb public pages)

Report number: LHCb-PAPER-2026-014, CERN-EP-2026-199

Key-Frame Reasoning with SAM3: Third Place Solution for the MeViS-Text Track of the 8th LSVOS Challenge

Authors: Ce Bian, Xusheng He, Jinrong Zhang, Canyang Wu, Xianjing Han, Jianlong Wu

Abstract: This report presents a two-stage, training-free solution for the MeViS-Text track of the 8th LSVOS Challenge. The task requires a model to localize and segment the object specified by a natural-language expression throughout a video. Such expressions often depend on temporal cues, including actions, interactions, directions, and relative positions. Our first stage uses Gemini-3.1 Pro via API to de… ▽ More This report presents a two-stage, training-free solution for the MeViS-Text track of the 8th LSVOS Challenge. The task requires a model to localize and segment the object specified by a natural-language expression throughout a video. Such expressions often depend on temporal cues, including actions, interactions, directions, and relative positions. Our first stage uses Gemini-3.1 Pro via API to decompose a video-level event into instance-level targets, select a key frame for each target, and generate a discriminative description aligned with that frame. In the second stage, SAM3-agent produces a pixel-level seed mask on the selected frame, and the SAM3 video tracker propagates the mask bidirectionally through the video. Valid instances are grounded and propagated independently before their frame-wise masks are merged. All local SAM3 processing runs on a single NVIDIA GeForce RTX 4090 without task-specific training or model ensembling. Our method ranked third on the challenge test set, obtaining J&F, J, F, N-acc., T-acc., and Final scores of 0.761, 0.7367, 0.7852, 0.8333, 0.9755, and 0.856593, respectively. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement

Authors: Shenao Chen, Yidan Xu, Xiangmin Han, Rundong Xue, Duanpo Wu, Yuhan Gao, Chenggang Yan, Yue Gao

Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data. Furthermore, existing refine… ▽ More Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data. Furthermore, existing refinement strategies often rely on exhaustive, full-page reconstruction to align cross-modal information, leading to prohibitive computational redundancy and the introduction of contextual noise in long-form document processing. In this paper, we propose Hyper-M2RAG, a novel framework that redefines multimodal document retrieval through High-order Hypergraph Representation Learning. We first formalize the document structure as a Multimodal Hypergraph, utilizing hyperedges as unified semantic containers to encapsulate multi-way associations across text, images, and tables, thereby transcending point-to-point modeling. To mitigate semantic fragmentation caused by physical pagination, we introduce an Anchor-driven Incremental Refinement mechanism. Rather than performing a global sweep, our approach identifies boundary-crossing anchor nodes and reconstructs their local hyper-topology using one-hop neighborhood contexts. This targeted refinement effectively bridges cross-page knowledge gaps with minimal computational footprints. Extensive evaluations on multimodal benchmarking datasets demonstrate that Hyper-M2RAG significantly outperforms state-of-the-art methods in both retrieval precision and generation coherence. Our code is available at https://github.com/ShenAoChen2001/MMHRAG. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Comments: Accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026)

First measurements of the branching fractions of $J/ψ$ and $ψ(3686) \to Σ^{0} \barΣ^{0}η$

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, 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. (750 additional authors not shown)

Abstract: Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ and $(2712 \pm 14) \times 10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the hadronic decays $J/ψ\to Σ^{0} \barΣ^{0} η$ and $ψ(3686) \to Σ^{0} \barΣ^{0} η$ are observed for the first time. The corresponding branching fractions are measured to be… ▽ More Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ and $(2712 \pm 14) \times 10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the hadronic decays $J/ψ\to Σ^{0} \barΣ^{0} η$ and $ψ(3686) \to Σ^{0} \barΣ^{0} η$ are observed for the first time. The corresponding branching fractions are measured to be $\mathcal{B}(J/ψ\to Σ^{0} \barΣ^{0}η)= (7.5 \pm 0.3 \pm 0.8) \times 10^{-5}$ and $\mathcal{B}(ψ(3686) \to Σ^{0} \barΣ^{0}η)= (1.3\pm 0.1 \pm 0.1) \times 10^{-5}$, respectively, where the first uncertainties are statistical, and the second systematic. The ratio $\text{Q} \approx \frac{\mathcal{B}(ψ(3686) \to Σ^{0} \barΣ^{0} η)}{\mathcal{B}(J/ψ\to Σ^{0} \barΣ^{0} η)}$ is determined to be $(17.3 \pm 1.5 \pm 1.7)\%$, which is con sistent with the 12\%-rule within 3.0$σ$.~No significant intermediate states or threshold enhancements are observed in the $Σ^0$($\barΣ^{0}$)$η$ and $Σ^0$$\barΣ^{0}$ invariant mass spectra. △ Less

Submitted 17 August, 2026; originally announced August 2026.

From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents

Authors: Zhengzhao Ma, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range dependencies through which errors accumulate across an execution trajectory. As a result, they may… ▽ More Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range dependencies through which errors accumulate across an execution trajectory. As a result, they may fail to identify agent failures whose causes originate several reasoning or interaction steps before the final answer. We propose RUPA (Relational Uncertainty Propagation for Agents), a trajectory-level UQ framework for LLM agents. RUPA represents an execution history as a directed trajectory graph in which reasoning states, tool interactions, and environment feedback are nodes connected by temporal and semantic dependency edges. It then propagates uncertainty over this graph to capture how execution risk accumulates and transfers across interaction steps. The propagated signal is combined with trajectory-level behavioral features and goal-alignment information to produce a confidence estimate for the full agent trajectory. We evaluate RUPA on representative agent benchmarks, including $τ$-2, Terminal-Bench-2, and GAIA, using 6 open-source LLMs spanning multiple model families. Experimental results show that RUPA consistently outperforms existing UQ methods by providing more accurate uncertainty estimates, enabling earlier failure detection, and improving uncertainty-guided agent execution across diverse agent tasks. These results demonstrate that explicitly modeling relational dependency is crucial to reliable UQ for long-horizon LLM agents, providing a practical foundation for trustworthy agent execution. △ Less

Submitted 18 August, 2026; v1 submitted 16 August, 2026; originally announced August 2026.

From "What-If" to "What-Is": Counterfactual Thinking-Inspired Semantic Alignment for Visual Brain Decoding

Authors: Kaitao Yan, Chi Liu, Congcong Zhu, Huajie Chen, Gengshen Wu, Minghao Wang, Xiaotong Han, Tianqing Zhu

Abstract: Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to studying how visual information is represented in the brain. Recent multimodal representations and diffusion priors have improved reconstruction realism. However, visually plausible reconstructions may contain incorrect objects, attributes, or relatio… ▽ More Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to studying how visual information is represented in the brain. Recent multimodal representations and diffusion priors have improved reconstruction realism. However, visually plausible reconstructions may contain incorrect objects, attributes, or relations because a strong generative prior can complete content not sufficiently specified by the decoded representation. Conventional reconstruction metrics mainly assess the final image and may therefore obscure such semantic errors. We propose ConceptAlign, a counterfactual semantic alignment framework for visual brain decoding. ConceptAlign pools decoded visual tokens and projects them into a frozen text-embedding space, aligning the representation with the ground-truth caption while separating it from scene-preserving near-miss alternatives. Generated offline by an LLM, these alternatives modify one critical object, attribute, or relation while retaining the scene. A margin-based objective learns fine-grained semantic boundaries between the observed stimulus and plausible but incorrect interpretations without requiring LLM calls during inference. We introduce a systematic three-level semantic evaluation framework covering foundational discriminability, counterfactual description discrimination, and representational geometry. Experiments on the Natural Scenes Dataset show that ConceptAlign improves reconstruction measures, counterfactual semantic discrimination, and representational alignment over the MindEye2 backbone. Matched negative-source ablations, independent LLM and human-written alternatives, and human evaluation support the effectiveness and robustness of the supervision, with favorable patterns in fine-grained conflicts, limited-data decoding, and cross-subject structure. △ Less

Submitted 15 August, 2026; originally announced August 2026.

Comments: Under Review

Improved measurement of $C\!P$ violation in $B^{0}_{s} \!\to J/ψπ^{+}π^{-}$ decays

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, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, L. An , et al. (1116 additional authors not shown)

Abstract: The time-dependent $C\!P$ asymmetry in $B^{0}_{s} \!\to J/ψπ^{+}π^{-}$ decays is measured using proton-proton collision data, corresponding to an integrated luminosity of $6\,\text{fb}^{-1}$, collected with the LHCb detector at a centre-of-mass energy of $13\,\text{TeV}$ during $\mbox{2015--2018}$. The $C\!P$-violating phase, $φ_{s}$, the direct $C\!P$-violation parameter, $\left|λ\right|$, and th… ▽ More The time-dependent $C\!P$ asymmetry in $B^{0}_{s} \!\to J/ψπ^{+}π^{-}$ decays is measured using proton-proton collision data, corresponding to an integrated luminosity of $6\,\text{fb}^{-1}$, collected with the LHCb detector at a centre-of-mass energy of $13\,\text{TeV}$ during $\mbox{2015--2018}$. The $C\!P$-violating phase, $φ_{s}$, the direct $C\!P$-violation parameter, $\left|λ\right|$, and the decay width of the heavy mass eigenstate in the $B^{0}_{s}$ system, $Γ_{\mathrm{ H}}$, are measured respectively to be $φ_{s} = -0.077 \pm 0.034 \pm 0.007\,\text{rad}$, $\left|λ\right| = 0.993 \pm 0.026 \pm 0.007$ and $Γ_{\mathrm{ H}} = 0.610 \pm 0.002 \pm 0.004\,\text{ps}^{-1}$, where the first uncertainties are statistical and the second systematic. These results are consistent with previous measurements and the expectation based on the Standard Model. The combination with previous measurements in $B^{0}_{s} \!\to J/ψπ^{+}π^{-}$ decays using $7\,\text{TeV}$ and $8\,\text{TeV}$ proton-proton collision data yields $φ_{s} = -0.046 \pm 0.031\,\text{rad}$, $\left|λ\right| = 0.975 \pm 0.024$ and $Γ_{\mathrm{ H}} = 0.610 \pm 0.004\,\text{ps}^{-1}$, while the combination including all other LHCb measurements gives $φ_{s} = -0.041 \pm 0.017\,\text{rad}$. △ Less

Submitted 14 August, 2026; originally announced August 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/5454/ (LHCb public pages)

Report number: LHCb-PAPER-2026-017, CERN-EP-2026-181

Cleft Extensions for Hopf Algebroids without Antipodes

Authors: Xiao Han, Peter Schauenburg

Abstract: We introduce cleft extensions for Hopf algebroids. We prove the equivalence between cleft extensions, $σ$-twisted crossed products, and Hopf-Galois extensions with the normal basis property, thereby generalizing the theory of cleft extensions for Hopf algebroids developed by B{ö}hm and Brzezi{ń}ski, and fitting in with the general theory of Galois and biGalois extensions over Hopf algebroids devel… ▽ More We introduce cleft extensions for Hopf algebroids. We prove the equivalence between cleft extensions, $σ$-twisted crossed products, and Hopf-Galois extensions with the normal basis property, thereby generalizing the theory of cleft extensions for Hopf algebroids developed by B{ö}hm and Brzezi{ń}ski, and fitting in with the general theory of Galois and biGalois extensions over Hopf algebroids developed by the authors. We investigate the Ehresmann Hopf algebroid associated with a cleft extension and show that it is isomorphic to a generalized version of the Connes-Moscovici Hopf algebroid. A special case of the Connes-Moscovici Hopf algebroid, namely the case where the coinvariants of the cleft extension coincide with the base of the Hopf algebroid, is a Drinfeld twist of a Hopf algebroid by a two-cocycle, generalizing work of B{ö}hm, Han and Majid. △ Less

Submitted 14 August, 2026; originally announced August 2026.

Jais 2: A Family of Arabic-Centric Open Large Language Models

Authors: Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, Mostafa Awad, Abdelrahman Sadallah, Gurpreet Gosal, Gokulakrishnan Ramakrishnan, Sarath Chandran, Biswajit Mishra, Rituraj Joshi, Ahmed Frikha, Etienne Goffinet, Abhishek Maiti, Ali El Filali, Sarah AlBarri, Samujjwal Ghosh, Rahul Pal, Parvez Mullah, Awantika Shukla, Sajid siddiki, Samta Kamboj, Onkar Pandit, Sunil Kumar Sahu, AbdelRahman Elbadawy, Amr Mohamed , et al. (35 additional authors not shown)

Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest open Arabic-centric LLM trained from scratch at 70B parameters, and a competiti… ▽ More Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest open Arabic-centric LLM trained from scratch at 70B parameters, and a competitive 8B-parameter variant among the evaluated open models. A custom Arabic-centric vocabulary enables efficient training and inference. In addition, an optimized architecture and training recipe yield highly compute-efficient training. With a substantially smaller token budget than comparable models, Jais 2 achieves strong Arabic performance on the benchmarks considered in this report and competitive English results. The models obtain leading results among the evaluated open models on OALL2 and AraGen. They also perform strongly on several culturally grounded Arabic benchmarks, including poetry, religion, cuisine, and dream interpretation, as well as in general tasks such as translation and summarization. We release the models in HuggingFace under a commercially permissive license. Jais 2 70B is also released as a chat app on the Web, iOS, and Android; it runs on Cerebras hardware, delivering up to 2,000 tokens per second, and enabling high-throughput Arabic-centric chat serving in our deployment setting. By uniting scale, linguistic diversity, cultural fidelity, openness, and speed, Jais 2 provides an open-weight foundation intended to support further research and development in Arabic-centric LLMs. △ Less

Submitted 7 July, 2026; originally announced August 2026.

High-precision measurement of the space-like $η^\prime$ transition form factor

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, M. S. Anderson, 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 , et al. (758 additional authors not shown)

Abstract: Using a data sample corresponding to an integrated luminosity of $20.3\ \text{fb}^{-1}$, collected with the BESIII detector at a center-of-mass energy of $3.773\ \text{GeV}$ at the BEPCII collider, we report a precision measurement of the product $Q^2|F(Q^2)|$, where $F(Q^2)$ is the single-virtual space-like transition form factor of the $η'$ meson and $Q^2$ is the squared momentum transfer of the… ▽ More Using a data sample corresponding to an integrated luminosity of $20.3\ \text{fb}^{-1}$, collected with the BESIII detector at a center-of-mass energy of $3.773\ \text{GeV}$ at the BEPCII collider, we report a precision measurement of the product $Q^2|F(Q^2)|$, where $F(Q^2)$ is the single-virtual space-like transition form factor of the $η'$ meson and $Q^2$ is the squared momentum transfer of the tagged virtual photon. The transition form factor is extracted from the differential Born cross section of the two-photon fusion processes $e^+e^- \to e^+e^-γγ^* \to e^+e^-η^\prime$ using a single-tag technique, where only one scattered lepton is detected. The measurement covers $Q^2 \in [0.1, 6.0]$ GeV$^2$, achieving unprecedented precision, better than $3.0\%$ for $Q^2 < 1.5$ GeV$^2$, and providing the first direct determination at $Q^2 < 0.3$ GeV$^2$. △ Less

Submitted 12 August, 2026; originally announced August 2026.

VOS-Agent: The 1st Place Solution for the 8th LSVOS Challenge (MOSEv2 Track)

Authors: Canyang Wu, Jinrong Zhang, Xusheng He, Ce Bian, Xianjing Han, Jianlong Wu

Abstract: Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask propagation, a uniform inference path remains unreliable for tiny targets with insufficient visual evidence and semantic-dominated targets whose identities depend on explicit attributes. To this end, we present VOS-Agent, a colla… ▽ More Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask propagation, a uniform inference path remains unreliable for tiny targets with insufficient visual evidence and semantic-dominated targets whose identities depend on explicit attributes. To this end, we present VOS-Agent, a collaborative framework that retains SAM3 as the shared dense segmentation module and conditionally activates specialized agents according to target characteristics. A Target Perception and Routing Agent assigns each sequence to a regular, tiny, or semantic-dominated route. Tiny targets are supported by a Visual Tracking Agent through confidence-aware box prompts, while semantic-dominated targets are handled by an MLLM-based Semantic Agent through description-guided localization and candidate verification. On the MOSEv2 test set, VOS-Agent achieves 69.82% on the official $\mathcal{J}\&\dot{\mathcal{F}}$ metric and ranks first in the MOSEv2 Track of the 8th LSVOS Challenge at ECCV 2026. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Comments: 1st Place Solution for the 8th LSVOS MOSEv2 Challenge (ECCV 2026 Workshop)

Observation of several sources of $C\!P$ violation in $B^+ \!\to K^+ π^+ π^-$ decays

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: An amplitude analysis of $B^+ \!\to K^+ π^+ π^-$ decays is presented in which six $C\!P$-violating phenomena are judged to be of significance for the first time. This analysis is based on $pp$ collision data recorded with the LHCb detector in 2011-2012, corresponding to an integrated luminosity of $3\,\text{fb}^{-1}$. Quasi-two-body $C\!P$ violation in $B^+ \!\to ρ(770)^0 K^+$ decays is discovered… ▽ More An amplitude analysis of $B^+ \!\to K^+ π^+ π^-$ decays is presented in which six $C\!P$-violating phenomena are judged to be of significance for the first time. This analysis is based on $pp$ collision data recorded with the LHCb detector in 2011-2012, corresponding to an integrated luminosity of $3\,\text{fb}^{-1}$. Quasi-two-body $C\!P$ violation in $B^+ \!\to ρ(770)^0 K^+$ decays is discovered, while $C\!P$ violation at amplitude level is established in $B^+ \!\to f_2(1270) K^+$ decays. First evidence for $C\!P$ violation is reported in both the fully elastic S-wave $ππ$-$ππ$ rescattering region and also for any decay involving a spin-3 resonance. Additionally, significant $C\!P$-violation effects are identified in the interference between different $ππ$ partial waves, with observation in S-P wave interference and evidence in S-D wave interference, both of which must be driven by long-distance interactions. △ Less

Submitted 14 August, 2026; v1 submitted 12 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/5752/ (LHCb public pages)

Report number: LHCb-PAPER-2025-069, CERN-EP-2026-218

Resolution of outstanding puzzles in $B^+ \!\to K^+ π^+ π^-$ decays

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: An amplitude analysis of $B^+ \!\to K^+ π^+ π^-$ decays is presented, based on $pp$ collision data recorded with the LHCb detector in 2011--2012, corresponding to an integrated luminosity of $3\,\text{fb}^{-1}$. Previous studies of the $B \!\to K ππ$ sector have left key unresolved questions concerning the model of the S-wave contributions. A pivotal finding is that relaxing unitarity-based assump… ▽ More An amplitude analysis of $B^+ \!\to K^+ π^+ π^-$ decays is presented, based on $pp$ collision data recorded with the LHCb detector in 2011--2012, corresponding to an integrated luminosity of $3\,\text{fb}^{-1}$. Previous studies of the $B \!\to K ππ$ sector have left key unresolved questions concerning the model of the S-wave contributions. A pivotal finding is that relaxing unitarity-based assumptions about the relation between the $K^*_0(1430)^0$ resonance and the slowly varying scalar part in $K^+π^-$ leads to considerably better agreement between the model and data. The $B^+ \!\to K^*_0(1430)^0 π^+$ branching fraction now challenges the experimental consensus that $B \!\to K^*_0(1430) π$ decays dominate the $B \!\to K ππ$ phase space, aligning with the predictions of QCD factorisation rather than perturbative QCD, thus reversing the agreement found in previous measurements. With this increased flexibility, it also becomes possible to model the scalar $π^+ π^-$ amplitude using established states, eliminating the need for the ad-hoc ``$f_X(1300)$'' component included in previous analyses of the $B \!\to Kππ$ sector. These advances facilitate the discovery of ten intermediate decays. △ Less

Submitted 14 August, 2026; v1 submitted 12 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/5754/ (LHCb public pages)

Report number: LHCb-PAPER-2025-068, CERN-EP-2026-217

Amplitude analysis of $B^+ \!\to K^+ π^+ π^-$ decays

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: The branching fractions and quasi-two-body $C\!P$-violating asymmetries of intermediate states obtained through an amplitude analysis of the charmless three-body decay $B^+ \!\to K^+ π^+ π^-$ are reported. The analysis is based on $pp$ collision data at centre-of-mass energies $\sqrt{s}=7$ and $8\,\text{TeV}$ recorded with the LHCb detector, corresponding to an integrated luminosity of… ▽ More The branching fractions and quasi-two-body $C\!P$-violating asymmetries of intermediate states obtained through an amplitude analysis of the charmless three-body decay $B^+ \!\to K^+ π^+ π^-$ are reported. The analysis is based on $pp$ collision data at centre-of-mass energies $\sqrt{s}=7$ and $8\,\text{TeV}$ recorded with the LHCb detector, corresponding to an integrated luminosity of $3\,\text{fb}^{-1}$. The most challenging aspect of the amplitude modelling lies in the description of the dominant $K^+ π^-$ and $π^+ π^-$ S-wave contributions. This is achieved by three complementary approaches based on a physically motivated analytic model built on the isobar approximation, the K-matrix formalism, and a quasi-model-independent procedure in which overlapping crossing partial waves are simultaneously studied. In addition, alternative sets of results are presented, considering the $π^+ π^-$ final state to manifest either through direct $ω(782)$ decays or $ρ(770)^0\textrm{-}ω(782)$ mixing. The most precise measurements of branching fractions and $C\!P$ asymmetries are obtained for the vast majority of intermediate states, establishing firmer reference points against which to cleanly probe model-independent physics beyond the Standard Model. The results from all three approaches agree and provide new insight into strong dynamics and the origin of $C\!P$-violation effects in $B^+ \!\to K^+ π^+ π^-$ decays. △ Less

Submitted 14 August, 2026; v1 submitted 12 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/1622/ (LHCb public pages)

Report number: LHCb-PAPER-2025-067, CERN-EP-2026-216

VICBench: A Multi-Language Benchmark for Code Vulnerability Detection

Authors: Jin Lu, Xuening Han, Yang Zhong, Lin Tan, Kevin Luo, Andrew Gacek, Neha Rungta

Abstract: Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases. VICs are essential for determining the full range of vulnerable software versions. Existing vulnerability datasets suffer from limited programming language coverage, restricted patch complexity, and narrow projec… ▽ More Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases. VICs are essential for determining the full range of vulnerable software versions. Existing vulnerability datasets suffer from limited programming language coverage, restricted patch complexity, and narrow project scope. Through our dual annotation by human experts and an agentic workflow, we create a benchmark - VICBench - of 100 verified VICs for 100 CVEs across 88 projects in Python, Java, and C++, covering 48 CWE types. VICBench features complex real-world vulnerability fixes averaging 38.6 lines and corresponding VICs of 252.5 lines - significantly larger than prior work. Our evaluation shows that state-of-the-art algorithms V-SZZ and LLM4SZZ achieve only 33.3%-40.1% F1, confirming that using existing approaches still entails significant manual effort. VICBench enables robust evaluation of vulnerability detection approaches. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Model-independent measurement of the transversity amplitudes of the $B^0\to K^{*0}μ^+μ^-$ 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, S. Akar, K. Akiba, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, Z. Amos , et al. (1157 additional authors not shown)

Abstract: An analysis of the decay amplitudes of $B^0 \to K^{*0}(\to K^+π^-)μ^+μ^-$ is presented, using proton-proton collision data recorded by the LHCb experiment at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 8.4 fb$^{-1}$. The amplitudes are constructed from Legendre polynomials in the $μ^+μ^-$ invariant mass squared region $1.1<q^2<8.0$ GeV$^2/c^4$. $C\!P$-… ▽ More An analysis of the decay amplitudes of $B^0 \to K^{*0}(\to K^+π^-)μ^+μ^-$ is presented, using proton-proton collision data recorded by the LHCb experiment at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 8.4 fb$^{-1}$. The amplitudes are constructed from Legendre polynomials in the $μ^+μ^-$ invariant mass squared region $1.1<q^2<8.0$ GeV$^2/c^4$. $C\!P$-averaged observables are obtained from the amplitudes. Some of these observables present deviations with respect to the Standard Model, which can be interpreted as shifts in the effective Wilson coefficients. This model-independent approach enables tests of theoretical predictions that can help disentangle hadronic effects from potential contributions from physics beyond the Standard Model. This allows flexibility in the choice of $q^2$ binning for global analyses. Depending on the binning scheme, the deviation of the Wilson coefficient $C_9$ from its Standard Model expectation varies from $4.3σ$ to $4.8σ$. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/3413/ (LHCb public pages)

Report number: LHCb-PAPER-2026-023, CERN-EP-2026-226

RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning

Authors: Yibo Shen, Xudong Han, Xiaowei Zhu, Gen Li, Zhenxuan Pan

Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottleneck to the other. In MoE RL, rollout-time routing replay exposes every sample's se… ▽ More Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottleneck to the other. In MoE RL, rollout-time routing replay exposes every sample's sequence length and layer-wise expert demand before its training step. We present RoutePack, a hierarchical planner that coordinates state-consistent, layer-wise expert rerouting with joint attention- and expert-aware data packing over an optimizer-step window. RoutePack first places experts independently at each MoE layer using aggregate routing demand. It then packs samples into the smallest certified, or best-known feasible, number of token-capped execution rows and optimizes their DP layout with a projected EDP-shard-aware objective. The objective combines a window-normalized linear-quadratic attention proxy with per-layer physical EP-rank peaks and minimizes the accumulated cost of the slowest EDP shard. Parallel population annealing searches fixed-row feasible layouts while preserving sample coverage, capacity, nonempty cells, equal microbatch counts, and communicator topology. State-consistent materialization preserves logical top-k routing and existing MoE kernels without microbatch-level expert replication. Across Ling-3.0-Tiny and Ling-3.0-Flash, expert rerouting improves mean trainer-measured token throughput by 3.80% and 10.50%, while routing-aware packing adds another 4.86% and 3.98%, respectively. Overall, RoutePack improves throughput by 8.85% and 14.89% over the baseline. △ Less

Submitted 12 August, 2026; originally announced August 2026.

GeoBridge: Decoupled Semantic Conditioning for Generative Image Geolocalization

Authors: Zhiyang Dou, Xumeng Han, Fengde Peng, Zipeng Wang, Moxuan Zhao, Zhipei Huang, Zhenjun Han

Abstract: Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoning isdecoded into coordinates, however, has lagged behind. Predicting a place name for a geocoding API is discrete and lossy: it ignores image evidence and collapses multi-granular semantics into a coarse lookup. We argue that the bottleneck has sh… ▽ More Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoning isdecoded into coordinates, however, has lagged behind. Predicting a place name for a geocoding API is discrete and lossy: it ignores image evidence and collapses multi-granular semantics into a coarse lookup. We argue that the bottleneck has shifted from what a model reasons to how that reasoning is represented for a continuous, geometry-aware decoder. We present GeoBridge, a role-decoupled conditioning mechanism that connects a frozen semantic MLLM to a frozen Riemannian flow-matching head that generates coordinates on the sphere. The central obstacle is arole conflict: supervising the condition with discrete semantic labels biases its representation toward class-discriminative geometry, at odds with the smooth manifold the generative head requires. GeoBridge keeps the semantic supervision decoupled from the condition interface: a separate projection forms the continuous condition the frozen head expects, injecting geographic priors without disturbing the spherical decoder. On IM2GPS3K, GeoBridge reaches 38.67/52.89/70.37 at the 25/200/750 km thresholds, improving over a place-name-to-API pipeline and reasoning-augmented direct prediction at these precision-relevant scales. GeoBridge is a decode-side algorithmic contribution, orthogonal and complementary to chain-of-thought reasoning. Code will be made publicly available. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Authors: Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao

Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions. In these settings, models often rely on implicit inference without sufficient visual evidence, leading to a disconnect between perception and reasoning. Meanwhile, existing outcome-oriented benchmar… ▽ More While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions. In these settings, models often rely on implicit inference without sufficient visual evidence, leading to a disconnect between perception and reasoning. Meanwhile, existing outcome-oriented benchmarks evaluate only final predictions and fail to diagnose failures in the underlying reasoning process. To address this gap, the authors propose AD2-Bench, which introduces a Hierarchical Visual Diagnosis framework that decomposes reasoning into a structured Chain of Evidence (CoE). This fine-grained diagnosis reveals that robust multimodal reasoning fundamentally depends on accurate evidence acquisition. Building on this perspective, the authors formulate reasoning from a probabilistic viewpoint and identify two primary causes of reasoning failure: Spatial Ambiguity, where models fail to distinguish target objects from background clutter, resulting in localization errors; and Semantic Uncertainty, where degraded visual features lead to incorrect semantic interpretation, resulting in understanding errors. To overcome these evidence deficiencies, they further propose Evidence-grounded Visual Reasoning (EGVOR), which replaces implicit reasoning with the explicit generation of Evidence Atoms - structured spatial-semantic triplets that enforce tight alignment between localization and semantic understanding. The model is trained through a hierarchical curriculum that progresses from reflective supervision construction to reinforcement learning, where reducing reasoning variance is explicitly rewarded. Extensive experiments demonstrate that EGVOR substantially improves reasoning stability under adverse conditions, providing a more robust framework for trustworthy multimodal cognition. △ Less

Submitted 11 August, 2026; originally announced August 2026.

Comments: Accepted by IJCV

Study of muon-tagged $D_{s1}(2460)^+$ and $D_{s1}(2536)^+$ decays to the $D_s^{+}π^+π^-$ final state

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, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, L. An , et al. (1120 additional authors not shown)

Abstract: Decays of the pseudovector $D_{s1}(2460)^+$ and $D_{s1}(2536)^+$ mesons to the three-body $D_{s}^+π^+π^-$ final state are studied. The data sample is based on decays of beauty hadrons into $D_{s1}^+$ states accompanied by a muon from the $b$-hadron decay chain collected by the LHCb detector during 2016--2018, corresponding to an integrated luminosity of 5.4 fb${}^{-1}$. The \mbox{… ▽ More Decays of the pseudovector $D_{s1}(2460)^+$ and $D_{s1}(2536)^+$ mesons to the three-body $D_{s}^+π^+π^-$ final state are studied. The data sample is based on decays of beauty hadrons into $D_{s1}^+$ states accompanied by a muon from the $b$-hadron decay chain collected by the LHCb detector during 2016--2018, corresponding to an integrated luminosity of 5.4 fb${}^{-1}$. The \mbox{$D_{s1}(2536)^+\to D_s^+π^+π^-$} branching fraction is measured for the first time, with the $D_{s1}(2536)^+\to D^+K^+π^-$ decay used as a reference. A simultaneous amplitude analysis of the $D_{s1}(2460)^+$ and $D_{s1}(2536)^+\to D_s^+π^+π^-$ decays is performed. The Dalitz-plot distributions of the two decays are found to be significantly different, suggesting differences in the internal structure of the two states, with evidence of exotic contributions to the $D_{s}^+π^{\pm}$ channel with the pole below the $DK$ threshold. Measurements of the masses of the $D_{s1}(2460)^+$ and $D_{s1}(2536)^+$ states are performed, and an upper limit on the $D_{s1}(2460)^+$ width is set. △ Less

Submitted 19 August, 2026; v1 submitted 11 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/3761 (LHCb public pages); The only change in this revision is added authorship XML file for INSPIRE

Report number: LHCb-PAPER-2026-019, CERN-EP-2026-208

DualSpectralCF: Training-Free Sign-Aware Spectral Collaborative Filtering

Authors: Guanqun Yang, Tong Qi, Xiaoxue Han

Abstract: Real-world recommendation platforms routinely collect explicit negative feedback such as 1-star reviews, hate-button clicks, distrust between users, and very-low watch-ratio videos. Learned sign-aware recommenders exploit this signal for clear accuracy gains, but only at the cost of gradient-based training. In parallel, a line of training-free spectral collaborative filtering methods matches or be… ▽ More Real-world recommendation platforms routinely collect explicit negative feedback such as 1-star reviews, hate-button clicks, distrust between users, and very-low watch-ratio videos. Learned sign-aware recommenders exploit this signal for clear accuracy gains, but only at the cost of gradient-based training. In parallel, a line of training-free spectral collaborative filtering methods matches or beats learned graph recommenders at a fraction of the cost, yet operates on positive interactions alone. We bridge these two lines with DualSpectralCF, a training-free framework of two components that attach to any spectral backbone of the form $\hat{\mathbf{r}}_u = F(\mathbf{M}) \mathbf{r}_u$: a signed input signal $\mathbf{r}_u^{\pm}$ that encodes the user's explicit dislikes, and a signed item-item operator $\mathbf{M}^{\pm}$ that blends like-together and dislike-together similarity. The framework is backbone-agnostic and adds just two scalar hyperparameters. We instantiate DualSpectralCF on ChebyCF, GF-CF, and Turbo-CF, and evaluate on five sign-aware benchmarks: every instance matches or beats its unsigned backbone on all 5 datasets, with Recall@20 lifts up to +32.6% with backbone-specific $(γ, κ)$ tuning and +1.9% to +16.0% for DualSpectralCF-Cheby at the fixed default $(γ= -0.5, κ= 0.1)$, and the family runs 7.7 to 155.3$\times$ faster than SIGformer while reaching 70.7% to 90.7% of its accuracy. Sign-awareness helps most for cold-start users, with up to +29.2% Recall@20 on Epinions users with 1 to 5 training items. △ Less

Submitted 10 August, 2026; originally announced August 2026.

Comments: Accepted at CIKM 2026. Code: https://github.com/guanqun-yang/DualSpectralCF

RFCheck: Synthetic RF Sensing Data Can Fail Measurement Consistency

Authors: Di Zhang, Yuanhao Cui, Tony Xiao Han, Xiaojun Jing

Abstract: Synthetic radio-frequency (RF) sensing data are widely used to augment wireless sensing tasks, yet their measurement consistency with real data is rarely evaluated under matched acquisition conditions. This paper identifies a measurement-consistency failure mode: synthetic samples may pass task-facing checks while deviating from the measurement behavior of real samples collected and processed by t… ▽ More Synthetic radio-frequency (RF) sensing data are widely used to augment wireless sensing tasks, yet their measurement consistency with real data is rarely evaluated under matched acquisition conditions. This paper identifies a measurement-consistency failure mode: synthetic samples may pass task-facing checks while deviating from the measurement behavior of real samples collected and processed by the same sensing pipeline, potentially introducing synthetic shortcuts and biasing downstream model selection. We propose RFCheck, a calibrated measurement audit that uses held-out real data from the same acquisition and preprocessing pipeline as the reference. RFCheck calibrates representation-specific tests on real samples and flags synthetic samples whose responses exceed the calibrated real-data range. We use the audit for candidate screening and residual repair. We validate RFCheck primarily on Wi-Fi channel state information (CSI), where the audit examines delay-domain and local frequency-domain structures. Experiments show that aggregate statistics and label-based screening can miss measurement failures detected by RFCheck. Under the same label acceptance rule, low-risk and high-risk synthetic candidates exhibit different downstream behavior. A repair reference reduces the flagged ratio to 10.83% while preserving mean task performance. In a held-out proposal study, correction followed by calibrated selection produces a class-balanced set with no flagged samples under a fixed training budget. We further apply the same calibration principle to frequency-modulated continuous-wave (FMCW) millimeter-wave radar gesture sensing. The results show that synthetic RF sensing data can violate measurement consistency even when conventional task checks are satisfied, motivating measurement-aware diagnosis and mitigation before augmentation. △ Less

Submitted 10 August, 2026; originally announced August 2026.

A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

Authors: Xin Zhou, Chun Yong Chong, Kisub Kim, Yun Peng, Rui Shu, Zihan Wu, Xu Han, Guowen Yuan, Zeyang Zhuang, Jounghoon Kim, Jeongjin Ju, Seongmin Ju, Taein Yoon, David Lo

Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level benchmarks typically evaluate only whether the final patch passes tests. Satisfying a user request requires a long chain of interdependent reasoning and decisions: an agent must recover explicit and implicit requirement… ▽ More Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level benchmarks typically evaluate only whether the final patch passes tests. Satisfying a user request requires a long chain of interdependent reasoning and decisions: an agent must recover explicit and implicit requirements, formulate a repository-grounded implementation plan, and translate it into correct code. A pass/fail outcome cannot characterize how an unsuccessful trajectory diverges from the requirements and implementation process needed for a correct patch. To address this gap, we introduce SWE-RPG, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for (1) Requirement Clarification and (2) Implementation Planning. These intermediate GTs support retrospective, GT-aligned diagnosis of complete coding-agent trajectories across clarification, planning, code generation, and artifact submission. SWE-RPG comprises 163 tasks from 31 Python and Java repositories, including 113 bug fixes and 50 feature additions. We evaluate 3 coding agents, including Claude Code, Codex, and OpenCode, with 6 large language model backends, including Claude-Sonnet-5 and GPT-5.6-Terra. Results show that the evaluated popular coding agents still struggle to implement user requests in existing repositories, achieving an average resolved rate of only 31.5% on SWE-RPG. Intermediate-GT diagnosis further identifies implicit requirement recovery as the main bottleneck, accounting for 24.5%--46.0% of agent runs. This result suggests implicit-requirement recovery as a key candidate direction for improving coding agents. The benchmark data and evaluation code are available at https://github.com/Xin-Zhou-smu/SWE-RPG-Bench. △ Less

Submitted 9 August, 2026; originally announced August 2026.

Comments: 9 pages

SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs

Authors: Debopriyo Banerjee, Kapil Rajesh Kavitha, Angana Borah, Xudong Han, Yuxia Wang, Parameswari Krishnamurthy, Utkarsh Agarwal, Atharva Kulkarni, Swaran Lata, Ayush Munot, Dhruv Sahnan, Aaryamonvikram Singh, Preslav Nakov, Monojit Choudhury

Abstract: Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts, often overlooking the linguistic diversity and culturally grounded safety risks present in other languages. To address this gap, we introduce SurakshaEval, a novel safety benchmark composed of human-written prompts spanning real-world scenarios, explicitly designed for ten majo… ▽ More Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts, often overlooking the linguistic diversity and culturally grounded safety risks present in other languages. To address this gap, we introduce SurakshaEval, a novel safety benchmark composed of human-written prompts spanning real-world scenarios, explicitly designed for ten major Indian languages - Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, and Telugu, along with English. SurakshaEval includes both generic prompts common across India and region- and language-specific prompts that capture localized sociocultural sensitivities. We benchmark a broad range of state-of-the-art LLMs on SurakshaEval, establish baseline safety performance, and identify recurring failure modes, including over-refusal, missed detection of implicit bias, and insufficient contextual awareness in regionally sensitive settings. Our results show that even strong multilingual LLMs struggle to reliably meet nuanced safety requirements when operating in Indic languages, particularly in native scripts. These findings highlight the urgent need for safety evaluation frameworks that incorporate region-specific data and structured assessment protocols, enabling the development and deployment of AI systems that operate securely, ethically, and in alignment with diverse societal values. Our code and data are available at https://github.com/debobanerjee/SurakshaEval. Warning: This paper contains text that may be offensive or unsafe. △ Less

Submitted 7 August, 2026; originally announced August 2026.

StateFlow: Sequence Pipeline Parallelism for Long-Context Modeling with Linear Recurrence

Authors: Wenxuan Zhao, Yingfa Chen, Xu Han, Wenjing Han, Tianbo Huang, Zhiyu Li, Ao Sun, Jingheng Xu, Lin Gan, Guangwen Yang

Abstract: Long-context training is increasingly important for large language models, and linear attention and state space models have become popular for improving long-context efficiency. However, efficiently parallelizing long-sequence training for recurrent and hybrid models remains challenging. We present StateFlow, a sequence pipeline parallelism system for models with linear recurrence. StateFlow par… ▽ More Long-context training is increasingly important for large language models, and linear attention and state space models have become popular for improving long-context efficiency. However, efficiently parallelizing long-sequence training for recurrent and hybrid models remains challenging. We present StateFlow, a sequence pipeline parallelism system for models with linear recurrence. StateFlow partitions each sequence into chunks and schedules their execution while propagating boundary states and gradients across chunks, thereby reducing activation lifetimes and improving training throughput. StateFlow further uses profile-guided nonuniform chunking to balance recurrence and softmax attention computation in hybrid models, and overlaps state transitions that expose limited parallelism with surrounding computation. Applying StateFlow to models with up to 32B parameters and 256K context length, we achieve up to \(2.22\times\) throughput improvements and \(2.45\times\) memory reduction compared to conventional pipeline parallelism, enabling otherwise infeasible configurations. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Search for the charged lepton flavour violating decay $η'\to eμ$

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, M. S. Anderson, 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 , et al. (744 additional authors not shown)

Abstract: Based on $(8998\pm40)\times10^6$ $J/ψ$ events collected in $e^+e^-$ collisions at $\sqrt{s} = 3.097$ GeV with the BESIII detector, we present a search for the charged lepton flavour violating decay $η'\to eμ$ with $J/ψ\toγη'$. No significant signal is observed, and an upper limit on its decay branching fraction is set to be $6.3\times10^{-7}$ at the 90% confidence level, improving the previous bes… ▽ More Based on $(8998\pm40)\times10^6$ $J/ψ$ events collected in $e^+e^-$ collisions at $\sqrt{s} = 3.097$ GeV with the BESIII detector, we present a search for the charged lepton flavour violating decay $η'\to eμ$ with $J/ψ\toγη'$. No significant signal is observed, and an upper limit on its decay branching fraction is set to be $6.3\times10^{-7}$ at the 90% confidence level, improving the previous best result by nearly three orders of magnitude. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 20 pages, 5 figures, 3 tables

ePIC Early Science Report

Authors: D. Abbott, N. Abdelrahman, S. Abhijit, I. Abualrob, R. B. Achari, J. Adam, L. Adamczyk, K. Adkins, A. Affolder, K. Agarwal, J. Agarwala, N. Agrawal, C. A. Aidala, W. Akers, A. Al-bataineh, S. N. Alam, M. Alekseev, P. R. Altieri, J. -S. Alvarado Gallenao, S. B. L. Amar, R. Ammendola, I. Amos Cali, G. An, D. Anderson, E. Anderssen , et al. (774 additional authors not shown)

Abstract: This Early Science Report from the ePIC Collaboration outlines the compelling physics program achievable during the first years of operation of the Electron-Ion Collider (EIC), prior to the establishment of the full design luminosity and energy range. The analyses are based on realistic early-running beam configurations and detailed Geant4 ePIC detector simulations, hit digitization and data recon… ▽ More This Early Science Report from the ePIC Collaboration outlines the compelling physics program achievable during the first years of operation of the Electron-Ion Collider (EIC), prior to the establishment of the full design luminosity and energy range. The analyses are based on realistic early-running beam configurations and detailed Geant4 ePIC detector simulations, hit digitization and data reconstruction. The projected studies from the physics working groups of ePIC span inclusive, semi-inclusive, exclusive, diffractive and tagging, as well as jet and heavy flavor measurements in both electron-proton and electron-ion collisions. Even before the collider reaches its full design performance, these measurements will constrain parton distribution functions in nucleons and nuclei, access transverse-momentum-dependent and spin-dependent observables, probe gluon dynamics in nuclei, and initiate a program of imaging of quarks and gluons. Each measurement is directly connected to the core science pillars of the EIC, identified in the 2018 report by the National Academy of Sciences: understanding the origin of the nucleon mass, unraveling the spin structure of the nucleon, and exploring the emergent properties of dense gluonic matter. The results presented here provide examples that demonstrate that the early years of EIC running with ePIC will deliver novel world-leading insights into Quantum Chromodynamics. In addition, the early science program will establish measurement and analysis methodologies that will pave the way to the subsequent full EIC physics program. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Report number: epic-AN-AC-2026-004

Towards Trustworthy Hypergraph Neural Networks under Label Noise

Authors: Mengyao Zhou, Zhiheng Zhou, Xiao Han, Guiying Yan

Abstract: Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise (GLN), noisy-label learning on hypergraphs remains underexplored. In this paper, w… ▽ More Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise (GLN), noisy-label learning on hypergraphs remains underexplored. In this paper, we present a systematic study of hypergraph node classification under label noise. First, we adapt representative LLN and GLN methods to hypergraphs and evaluate them under a unified benchmark, revealing the limitations of existing robust learning strategies for hypergraphs. Building on this, we propose a new hypergraph robust framework, HyperTrust, which first estimates hyperedge trustworthiness through a pretraining-based, entropy-aware strategy, and then incorporates the HyperedgeBoost module to enhance reliable supervision by connecting unlabeled nodes to trustworthy hyperedges, as well as the HyperedgePrune module to suppress noisy propagation by removing untrustworthy node-hyperedge incidences. Finally, two modules work collaboratively to adjust the hypergraph structure and generate final predictions. Extensive experiments and theoretical analysis demonstrate the effectiveness and robustness of HyperTrust on multiple hypergraph datasets under various noisy settings. Our work provides a unified benchmark and an effective solution for hypergraph learning with label noise and lays a foundation for future research in this direction. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 20 pages, 7 figures

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Authors: Xiaomin Li, Yuexing Hao, Jianheng Hou, Jintao Huang, Qianfeng Wen, Shirley Huang, Yifan Liu, Xiaoyi Liu, Yilan Fan, Yijun Wang, Koutian Wu, Ruoqi Gao, Muhammad Ahmed Mohsin, Jing Tang, Brihi Joshi, Heming Liu, Zheyuan Deng, Zonglin Di, Sankalp Jajee, Jiuyao Lu, Zhiwei Zhang, Saksham Kapoor, Ishan Gupta, Yunhan Zhao, Chanwoo Park , et al. (68 additional authors not shown)

Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First,… ▽ More Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions. Records are either sampled from a dependency graph that preserves correlated attributes or derived from human-authored profiles. We release a quality-filtered coreset of approximately 1 million personas, comprising 599,847 human-grounded and 400,000 synthetic records. Second, the MatrAIx Playground provides four environments in which diverse users evaluate and interact with digital products: Survey, AI Chatbot, Web, and App. Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare. We conducted 18,189 evaluation trials across eight representative tasks. Persona agents were powered by three LLMs: Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The resulting feedback captures how decisions and preferences vary across persona backgrounds, including hesitation after a price increase, willingness to continue after an AI assistant fails, and latency tolerance. We conducted two main validation studies: First, a 400-trial controlled study evaluated persona adherence across ten behavioral attributes and all four environments. The declared behavior was expressed or correctly suppressed in 366 trials (91.5%). Second, human and LLM judges evaluated the extraction quality of human-grounded personas. Overall, MatrAIx provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: Project website: https://matraix.ai

JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion

Authors: Yicheng Xiao, Wenxun Dai, Xinran Qin, Lin Song, Maoquan Zhang, Hang Xu, Yukang Chen, Yitong Li, Guohui Zhang, Yuan Zhang, Xuying Zhang, Tommy Zhang, Jianlong Yuan, Peihao Li, Shuai Lu, Siming Fu, Chuyang Zhao, Xin Han, Jie Huang, Wenbo Li, Guoqing Ma, Wei Huang, Xiaojuan Qi, Haoyang Huang, Nan Duan

Abstract: Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present JoyAI-Video-Edit, a 16B-parameter autoregressive diffusion framework for real-time, open-ended video editing without access to future frames or a predefined video duration. Our method combines chunk-wise autoregressive a… ▽ More Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present JoyAI-Video-Edit, a 16B-parameter autoregressive diffusion framework for real-time, open-ended video editing without access to future frames or a predefined video duration. Our method combines chunk-wise autoregressive adaptation, Source-Anchored Distribution Matching Distillation (SA-DMD), and Long-Horizon Autoregressive Distillation to reduce train--inference mismatch, preserve source fidelity during two-step generation, and mitigate accumulated temporal drift. Extensive automatic and human evaluations show that JoyAI-Video-Edit substantially outperforms existing streaming editors and remains competitive with strong offline systems on both short and long videos. The complete system achieves end-to-end 720p video editing at approximately 30 FPS on a single Nvidia B200 GPU. Code is available at https://github.com/jd-opensource/JoyAI-Video-Edit. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: Code: https://github.com/jd-opensource/JoyAI-Video-Edit

New measurements of $B^+_c$ decays into single charm final states

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, F. Alessio, Z. Aliouche, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An , et al. (1126 additional authors not shown)

Abstract: Using proton-proton collision data corresponding to an integrated luminosity of $9 \,\textrm{fb}^{-1}$ collected by the LHCb experiment, searches are performed for $B^+_c$ mesons decaying to a charm and a charmless meson pair. Five products of branching fraction, ${\cal B}(B^+_c\!\to DX)$, and fragmentation ratio $f_c\big/f_u$ are reported, \begin{align*} R_{D^+ K^{*0}} &= ( 1.42 \pm 0.23 \pm 0.07… ▽ More Using proton-proton collision data corresponding to an integrated luminosity of $9 \,\textrm{fb}^{-1}$ collected by the LHCb experiment, searches are performed for $B^+_c$ mesons decaying to a charm and a charmless meson pair. Five products of branching fraction, ${\cal B}(B^+_c\!\to DX)$, and fragmentation ratio $f_c\big/f_u$ are reported, \begin{align*} R_{D^+ K^{*0}} &= ( 1.42 \pm 0.23 \pm 0.07 \pm 0.11 ) \times 10^{-6}, \\ R_{D^{*0} K^+} &= (1.46 \pm 0.30 \pm 0.11 \pm 0.05 ) \times 10^{-6}, \\ R_{D^+_s φ}\ \ \ &= ( 4.0\pm 1.3 \pm 0.2 \pm 0.5) \times 10^{-7 }, \\ R_{D^0 K^+}\ &= ( 9.7 \pm 1.0 \pm 0.4 \pm 0.3 ) \times 10^{-7}, \\ R_{D^0 π^+}\ &<\ 1.4 \times 10^{-7}\ \text{at 95\% CL}. \end{align*} In each case, the first uncertainty is statistical, the second is systematic and the third includes external uncertainties. The first result is a first observation, the second and third exhibit clear evidence and the fourth improves the precision of previous measurements by a factor two. Additionally, the $CP$ asymmetry in $B^+_c\!\to D^0 K^+$ decays is measured and found to be compatible with zero. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: All figures and tables, along with any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/1627

Report number: CERN-EP-2025-088, LHCb-PAPER-2024-035

ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models

Authors: Xiaolin Chen, Xuemeng Song, Wenhao Shi, Xianjing Han, Mong-Li Lee, Wynne Hsu

Abstract: Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotional perception of a given image and explains the underlying rationale. Although related benchmarks exist, they are limited by inconsistent individual annotations, which hinder the d… ▽ More Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotional perception of a given image and explains the underlying rationale. Although related benchmarks exist, they are limited by inconsistent individual annotations, which hinder the derivation of majority-supported culture-level emotion labels, and imbalanced cultural coverage. Thus, we present ArtECulture, a benchmark containing 6,792 artworks with culture-specific emotion labels and explanations across English, Chinese, and Arabic cultures, with balanced Western and non-Western content. Evaluations of 16 open- and closed-source Multimodal Large Language Models (MLLMs) under a zero-shot setting reveal that the task remains challenging, with the best model achieving below 50\% accuracy. To address this limitation, we introduce a retrieval-augmented culture-conditioned emotion understanding framework, which leverages a concept-based cultural emotion knowledge base to inject explicit cultural knowledge into MLLMs without additional training. The framework improves both culturally aligned emotion prediction and grounded explanation generation. Our benchmark and code will be publicly released. △ Less

Submitted 4 August, 2026; originally announced August 2026.

CorePath: A Breast-Specialized Pathology Foundation Model for Core Needle Biopsy Diagnosis and Risk-Controlled Report Generation

Authors: Ting Yin, Danning Li, Chen Shu, Xiaoxia Yao, Boyu Fu, Yujing Chang, Tianyu Shi, Mengna Feng, Jie Chen, Jing Fu, Xiuli Xiao, Tianlin Li, Mumin Shao, Jiaxin Bi, Wenchuan Zhang, Xiaoyan Wu, Xiao Han, Zhang Zhang, Yuhao Yi, Hong Bu

Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions. We developed CorePath, a breast-specialized multimodal pathology foundation model fine-tuned from PRISM using 7901 paired CNB whole-slide images and diagnostic reports from two centers.… ▽ More Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions. We developed CorePath, a breast-specialized multimodal pathology foundation model fine-tuned from PRISM using 7901 paired CNB whole-slide images and diagnostic reports from two centers. Evaluated across six CNB cohorts and two public breast pathology benchmarks without task-specific retraining, CorePath consistently outperformed PRISM across cancer detection, invasion assessment, and histological subtyping. It achieved weighted area under the receiver operating characteristic curves (AUCs) of 0.9526-0.9735 for five-class CNB histological subtyping across private centers. On public benchmarks, CorePath outperformed leading pathology foundation models, achieving the highest weighted AUCs of 0.7780 for BCNB invasive carcinoma subtyping, 0.8178 for BRACS lesion stratification, and 0.8252 for BRACS fine-grained classification. In report generation, CorePath reduced the overall non-breast hallucinations from 30.1% to 2.8%, demonstrating improved domain fidelity after breast-specific adaptation. CorePath-CRG further combined conformal subtype-confidence gating with Learn-Then-Test risk control to enable selective report release, subtype-level fallback, and deferral. CorePath-CRG achieved zero non-breast hallucinations among released outputs and showed the strongest overall performance in pathologist-validated LLM-based Evaluation Scores and quantitative report-generation metrics across most centers. These results demonstrate that domain-specialized foundation models with statistical risk control offer a promising approach for accurate breast CNB diagnosis and reliable report generation. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: The code will be made publicly available upon publication

Screening phonon-mediated superconductors from static orbital Hamiltonians

Authors: Jian-Feng Zhang, Ze-Feng Gao, Xiao-Qi Han, Dingshun Lv, Miao Gao, Kai Liu, Xinguo Ren, Zhong-Yi Lu, Tao Xiang

Abstract: The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transparent framework that identifies strong-EPC materials directly from static orbital-based Hamiltonians without explicit phonon perturbation calculations. Verification using density functional perturbation theory (DFPT) for r… ▽ More The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transparent framework that identifies strong-EPC materials directly from static orbital-based Hamiltonians without explicit phonon perturbation calculations. Verification using density functional perturbation theory (DFPT) for representative superconductors shows that the framework captures semi-quantitatively the EPC scale at substantially lower computational cost. Applied to more than 36,000 compounds in the MattKeyBond database, it identifies 34 dynamically stable superconducting candidates with calculated $T_c > 10$ K after DFPT verification. These candidates reveal two distinct routes to relatively high-$T_c$ superconductivity: a metallized covalent $σ$-bond route that is more favorable for achieving high-$T_c$ superconductors, and a Fermi-level density-of-states accumulation route that can enhance $T_c$ but usually to a more limited extent. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: 12 pages, 5 figures, 2 tables

CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation

Authors: Xianjing Han, Yuhan Su, Yang Deng, Dong Ma, Wee Peng Tay, Bin Zhu

Abstract: Text-to-video (T2V) generation models have advanced rapidly, yet their ability to represent diverse cultural contexts remains underexplored. Existing benchmarks mainly focus on perceptual quality, physical plausibility, and text-video alignment, but do not directly assess whether generated videos capture culturally specific objects, actions, rituals, visible text, or audio cues. We introduce Cultu… ▽ More Text-to-video (T2V) generation models have advanced rapidly, yet their ability to represent diverse cultural contexts remains underexplored. Existing benchmarks mainly focus on perceptual quality, physical plausibility, and text-video alignment, but do not directly assess whether generated videos capture culturally specific objects, actions, rituals, visible text, or audio cues. We introduce CultureVidBench, a comprehensive benchmark for evaluating cultural understanding in T2V generation. CultureVidBench contains 1,000 curated prompts covering 12 countries, 6 continents, 8 cultural regions, and 14 cultural aspects organized into three categories: material culture, social practice & performance, and ritual & ceremony. Designed specifically for video generation, CultureVidBench emphasizes dynamic and multimodal cultural representation, including social interactions, ritual procedure, and culturally appropriate visible text and audio. We evaluate seven representative T2V models through human user studies and MLLM-based automatic assessment across cultural faithfulness, multimodal cultural rendering, semantic adherence, and perceptual quality. Results show that although current models achieve strong semantic adherence and visual quality, they often fail to faithfully capture fine-grained cultural details, particularly for underrepresented regions, rituals, and multimodal cultural cues. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: Project page:https://hanxjing.github.io/CultureVidBench/

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

Authors: Xinheng Han, Jianfei Wang, Yu Chen, Xiang Wang, Shuai Li, Weixing Li, Feng Pan

Abstract: Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-object binding, object cardinality preservation, and precisely localized grounding and segmentation outputs. However, existing group-relative reinforcement learning methods provide only response-level supervision, creating a granularity mismatch for s… ▽ More Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-object binding, object cardinality preservation, and precisely localized grounding and segmentation outputs. However, existing group-relative reinforcement learning methods provide only response-level supervision, creating a granularity mismatch for structured multi-object prediction: a single advantage is broadcast to all tokens in a response, without distinguishing individual box contributions. To address this mismatch, we propose MCR-GRPO, a marginal contribution assignment framework that derives box-level credit directly from each sampled response. Specifically, Marginal Contribution Reward (MCR) estimates each predicted box's contribution through a leave-one-out comparison, measuring how the matched set value changes when the box is removed from the response. After within-response normalization, records that improve the set value receive positive credit, while redundant or harmful ones are suppressed. To make marginal attribution stable and informative, we further introduce a Continuous Matched Set Value Evaluator that integrates permutation-invariant matching, count-aware normalization, and graded localization. MCR-GRPO maps normalized box-level marginal advantages to the token spans that generated each box, preserving GRPO's response-level comparison while enabling box-aware optimization of structured multi-object grounding. Experiments across REC, DOD, segmentation, and counting benchmarks show state-of-the-art performance over prior GRPO-based baselines. △ Less

Submitted 2 August, 2026; originally announced August 2026.

AIMold: An Autonomous AI-based Pipeline for Complex Mold Design

Authors: Pengyun Qiu, Shuo Wang, Zeyuan Chen, Yihao Zhi, Chongjie Ye, Xiaoguang Han

Abstract: Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designin… ▽ More Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designing these intricate assemblies is a laborious process that relies heavily on expert knowledge. Furthermore, the scarcity of public datasets has hindered the development of effective learning-based solutions. To bridge these gaps, we introduce MoldCAD, a curated dataset that pairs complex single-body CAD parts with industry-standard mold assemblies. Each entry includes the upper and lower molds, parting surfaces, demolding orientations, and necessary auxiliary components. The dataset comprises 4,934 CAD models and over 3,850 mold assemblies, totaling more than 23k individual models. Building upon this dataset, we propose a comprehensive pipeline that predicts demolding orientations, identifies auxiliary components, and constructs parting surfaces to derive a complete, manufacturing-ready mold assembly for downstream CAD/CAM workflows. Our results demonstrate a promising path toward fully automated industrial mold design and contribute to the broader advancement of manufacturing-aware CAD generation. △ Less

Submitted 1 August, 2026; originally announced August 2026.

Comments: Accepted to ECCV 2026. Code is available at https://github.com/tb2-sy/AIMold

DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation

Authors: Yunfan Lou, Hewen Gao, Xiyu Zhu, Zhuoran Qiao, Xuan Han, Yifan Yang, Yifan Ye, Boxian Yao, Zhibo Pang

Abstract: Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on the current frame and execute large backbones synchronously, limiting motion awareness and responsive control in dynamic scenes. We propose DynamicWAM, a compact WAM for dynamic object manipulation with dual-path motion conditioning. DynamicWAM intro… ▽ More Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on the current frame and execute large backbones synchronously, limiting motion awareness and responsive control in dynamic scenes. We propose DynamicWAM, a compact WAM for dynamic object manipulation with dual-path motion conditioning. DynamicWAM introduces history-flow conditioning, encoding temporally aligned optical-flow frames alongside the current observation through a frozen pretrained video VAE to preserve spatial motion structure, while injecting kinematic descriptors of displacement, duration, velocity, and acceleration into the action expert to provide motion magnitude and timing. The two complementary paths are fused through joint world-action attention. A distilled compact backbone and real-time chunking (RTC)-based asynchronous execution further enable responsive control. On DOMINO, DynamicWAM achieves a 38.2% success rate and a 53.2 manipulation score, outperforming all evaluated baselines. Across 12 real-world tasks spanning linear, circular, and compound target motion, it achieves a 46.7% average success rate, exceeding the strongest baseline by 22.9 percentage points. △ Less

Submitted 6 August, 2026; v1 submitted 1 August, 2026; originally announced August 2026.

Comments: 18 pages, 9 figures. Project page: https://dynamicwam.github.io/

CurveShift: Is Agent Progress Scalar? Separating Level from Shape

Authors: Hanwen Xing, Pengyun Wang, BingXu Meng, Kumail Alhamoud, Xiang Li, Jicheng Wang, Xin Yu, Xinyang Han, Xiaomin Li, Philip Torr, Yuexing Hao

Abstract: Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that most of the apparent shift in gains toward harder tasks does not reflect a chang… ▽ More Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that most of the apparent shift in gains toward harder tasks does not reflect a change in the shape of the difficulty-response curve. On METR time-horizon data, a single Rasch model with rising ability reproduces this pattern, so it is largely explained by ceiling effects rather than a qualitative change in capability. This echoes how the choice of metric can make claimed emergent abilities look like a property of the models themselves. We then identify a smaller hard-task effect that survives this control. Isolating it is difficult on agentic benchmarks, because newer models are usually run with newer agentic harnesses, so a gain on hard tasks cannot be assigned to the model or its scaffold. We break the confound with LiveCodeBench, a public competitive programming benchmark that runs no agentic scaffold while pairing dated models with an exogenous difficulty ordering. After accounting for the rise in overall ability, models released after September 2024 still gain on the hardest problems beyond what their easy and medium performance predicts, by about +0.40 logits under our most conservative assumption, raising the hard-problem solve rate from roughly 18% to 25%. The effect is led by the strongest reasoning models and holds for hard tasks that need only short reasoning, not autonomy over long horizons. We present this as a result specific to competitive programming, since our clean identification rests on a single coding benchmark. We release the LiveCodeBench Difficulty Panel (66 dated models x 1,055 problems) and our analysis code. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: 25 pages, 4 figures, 7 tables. Data and code: https://github.com/harvenstar/CurveShift

ACM Class: I.2.7; I.2.6; G.3

Modification of $Υ$ production in $p$O and OO collisions 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, S. Akar, K. Akiba, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, Z. Amos , et al. (1166 additional authors not shown)

Abstract: The production rates of $\mathitΥ(2S)$ and $\mathitΥ(3S)$ mesons relative to that of the $\mathitΥ(1S)$ state are measured in $pp$, $p$O, and OO collisions by the LHCb collaboration. The ratios measured in $pp$ data are consistent with previous LHCb measurements at different center-of-mass energies. Only slight relative suppression of the $\mathitΥ(2S)$ and $\mathitΥ(3S)$ states is found in $p$O c… ▽ More The production rates of $\mathitΥ(2S)$ and $\mathitΥ(3S)$ mesons relative to that of the $\mathitΥ(1S)$ state are measured in $pp$, $p$O, and OO collisions by the LHCb collaboration. The ratios measured in $pp$ data are consistent with previous LHCb measurements at different center-of-mass energies. Only slight relative suppression of the $\mathitΥ(2S)$ and $\mathitΥ(3S)$ states is found in $p$O collisions, while in OO collisions the $\mathitΥ(2S)$ is suppressed by a factor of $\sim2$, with evidence for suppression of the $\mathitΥ(3S)$. The significant suppression in OO data, compared to the small effect in $p$O data, shows the emergence of additional suppression mechanisms in the relatively small OO collision system. Models incorporating quark-gluon plasma formation in OO collisions successfully describe the data. Implications for the interplay between cold nuclear matter effects and color screening in a deconfined quark-gluon plasma are discussed. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcb-glance.cern.ch/alcm/public/analysis/full-details/6994/ (LHCb public pages)

Report number: LHCb-PAPER-2026-027, CERN-EP-2026-207

EEG-JEPA: Structured Latent Prediction for EEG Foundation Models

Authors: Jinhao Li, Zhiyuan Ma, Xueqiao Han, Zhongye Xia, Xinche Zhang, Shanghong Xie, Yixuan Liu, Yongjian Li, Runmin Gan, Tianlin Huo, Sen Song

Abstract: Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This r… ▽ More Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer contextual latent states produced by an exponential-moving-average target encoder that observes the complete input. EEG-JEPA organizes target design along three complementary dimensions: target content specifies what representation is predicted, target support specifies where prediction occurs over structured electrode--time regions through Neurotopology-Aware Multi-scale Electrode-Temporal Masking (N-MET), and target depth specifies at which encoder layers supervision is applied. Together, these designs shift EEG pretraining from recovering missing measurements to inferring latent states from structured electrode--time context. We evaluate EEG-JEPA through controlled objective comparisons, frozen multitask transfer, and full fine-tuning. Under the same backbone, pretraining corpus, and training duration, EEG-JEPA improves the 14-task frozen macro balanced accuracy from 40.49% to 50.42% over CBraMod-style masked waveform reconstruction. Multi-source continuation further raises this result to 52.94%, the highest average among the EEG foundation models evaluated on EEG-FM-Bench. Under protocol-matched full fine-tuning, EEG-JEPA also improves the nine-task average balanced accuracy from 68.98% to 70.65%. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: 9 pages, 6 figures

Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

Authors: Xinyan Guan, Jiali Zeng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Fandong Meng

Abstract: Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivations mislead users. We characterize this \textit{futile reasoning} phenomenon through systematic analysis, revealing universal capability overreach and systematic miscalibration between capability and behavior. The dominan… ▽ More Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivations mislead users. We characterize this \textit{futile reasoning} phenomenon through systematic analysis, revealing universal capability overreach and systematic miscalibration between capability and behavior. The dominant failure mode is specious reasoning, which outputs look superficially valid but contain subtle errors, escalating with task difficulty. To address this, we introduce \textbf{CaRL} (\textbf{Ca}pability-\textbf{a}ligned \textbf{R}einforcement \textbf{L}earning), which aligns model behavior with capability boundaries through reward shaping that incentivizes refusal over futile reasoning and hindsight refusal augmentation that converts failures into refusal supervision. Experiments demonstrate a substantial reduction in futile reasoning while preserving performance across task difficulties, effectively achieving capability-aligned behavior without sacrificing utility. \footnote{https://github.com/icip-cas/Knowing-When-to-Quit} △ Less

Submitted 31 July, 2026; originally announced July 2026.

Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks

Authors: Youting Wang, Xiao Han, Dingyan Shang, Yuan Tang, Bowen Liu

Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, InjecAgent, AgentHarm, AgentDojo) as measurements to be validated, running each under its official implementation and author-provided scorer on up to 22 models, with MMLU and GPQA measured by us under one protocol as a capability composite. The metr… ▽ More Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, InjecAgent, AgentHarm, AgentDojo) as measurements to be validated, running each under its official implementation and author-provided scorer on up to 22 models, with MMLU and GPQA measured by us under one protocol as a capability composite. The metric is the first problem. On any binary trace-judgment benchmark scored by $F_1$, an ``always positive'' policy attains $F_1 = 2π/(1+π)$; on R-Judge that is $0.690$, above five of the 21 models that actually discriminate. The three broad-coverage benchmarks then rank the same 18 models differently, and the trade-off behind that disagreement is a small-panel artifact: R-Judge specificity against AgentHarm safety correlates $-0.64$ at $n{=}7$ and $+0.02$ at $n{=}18$, and a quarter of random size-7 subsets reach $|ρ| \geq 0.5$ around that near-zero value. Held-out validity turns on which outcome you pick. Capability predicts task success ($ρ{=}{+}0.60$) but correlates negatively with misalignment safety ($ρ{=}{-}0.44$, $n{=}21$). On their paired $n{=}20$ panel, the corresponding contrast is $Δ{=}{-}1.00$ (95% CI $[-1.48, -0.49]$, $p<0.001$), and it survives leave-one-organization-out and organization-clustered bootstrap analyses. On an expanded 41-model panel, the misalignment correlation weakens to $-0.16$ (95% CI $[-0.54, +0.22]$) and jailbreak strengthens to $+0.34$, though neither change is significant. \mbox{AgentHarm} shows the strongest held-out association, $ρ{=}{+}0.72$ with three-template jailbreak safety after controlling capability. But both instruments score harmful compliance, so this is evidence of convergent validity rather than general safety. Naming the benchmark, metric, target behavior, and model panel is the minimum a safety claim needs. △ Less

Submitted 29 July, 2026; originally announced July 2026.

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

Authors: Tao Wen, Shuai Shao, Pei Ke, Xu Han, Jie Zou, Guannan Li, Tao Tian, Jinjie Qiu, Lan Wang, Ke Qin

Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, ta… ▽ More Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks. To address these limitations, we introduce MiGUE-Bench, a systematic benchmark for assessing the performance of LLMs in multi-granularity event analysis. To support large-scale evaluation, we first develop an LLM-driven self-correcting annotation framework called MiGUE-Pipeline, enabling scalable acquisition of high-quality source data of events with automatic labels. Then, we design four core tasks in our benchmark, i.e., event detection, relation reasoning, structure induction, and future prediction, to probe model competence at different levels, from atomic event details to complex cross-document narratives. Extensive experiments on state-of-the-art LLMs and retrieval-augmented generation (RAG) methods delineate the current capability boundary and identify critical deficiencies, providing insights into the future improvement of LLMs in challenging event analysis tasks. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 9 pages. Published in the Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026)

ACM Class: I.2.7

Journal ref: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '26), pp. 3464-3472, 2026