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Computer Vision

InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views

Yongzhen Hu, Hao Yu, Jiawei Wang et al. (+7 more)

Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and introduce InfiniSplat v2, which improves rendering quality through architectural and training changes. Our method decouples geometry and attribute prediction with a frozen geometry branch, aligns predicted Gaussians using ground-truth camera poses during training, and adopts large-angle target view supervision. Across six datasets, the feed-forward outputs of the same trained model achieve state-of-the-art mean LPIPS and DISTS among the evaluated methods in both pose-conditioned and pose-free settings. In the pose-conditioned setting, mean LPIPS and DISTS decrease by 25.8% and 32.9%, respectively, compared with the strongest evaluated baselines. We further find that opacity learned with only image reconstruction supervision suppresses geometrically inconsistent points. Project page: https://zju3dv.github.io/InfiniSplat2/
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All authors Yongzhen Hu, Hao Yu, Jiawei Wang, Xianming Zeng, Jianyun Xu, Xingliang Liu, Ruizhen Hu, Xiaowei Zhou, Hujun Bao, Sida Peng

High Energy Physics (Theory)

Black hole cohomologies in N=4 SYM

Seunggyu Kim, Jehyun Lee, Siyul Lee et al. (+1 more)

We systematically investigate BPS cohomologies of $\mathcal{N}=4$ super-Yang-Mills (SYM) theory with gauge group SU$(N)$ with $N=2, 3, 4$, focusing primarily on the BMN sector. We find non-Coulomb graviton cohomologies for $N>2$. For $N>2$, the lightest black hole cohomology in the BMN sector always carries non-trivial $R$-charge and corresponds to the brane-fused type. Therefore, the `core' black holes without hair are `heavier'. We also find that a family of brane-fused cohomologies in $N=3$ is not lifted at the first quantum order. At a fixed charge sector, gravitons carry definite parity under charge conjugation. Up to the levels we have tested, every black hole cohomology either has opposite parity to that of gravitons or is lifted by quantum corrections. We conjecture that black hole cohomologies always carry parity opposite to gravitons in the same charge sector.
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All authors Seunggyu Kim, Jehyun Lee, Siyul Lee, Jaewon Song

Functional Analysis

Weak (1,1) Shur multipliers and the Hörmander–Mikhlin–Schur theorem

Yongqiang Tian

Let $S_m$ be the Schur multiplier with symbol $m$. We characterize the boundedness of $S_m:\mathcal S^1\to\mathcal S^{1,\infty}$ by approximation of weighted columns in a common finite-dimensional subspace. If $W(m)$ is the weak $(1,1)$ bound and $\fD(m)$ is the column approximation constant, then $\tfrac12W(m)\le\fD(m)\le2W(m)$. These estimates hold uniformly under amplification. We also prove the Hörmander–Mikhlin–Schur weak $(1,1)$ estimate in every Euclidean dimension under the usual integer-order condition. The proof combines Cuculescu's projections, adjacent dyadic grids and tensor polynomial interpolation on a binary block decomposition.
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Signal Processing

Dual-Polarized Rate-Splitting Multiple Access for mmWave Communications: A Practical Design and Prototype Implementation

Álvaro Pendás-Recondo, Álvaro F. Vaquero, Jesús Alberto López-Fernández et al. (+1 more)

This work presents a dual-polarized rate-splitting multiple access (RSMA) design for millimeter-wave (mmWave) or frequency range 2 (FR2) communications. Focusing on a practical implementation, we consider the specific scenario of a single transmitter serving two receivers (users) within the same time, frequency, and code resource block. The transmitter and each receiver are equipped with a pair of dual-polarized antennas. We first present the system formulation and compute the achievable rate expressions for discrete constellations. Based on these expressions, we propose and evaluate two receiver architectures: one based on successive interference cancellation (SIC) and a non-SIC decoder. Both designs exhibit comparable performance, and the combination of polarization diversity and RSMA provides the system with significant flexibility to adapt to different channel conditions when serving both users, while the non-SIC decoder enables a simpler hardware implementation. We then present a prototype implementation of the proposed design, operating at a carrier frequency of 25 GHz, and evaluate it by measuring the symbol error rate (SER) of the different data streams under various signal-to-noise ratio (SNR) and polarization diversity conditions. The obtained measurements show good agreement with software simulations, thereby validating the system implementation. To the best of our knowledge, this is the first mmWave RSMA prototype reported in the literature and the first to exploit polarization diversity. Furthermore, given the selected carrier frequency, the proposed design may also find applicability in the upper portion of the envisioned FR3 band (7-24 GHz).
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All authors Álvaro Pendás-Recondo, Álvaro F. Vaquero, Jesús Alberto López-Fernández, Marcos R. Pino

Machine Learning

Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning

Yaomengxi Han and Debarghya Ghoshdastidar

The success of transformers is attributed to several factors, including their capacity to express formal languages over variable-length inputs, and ability of the attention mechanism to focus on relevant input features. However, there is limited understanding of the training dynamics of shallow transformers, specifically how to decouple the learning of the attention and the output (feedforward) layer. A natural extension of the theory of one-step gradient feature learning in feedforward networks suggests that one could first learn the attention weights with a single gradient step, and then train the output layer. We show that this fails for a shallow transformer trained to recognize a star-free language from unbounded-length input. We prove that the transformer can be trained to recognize the language with a three-phase gradient descent: the output layer is updated first, followed by an attention update, and the output layer is fine-tuned in the third phase. In contrast, the language cannot be learned by training only the attention or output layer, or by a single attention-layer update followed by finetuning the output layer. We empirically show that “attention learning lagging output layer learning" is beneficial in other language recognition, regression, and in-context learning tasks as well.
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Robotics

AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning

Jiawei Zhang, Chang Liu, Litao Deng et al. (+2 more)

Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human effort and robot time. Simulation provides a scalable alternative, yet converting a real workspace into a robot-learning-ready environment still requires substantial manual effort for object extraction, asset processing, coordinate calibration, and trajectory specification. We present AutoRSR, a highly automated real-to-sim-to real framework that converts scanned real-world workspaces into reusable embodied simulation assets and scalable manipulation data. Given multi-view observations, robot states, and open-vocabulary semantic queries, AutoRSR reconstructs the workspace, extracts individual object assets, and automatically registers the reconstructed scene to the robot base frame at metric scale. Starting from a small number of reference motions, AutoRSR then generates executable manipulation trajectories and scales them through trajectory interpolation and domain randomization. After workspace scanning and semantic queries, the remaining pipeline requires no manual object annotation, scene-to-robot calibration, or robot teleoperation. Across three real-world manipulation tasks, policies trained on 1K automatically generated episodes per task achieve 73.3–86.7% real-world success rates, comparable to pi 0.5 trained from 150 human teleoperated demonstrations, while reducing the human time required for data generation by an order of magnitude. These results demonstrate that real-world observations can be converted into metrically aligned, executable, and scalable simulation data with substantially reduced human effort.
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All authors Jiawei Zhang, Chang Liu, Litao Deng, Tao Zhang, Qin Jin

Computer Vision

Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation

Yuheng Chen, Yufan Chen, Zhuojun Cai et al. (+8 more)

Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scientific figures, captions, and visual semantics weakly connected, and fails to measure whether a model can produce a single page-level parse that is both structurally faithful and semantically accessible. We introduce Read-Parse-Describe, a unified dataset for document parsing with visual element description generation. Given a page image, models are required to output an ordered list of layout elements containing bounding boxes, semantic labels, reading-order indices, recognized textual content, and alternative text descriptions for visual elements. The benchmark is built from 2,402 documents, comprising 18,163 continuous pages, 125,218 layout elements, and 11,338 visual instances. Besides, we further propose a plug-and-play space-awareness enhancer that reuses multi-layer visual features and injects grid-aware spatial guidance into vision-language tokens for layout-aware generation. Experiments across end-to-end VLMs and pipeline parsers show that existing systems still struggle to jointly recover reading order and visual semantics, while our enhanced model achieves strong joint performance. These results highlight the need to move beyond isolated document parsing modules toward unified models that jointly read, parse, and describe complex documents. The new dataset and code will be made publicly available at https://github.com/yufanchen96/RPD
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All authors Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng, Jiale Wei, Di Wen, Weijia Fan, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen

Artificial Intelligence

TreeFlex: Agents Navigating Their Own Search Trees

Dongzhuo Li, Fangzhou Cheng, Weiwei Li et al. (+8 more)

A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes its limited capacity and obscures relevant evidence, making focused reasoning increasingly difficult. We introduce TreeFlex, a framework in which the agent's own policy decides where to search. The agent records each intermediate state, with a short summary, as a node in a search tree maintained by the harness. The policy is a single language model that acts in two roles. In the Reflection role, it has a global view of the tree through these summaries and picks a node to resume, together with an instruction. In the Execution role, the harness restores the path that led to that node, and the model carries out the instruction with the usual task tools until it saves a new node or proposes a solution, which Reflection then approves or sends back to another node. Through these choices, the policy decides whether to deepen the current line, branch from or revisit an earlier state, verify a proposal, or stop, without a fixed traversal order or a score for each node. We train navigation, task actions, checkpointing, and proposal review jointly with a tree-aware adaptation of GRPO, using task-level outcome rewards augmented by process rewards. After small-scale RL of a 4B model, with the same active-context limit for all methods, TreeFlex reaches 48.8% accuracy on a challenging deep-research benchmark, versus 41.5% for flat ReAct and 42.8% for FoldAgent, and its Pass@8 is about ten points higher than either. On SWE-bench Verified, with all three systems trained with RL from the same 9B model, TreeFlex resolves 54.9% of tasks, versus 43.3% for FoldAgent and 45.2% for its step-wise variant.
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All authors Dongzhuo Li, Fangzhou Cheng, Weiwei Li, Kevin Han, Haixu Ma, Zhongchang Sun, Ruiyi Li, Serena Li, Xiangjun Fan, Lizhu Zhang, Yuandong Tian

Robotics

VIGOR-MPC: Vision-Guided Objective Regulation MPC

Jelena Trisovic, Rahel Rickenbach, Marek Landert et al. (+2 more)

Controlling an agent in an environment solely perceived through onboard vision is challenging: perception yields noisy, variable-length, and partially occluded context, while onboard compute is shared between perception and control, limiting the planning horizon executable in real time. We propose VIGOR-MPC, a modular student-teacher framework, agnostic to the perception pipeline, which addresses reference tracking across navigation tasks by distilling a privileged, field-of-view-aware long-horizon MPC into a computationally lightweight short-horizon MPC whose cost parameters are determined online from vision. The teacher operates in simulation with full state knowledge and a multi-trajectory formulation that guarantees the agent can stay within its field of view. The student observes only the noisy, occluded context produced by a perception module and learns, via a multi-level recurrent mapping, to infer cost parameters that aim to recover the teacher's behavior despite missing information. We evaluate VIGOR-MPC on vision-based autonomous racing in simulation and on hardware. Using only onboard perception of the track, VIGOR-MPC generalizes to tracks unseen during training and approaches the lap times of the privileged long-horizon teacher while reducing per-step computation by over 90%.
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All authors Jelena Trisovic, Rahel Rickenbach, Marek Landert, Panagiotis Minos, Melanie N. Zeilinger

Astrophysics of Galaxies

IRAC-Excess Galaxies in the NIRSpec Wide Survey: physical properties of bright EELGs at $z\sim7$ and implications for Reionization

Katherine Ormerod, Renske Smit, Joris Witstok et al. (+4 more)

We present an analysis of 23 Spitzer/IRAC excess galaxies at $5.66 < z < 7.64$, observed as part of the NIRSpec Wide GTO survey. With NIRSpec PRISM spectroscopy, we measure a median rest-frame [OIII]$+\rm{H}\beta$ EW $=1583$~Å, consistent with predictions from the pre-JWST selection and we identify $17/23$ $(\sim74\%)$ of our sample as extreme emission line galaxies (EELGs), based on a selection of [OIII]$+\rm{H}\beta$ EW $\geq1000$~Å. The typical EELG is powered by star-formation rather than active galactic nuclei (AGN) based on their line widths, line ratios and absence of high-ionization lines, though we cannot exclude an AGN contribution in a few individual sources. We find that our luminous ($\langle M_\mathrm{UV}\rangle=-20.7$) EELG sample is characterised by sub-solar metallicities $(7.38 < \rm 12 + \log(O/H) < 8.12)$, young stellar populations ($t_\star \sim 20$ Myr), and enhanced ionizing photon production efficiencies, with median $\log_{10}(\xi_\mathrm{ion}/\mathrm{erg}^{-1}~\mathrm{Hz}) = 25.62$. We find that more UV luminous ($\sim L_\mathrm{UV, z=7}^\ast$) EELGs exhibit systematically lower O32 ratios than fainter ($\sim 0.1\times L_\mathrm{UV}^\ast$) EELGs at fixed [OIII]$+\rm{H}\beta$ EW, indicating less extreme radiation fields and/or lower gas densities than their fainter counterparts. Furthermore, bright EELGs exhibit low neutral hydrogen column densities, consistent with a scenario in which they have largely ionized their circumgalactic medium (CGM). Despite this, we find very low Lyman-$\alpha$ escape fractions ($<2$%), suggesting these systems reside in relatively small ($\lesssim 0.25$~pMpc) ionized regions of the intergalactic medium (IGM), possibly suggesting that bright galaxies have a minor contribution to reionization.
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All authors Katherine Ormerod, Renske Smit, Joris Witstok, Anna de Graaff, Michael V. Maseda, Andrew J. Bunker, Chris Willott

Machine Learning

Are We Overthinking One-Step Generation?

Woobin Park, Jaeyeon Kim, Yilun Du et al. (+2 more)

Diffusion and flow matching are now dominant frameworks for generative modeling in continuous domains, but they typically require multiple network evaluations at inference. This has motivated extensive research on one-step generative modeling primarily built on diffusion and flow, though existing approaches often rely on complex training machinery. In this work, we introduce Priming Models, a simple two-stage framework for training one-step generators. Motivated by the difficulty of distributional training from scratch, Priming Models are first trained on a reconstruction objective, e.g., pixel-space denoising at a fixed noise level, before undergoing feature-space distributional post-training. Our central scientific finding is that this simple reconstruction stage is already enough to entirely alleviate the difficulty of distributional training. Empirically, across class-conditional image generation, text-to-image generation, and world modeling, Priming Models significantly advance the Pareto frontier of training compute vs. generation quality. On ImageNet at $256 \times 256$ resolution, we achieve a state-of-the-art FID of $1.19$ among one-step generators using approximately one-tenth the training compute of prior methods with comparable FID. We open-source our codebase at https://github.com/JaeyeonKim01/priming.
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All authors Woobin Park, Jaeyeon Kim, Yilun Du, Sitan Chen, Bohyung Han

Soft Condensed Matter

Dynamic morphing of non-Euclidean active shells enabled by flutter under a steady stimulus

Roberto Marchello, Pietro Maria Santucci and Giovanni Noselli

Synthetic active surfaces are usually designed to attain a prescribed static configuration, with dynamic shape changes typically requiring modulation of the actuation. We show that thin active shells can instead sustain dynamic morphing under a steady stimulus by harnessing flutter instability. We focus on polyelectrolyte hydrogel shells immersed in a viscous fluid and subjected to a constant and uniform electric field, and specialize the non-Euclidean framework for thin structures to account for field-induced activity through a spontaneous curvature evolving in time with the system configuration. Examining elliptic plates and cylindrical shells as test cases, we integrate linear stability analyses with nonlinear simulations to first locate the onset of flutter and then unveil the resulting dynamics, characterized by thrust-generating shape changes. In all cases, the emergent behavior stems from the interplay between active reconfiguration and elasticity in the fluid environment. For plates, the problem is purely flexural in the linear regime, while the competition between bending and stretching becomes crucial in selecting the ensuing shapes and swimming trajectories at large deformations. Conversely, for cylindrical shells bending and stretching are coupled at all levels, with the latter becoming essential to sustain flutter. Experiments on millimeter-sized hydrogel plates provide tangible support for our theoretical findings through shape reconstructions performed by combining digital image correlation with optical stereo microscopy. Flutter thus offers a route to untethered soft robotic and microfluidic devices based on active shells and operating under a stimulus carrying neither an intrinsic frequency nor a characteristic length scale.
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Group Theory

Generation by lazy and invertible cellular automata over the Klein four-group

Edgar Alcalá-Arroyo, Alonso Castillo-Ramirez, Haobo Ma et al. (+1 more)

Let $G=C_2\times C_2$ and $A=\{0,1\}$. We characterize the submonoid of $\text{CA}(G;A)$ generated by lazy and invertible cellular automata. A cellular automaton belongs to this submonoid if and only if, whenever it maps the two free $G$-orbits onto the same free orbit, it maps both constant configurations and some two-point orbit to the same constant. The proof gives a factorization using elementary orbit collapses and three two-cell lazy maps. The criterion yields the cardinality $59\,712$ and identifies the maps missed by a weaker constant-separation invariant. Adjoining one idempotent generates the full monoid, and the generated submonoid is contained in exactly one maximal proper submonoid, of cardinality $65\,024$. The submonoid generated by lazy maps alone has $4\,007$ elements, and for both generating sets the maximum shortest-word length is six. The criterion and the cardinalities $59\,712$ and $4\,007$ are formally verified in Lean.
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All authors Edgar Alcalá-Arroyo, Alonso Castillo-Ramirez, Haobo Ma, Wenlin Zhang

Artificial Intelligence

Argument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chains

Simon Baur, Sumire Honda, Wojciech Samek et al. (+1 more)

Reliable uncertainty estimation is essential for deploying large language models in high-stakes reasoning tasks, yet most existing approaches rely on expensive sampling strategies or shallow token-level signals that ignore the internal logical structure of model outputs. We introduce Argument Graph Uncertainty, a post-hoc framework for multiple choice question tasks, that estimates model confidence directly from the logical structure of a single chain-of-thought reasoning trace. Our method uses either a lightweight instruction-tuned model or a simple deterministic rule to segment the reasoning chain into propositional units, then applies a NLI model to infer relational edges and construct two complementary argument graphs over these segments. The resulting edge scores are aggregated into a Dirichlet posterior over answer options, whose concentration yields calibrated uncertainty estimates reflecting both local step-to-step coherence and global logical consistency across the reasoning trace. Evaluated on GPQA, MMLU-Pro and TruthfulQA, our method consistently surpasses token-based and consistency-based uncertainty baselines across multiple reasoning models. We further demonstrate through extensive combination analyses that argument graph uncertainty captures an orthogonal signal to existing approaches, making it a complementary component in uncertainty ensembles. Crucially, our framework requires no additional calls to the evaluated model and relies entirely on small, efficient auxiliary models, producing fully interpretable, per-node uncertainty attributions at low computational cost.
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All authors Simon Baur, Sumire Honda, Wojciech Samek, Jackie Ma

Computation and Language

Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages

Stephen Fernandes and Narayan Choudhary

India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take place over telephones, in meetings, and in media. Knowing who spoke when, the task of speaker diarization, is essential to speech applications for such conversation: voice agents conversing with several people at once, speaker attributed transcription of meetings and calls, contact center analytics, and media monitoring. We present a systematic study of speaker diarization across all 22 languages on IndicDiarBench and introduce Anuvani-Diarization, which adapts a self-supervised end-to-end segmentation model to Indian conversation with uncertainty aware silver supervision. Evaluated zero shot alongside nine recent diarization systems, it reaches a duration weighted diarization error rate (DER) of 16.78% on all 1,164 recordings, the lowest of all systems we ran and second only to the best published result (16.0%), and leads the three strongest rivals in 14 of 22 languages. It matches its parent on six international benchmarks. We also show that the benchmark's utterance level annotation convention is a hidden variable worth 1.9 to 4.1 DER points, depending on the system, and that a convention learned from other Indian transcriptions does not transfer.
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Machine Learning

Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming

Anjali K.S., Manoj Kumar Panda and Chandramani Singh

Integrated Sensing and Communication (ISAC) systems rely on accurate Direction-of-Arrival (DoA) estimation for effective beamforming and spectral-efficiency enhancement. While numerous classical and deep-learning-based DoA estimation methods have been reported in the literature, their suitability for real-time ISAC operation remains insufficiently understood because practical deployment requires consideration of both estimation accuracy and computational latency. This paper presents a unified evaluation framework for systematically comparing classical and learning-based DoA estimators within an ISAC beamforming pipeline. Representative classical and learning-based DoA estimators, together with an ensemble architecture, are integrated with Singular Value Decomposition (SVD)-based beamforming and evaluated under identical operating conditions. The impact of DoA estimation accuracy, inference latency, beamforming-weight generation time, and resulting effective spectral efficiency is investigated. Results show that different estimators occupy distinct operating points in the accuracy-latency-performance design space. Although learning-based approaches substantially reduce estimation time compared with MUSIC, the overall processing latency increasingly becomes dominated by the computational cost of SVD-based beamforming. Among the evaluated methods, the Regression Convolutional Neural Network (RCNN) exhibits a favorable balance between estimation accuracy, computational efficiency, and beamforming performance, making it a promising candidate for real-time ISAC deployment. The findings further suggest that future low-latency ISAC systems may benefit from beamforming strategies that reduce or avoid computationally intensive SVD-based beamforming-weight generation.
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Cosmology and Nongalactic Astrophysics

Thermalization of the Primordial Stochastic Gravitational-Wave Background

Hongguang Zhang, Zhengyang Qiu, Sirui Wang et al. (+1 more)

It is conventionally believed that the Stochastic Gravitational-Wave Background (SGWB) from inflation provides information about the earliest universe. However, in this Letter, we present a different picture. Our analysis starts from the observation that the inflation-predicted spectrum implies gravitons reside in high-occupation states, which significantly enhances their self-interactions and triggers rapid thermalization. We show that approximately $80\%$ of the primordial gravitons participate in this thermalization, forming a Bose–Einstein distribution and thereby erasing the primordial information. Evaluated using typical inflation parameters, this thermalized spectrum yields a present-day peak frequency of $f_{0, \text{peak}}^{(\text{th})} \sim 1.4 \times 10^8 \, \mathrm{Hz}$ in the hundred-megahertz band with a peak amplitude of $\Omega_{0, \text{peak}}^{(\text{th})} \sim 3.5 \times 10^{-13}$. Due to the sharp drop-off away from the peak, the resulting spectrum lies beyond the reach of low-frequency observatories like PTAs, LISA, and LIGO, but remains uniquely accessible to high-frequency detectors such as microwave cavity resonators and dielectric haloscopes.
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All authors Hongguang Zhang, Zhengyang Qiu, Sirui Wang, Jiayi Wei

High Energy Physics (Experiment)

Measurement of Born cross sections of $e^+e^-\to pK^{-}\bar\Sigma^{0}$ + c.c. at $\sqrt{s}$ = 3.5-4.9 GeV and partial wave analysis at $\sqrt{s}$ = 3.773 GeV

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+770 more)

Using $e^+e^-$ collision data collected by the BESIII detector at BEPCII corresponding to a total integrated luminosity of 44.6 $\rm fb^{-1}$, we present the first measurement of the Born cross sections for the process $e^+e^-\to pK^{-}\bar{\Sigma}^{0}$ + c.c. at fifty-six center-of-mass energies between 3.51 and 4.95~GeV. By fitting the dressed cross sections of $e^+e^-\to pK^{-}\bar{\Sigma}^{0}$ + c.c. with the assumption of a power-law function plus a charmonium(-like) resonance, i.e., $\psi(3770)$, $\psi(4040)$, $\psi(4160)$, $Y(4230)$, $Y(4360)$, $\psi(4415)$, $\psi(4500)$, $Y(4660)$ or $\psi(4710)$, no significant charmonium(-like) state decaying into $pK^{-}\bar{\Sigma}^{0}$ + c.c. is observed. The product of branching fractions and electronic partial widths including their upper limits at the 90% confidence level are provided for each decay. In addition, a partial wave analysis of the process $e^+e^-\to pK^{-}\bar{\Sigma}^{0}$ + c.c. at $\sqrt{s}$ = 3.773 GeV is performed to investigate $N^{*}$, $\Lambda^{*}$ and $\Sigma^{*}$ resonances in the $K^{-}\bar{\Sigma}^{0}$ and $pK^{-}$ invariant mass spectra. Significant contributions are found from the $\Lambda(1520)$, $\Sigma(1670)$, $\Sigma(1915)$, $N(1900)$, $N(2300)$ and $N(2570)$. The masses, widths and production fractions for each component are determined.
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All 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, I. Boyko, R. A. Briere, A. Brueggemann, D. Cabiati, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, E. Di Fiore, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, Z. J. Dong, M. C. Du, S. X. Du, Shaoxu Du, X. L. Du, Y. Q. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, K. X. Fan, Y. H. Fan, J. Fang, Jin Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Xu Gao, Y. Gao, Y. N. Gao, Y. Y. Gao, Yunong Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, A. Gilman, K. Goetzen, J. Gollub, J. B. Gong, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, H. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, Z. Guo, A. Guskov, J. Gutierrez, J. Y. Han, T. T. Han, X. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Y. X. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, L. K. Jia, X. Q. Jia, D. Jiang, S. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, L. Krümmel, Y. Y. Kuang, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, C. K. Li, Chunkai Li, Cong Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, H. P. Li, Hui Li, J. N. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, M. R. Li, M. T. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. Li, S. X. Li, S. Y. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. H. Li, Y. B. Li, Y. C. Li, Y. G. Li, Y. P. Li, Z. H. Li, Z. J. Li, Z. L. Li, Z. X. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, Z. Z. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Y. Liu, Ke Liu, Kun Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. M. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. P. Liu, X. T. Liu, X. Y. Liu, Y. Liu, Y. B. Liu, Yi Liu, Z. A. Liu, Z. D. Liu, Z. L. Liu, Z. Q. Liu, Z. X. Liu, Z. Y. Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Maity, S. Malde, L. M. Mansur, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, I. V. Ovtin, S. Pacetti, Y. Pan, C. Y. Pang, A. Pathak, Y. P. Pei, M. Pelizaeus, G. L. Peng, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, L. Pöpping, F. Z. Qi, H. R. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, K. Ravindran, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, W. R. Ruangyoo, N. Salone, A. Sarantsev, Y. Schelhaas, M. Schernau, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, Ch. Y. Shi, H. Shi, J. L. Shi, J. Y. Shi, M. H. Shi, S. S. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, M. Stolte, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, H. Tabaharizato, N. T. Tagsinsit, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, Z. H. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, K. Yu. Todyshev, I. Uman, E. van der Smagt, B. Wang, Bin Wang, Bo Wang, C. Wang, Chao Wang, Cong Wang, D. Y. Wang, F. K. Wang, H. J. Wang, H. R. Wang, J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, Mi Wang, N. Y. Wang, P. Wang, S. Wang, Shun Wang, T. Wang, W. Wang, W. P. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Yanning Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Zhi Wang, Ziyi Wang, D. Wei, D. H. Wei, D. J. Wei, H. R. Wei, F. Weidner, H. R. Wen, S. P. Wen, U. Wiedner, G. Wilkinson, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Z. Wu, H. L. Xia, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, D. B. Xiong, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, X. Y. Yang, Y. Yang, Y. G. Yang, Y. H. Yang, Y. M. Yang, Y. Q. Yang, Y. Z. Yang, Youhua Yang, Z. Y. Yang, W. J. Yao, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, K. Yi, Junhao Yin, Qiqin Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Yongchao Yu, C. Z. Yuan, H. Yuan, J. Yuan, Jie Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. J. Zeng, Yujie Zeng, Y. C. Zhai, Y. H. Zhan, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, Gengyuan Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. L. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Han Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jin Zhang, Jiyuan Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, Q. Z. Zhang, R. Y. Zhang, S. H. Zhang, S. N. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Yao Zhang, Yu Zhang, Yu Zhang, Z. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Zh. Zh. Zhang, Zhilong Zhang, Ziyang Zhang, Ziyu Zhang, G. Zhao, J.-P. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, W. Q. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, M. Zhuge, J. H. Zou, J. Zu

Information Theory

Cache-Aided Linear Function Computation

Xiaohong Cai, Ruze Zhang, Laigang Guo et al. (+1 more)

We study cache-aided linear computation in distributed systems where each source generates and stores its own data. An external receiver requests a linear combination of these data, and the sources use information cached before the request is known to reduce the communication needed to recover the requested result. In our model, each of the $N$ sources stores its own independent file over a finite field and has an additional cache of normalized size $M$. The receiver must recover any requested linear combination with zero error from the messages sent by the sources over separate noiseless links. We establish a lower bound on the worst-case communication rate over any finite field by analyzing several requests under the same cache placement. This bound determines the optimal rate when no additional cache is available. For binary files, we propose parity-aided placement (PAP), a coded-placement scheme that combines cached parities with uncoded symbols. For every $N\geq3$, we characterize its communication rate for each request and optimize its placement parameters to obtain a closed-form worst-case rate. The resulting achievable rate meets the lower bound for $0\leq M\leq1/(2N-1)$ and $M\geq(N-1)/N$, establishing the optimal memory–rate tradeoff in these two ranges.
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All authors Xiaohong Cai, Ruze Zhang, Laigang Guo, Raymond W. Yeung

Systems and Control

Delay-Enabled Adaptive Learning of Time-Varying Parameters

Chengzhi Yuan and Mingxi Zhou

This paper proposes a novel adaptive learning method for unknown time-varying parameters by deliberately incorporating a delay into the parameter update. Specifically, a conventional adaptive estimator is augmented with only one additional dynamic state, which estimates the parameter variation rate from the difference between current and previous parameter estimates. The method fundamentally changes the influence of time variation on estimation performance: the resulting estimation error is governed by the second derivative of the unknown parameter rather than its first derivative. Under persistent excitation, a small-gain analysis proves exponential stability of the complete estimation-error system and establishes explicit bounds for the parameter, state-prediction, and parameter-rate errors. In particular, accurate learning of linearly time-varying parameters and their unknown rates can be guaranteed with errors converging exponentially fast to zero. Numerical comparisons with conventional adaptive estimation and a recent timevarying parameter estimator demonstrate superior learning performance in the considered examples. The proposed delay-enabled adaptive estimator requires neither state derivatives nor any assumed model of parameter variation, and its implementation consists of one delayed difference and a first-order filter. These results thus provide a direct extension of adaptive learning from constant to time-varying parameters without sacrificing the simplicity of the conventional adaptive estimation mechanism.
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Statistical Methodology

Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations

Seonghun Lee

Regression-based survey weighting connects model-based population estimation with design-based inference through equivalent weights. For nonlinear models, equivalent weights can be defined locally through the sensitivity of a poststratified population estimator. However, conventional regression models may inadequately capture nonlinear relationships and complex interactions across sparse poststratification cells. We develop a survey-weighting framework based on hierarchical Gaussian process (HGP) regression, combining structured group-level effects with kernel-based smoothing to share information across related cells. We derive Gaussian posterior prediction formulas that exploit the structured covariance representation to reduce computational costs. We also establish a uniform convergence result for the posterior predictive mean under stated regularity conditions and construct local equivalent weights by matching the first-order sensitivity of a plug-in poststratified estimand to that of a weighted average of fitted response means, with both derivatives taken with respect to the latent mean-scale vector. Simulations show that HGP achieves lower population-weighted prediction error under nonparametric structure, whereas logistic regression performs well under correct parametric specification. Additional analyses examine sample size, frame coverage, subgroup prediction, and population-mean agreement. An application to the Future of Families and Child Wellbeing Study illustrates the method. Overall, this work extends local equivalent-weight construction to nonparametric modeling and provides a computational framework and conditional theoretical results for survey weighting in structured, sparse populations.
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Robotics

TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation

Fucai Zhu, Xidong Zhang, Yichi Zhang et al. (+23 more)

Large-scale robot learning increasingly relies on diverse human demonstrations, yet tactile manipulation data remain difficult to scale, and existing multimodal datasets often lack consistent quality control and structured supervision. We present TacClaw-Infinity, a large-scale structured Vision-Tactile-Language-Action (VTLA) dataset for contact-rich manipulation, collected with TacClaw, a tactile-enabled UMI-style handheld interface that records synchronized visual observations, fingertip tactile signals, and end-effector trajectories without requiring a target robot during data collection. TacClaw-Infinity is built through a distributed worldwide collection across homes, laboratories, industrial workplaces, retail stores, and outdoor environments including tasks contributed by academic and industrial collaborators. Together, these efforts yield over 10,000 hours, 1 million episodes, and 1 billion frames spanning more than 1,000 task types. We develop a scalable pipeline for trajectory reconstruction, multimodal quality control, embodiment-aware filtering, and structured annotation, covering contact-centric segmentation, subtask description, and spatial grounding. We validate TacClaw-Infinity at both the data and model levels: At the data level, we assess the collection interface, the action-space compatibility of the curated demonstrations, and the structured annotations, while for the model level, we demonstrate cross-embodiment transfer across three heterogeneous robots without robot-specific demonstrations.
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All authors Fucai Zhu, Xidong Zhang, Yichi Zhang, Yipan Wang, Fubo Wang, Zibin Wu, Kaiqi Li, Jianhao Jiao, Shihan Lu, Di Hu, Mengdi Xu, Siyu Zhu, Xiaoxiao Long, Yao Yao, Xun Cao, Zhuo Xu, Koichi Hashimoto, Qifeng Chen, Zhe Li, Jianfei Yang, Haofei Lu, Danica Kragic, Shuicheng Yan, Jianghua Duan, Michael Yu Wang, Weihao Yuan

Computer Vision

Beyond Static Cues: Unified Dynamic Detection of MLLM Backdoors via Dual-Flow Trajectory Analysis

Yinzhen Huang, Jiahe Xie, Yitong Sun et al. (+2 more)

Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, yet their susceptibility to backdoor attacks remains a formidable challenge. While existing defenses have evolved from visual-centric patch cues to unsupervised internal attention statistics, they are fundamentally constrained by a reliance on static snapshots and attack-specific heuristics. Given the intricate interplay between dual modalities, the vast design space for multimodal triggers allows diverse threats, such as textual injections, sub-perceptual noises, and coordinated cross-modal triggers, to easily circumvent such rigid priors, particularly when attack types are unknown in real-world applications. In this paper, we rethink backdoor detection by shifting the diagnostic paradigm from static observations to dynamic output evolution. We observe that a successful attack inevitably hijacks the model's decoding trajectory, skewing internal modal contributions during generation while maintaining an abnormal output invariance despite input perturbations. To capture this behavior, we propose EVO-Detect, a unified detection framework that monitors the dynamic evolution of internal signals during early decoding steps through two complementary channels: Attention Flow and Logits Flow. The attention branch measures how image-text attention allocation evolves during early generation, whereas the logits branch quantifies the sensitivity of the output distribution to modal perturbations through interventional ablations. We then convert these trajectories into compact sample-level metrics and normalize them against negative-control signatures so that natural perturbations are less likely to be mistaken for backdoor evidence. Extensive evaluations show that our approach substantially outperforms state-of-the-art baselines such as BYE and TCAP across diverse attack families.
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All authors Yinzhen Huang, Jiahe Xie, Yitong Sun, Ruochen Zhang, Xingxing Wei

Robotics

Efficient Test-Time Adaptation For Robot Policies

Motasem Alfarra, Pietro Mazzaglia, Markus Peschl et al. (+2 more)

Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload changes, sensing degradation), and retraining or manual recalibration is often infeasible under on-device latency and memory constraints. We study test-time adaptation (TTA) for robotic control: adapting a pretrained policy online and in a self-supervised manner during execution, without access to rewards. Our starting point is a lightweight baseline that updates only the actor using a frozen pretrained critic as a deployment-time performance surrogate. Building on this, we introduce TEMPO, a test-time entropy-regularized, memory-guided policy optimization method that stabilizes critic-driven updates under severe shift. TEMPO combines (i) an entropy-promoting regularizer on the critic's predictive distribution to mitigate overconfident extrapolation, and (ii) a score-based memory, inspired by prioritized replay, that prioritizes low-value ("hard") observations and applies importance sampling to focus limited computation on the most informative deployment conditions. Across Go1, Go2, and G1 in MuJoCo Playground under four representative shifts and multiple architectures, TEMPO yields consistent gains, reaching up to 80% improvement in a single-device setting. We further validate on a physical Unitree Go2 with an added calf payload, where fully on-device adaptation improves performance over the non-adapted baseline across real-world episodes.
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All authors Motasem Alfarra, Pietro Mazzaglia, Markus Peschl, Daniel Dijkman, Christos Louizos

Combinatorics

Power quotients of plactic-like monoids

Antoine Abram, Florent Hivert, James D. Mitchell et al. (+2 more)

In this paper we describe the quotients of several plactic-like monoids by the least congruences containing the relations $a^{\sigma(a)} = a$ for $\sigma(a) \geq 2$ for every generator $a$. The starting point of this paper is the recent paper of Reutenauer and the first author about the stylic monoid. This monoid is the quotient of the plactic monoid by the relations $a^2 = a$ for every generator $a$. The plactic-like monoids considered here are the plactic monoid itself, the Chinese monoid, the hypoplactic monoid, the sylvester monoid, the metasylvester and hyposylvester monoids. In each case we describe a set of normal forms and the idempotents. We obtain formulae for their cardinalities and we describe their isomorphism types.
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All authors Antoine Abram, Florent Hivert, James D. Mitchell, Jean-Christophe Novelli, Maria Tsalakou

Number Theory

S-squareful values of binary quadratic forms

Sarah Sophie Pohl

Shute showed that the leading constant in the asymptotic formula for squareful values of the form $ax^2+by^2$ does not match with the conjecture established by Pieropan, Smeets, Tanimoto and Várilly-Alvarado. In this paper, $S$-squareful values of this form are considered for $S$ the set of primes that divide $a$ and $b$. We prove an asymptotic formula for these numbers and show that the leading constant of the asymptotic formula still does not match the conjecture. Furthermore, we consider the updated conjecture established by Chow, Loughran, Takloo-Bighash, and Tanimoto regarding the leading constant for Campana points in the given setting and show that this constant is consistent with our computed constant.
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Cryptography and Security

Building Sound Evaluation Practices for Rust-Based Security Frameworks

David Paaßen and Lucas Davi

Background: Rust is a modern programming language known for its focus on usability, concurrency, and security. However, empirical studies reveal that Rust is still affected by software vulnerabilities, with hundreds of memory safety issues found in Rust programs, including Rust's standard library. As a result, additional tool-assisted security testing is crucial to enhance the security of Rust code. Evaluating such tools, however, is highly complex. While there is substantial research on evaluating C/C++ security frameworks, including test sets, serious issues in published evaluations have been identified, undermining their scientific soundness and reproducibility. In practice, selecting an inadequate implementation (e.g., a fuzzer) can lead to missed vulnerabilities ultimately compromising a program's security. Aims: Our methodology aims to help researchers to conduct scientifically sound evaluations in order to prevent similar issues with the evaluation of security tools and frameworks in the Rust ecosystem. Method: We systematically analyze existing evaluation literature to develop the first sound methodology for Rust based evaluations. Our guidelines provide enough flexibility to support a variety of use cases. To quantify the performance of a security tool, we introduce an automated approach to inject unsafe code coverage oracles, which require no forward porting, avoid overfitting, and allow updating test sets in the future. We also present methods to quantify the diversity of a Rust test set, ensuring that the results are applicable to a large set of existing Rust programs. We practically apply our methodology in our evaluation of the most popular Rust fuzzers. Results: We show the practical applicability of our design and conduct an evaluation with a diverse test set of 20 Rust projects. [...]
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Mesoscale and Nanoscale Physics

Coherent and dissipative coupling of multiple photon-magnon hybrids in a chiral microwave resonator

Supanat Chantarattana, Charlie W. F. Freeman, Daniel Prestwood et al. (+3 more)

We demonstrate control between level repulsion and level attraction of photon-magnon coupling spectra in a chiral microwave-photon resonator loaded with a 2 mm YIG sphere. By systematically varying the coupling mode volume via the YIG position and the dc-field orientation, we tune the ratio between coherent and dissipative coupling strengths, $J/\Gamma$, and thereby switch between level repulsion and level attraction in the hybrid spectra. Full-wave electromagnetic and magnetization-dynamics simulations reveal multiple photon-magnon hybrid modes with distinct spatial overlap factors, which quantitatively account for gap sizes and their angular dependence. Strong nonreciprocity appears near $J/\Gamma \approx 1$, yielding 28.6 dB isolation ratio in agreement with a unified single-bath input-output model and full-wave simulations. Angular dependence of spectral characterization confirms a picture of the chirality-selection rule and our method of geometric tuning might be useful for considering reconfigurable on-chip isolators.
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All authors Supanat Chantarattana, Charlie W. F. Freeman, Daniel Prestwood, Nimisha Arora, Vincent Castel, Hidekazu Kurebayashi

Analysis of PDEs

Quantized slow blowup dynamics for the energy-critical Schrödinger map flow

Jacek Jendrej, Jitao Xu, Xu Yuan et al. (+1 more)

We study the Cauchy problem for the energy-critical Schrödinger map into the 2-sphere: \[ \partial_{t}u = u\times\Delta u, \quad (t,x)\in\mathbb{R}^{1+2}, \quad u(t,x)\in\mathbb{S}^2 \] for equivariant initial data of homotopy degree $k=1$. Given any integer $L\in\mathbb{N}_+$, we exhibit a set of initial data arbitrarily close to the ground state harmonic map in the energy norm such that the corresponding solution blows up in finite time with a quantized rate \[ \lambda(t)\sim \frac{\left(T-t\right)^L}{\left|\log(T-t)\right|^{\frac{2L}{2L-1}}} \quad\mbox{as}\ \ t\uparrow T. \] Our proof is inspired by the techniques developed for the energy-critical harmonic map heat flow in a similar context by Raphaël and Schweyer~[Anal. PDE 7 (2014), 1713–1805]. However, its adaptation to a Schrödinger-type equation requires the introduction of a new energy-Virial-Morawetz Lyapunov functional tailored to quantized slow blowup dynamics.
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All authors Jacek Jendrej, Jitao Xu, Xu Yuan, Lifeng Zhao

Artificial Intelligence

Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence

Xin Li, Xiaohuan Gu, Yifan Wang et al. (+6 more)

Vision-Language Models (VLMs) have made substantial progress in spatial reasoning, and self-evolving agents further extend these capabilities to embodied environments by learning from interactions. However, current self-evolution remains particularly challenging in embodied spatial tasks, complex and multi-step decisions from partial visual evidence making it difficult to determine why an execution fails. The failure may stem from misperception, insufficient exploration, or incorrect planning, yet execution feedback alone rarely reveals which component is responsible. As a result, simply accumulating interaction experience can reinforce ineffective strategies. To address this, we propose HiMeco, a brain-inspired self-evolving agent for embodied spatial intelligence, inspired by metacognitive monitoring and hippocampal memory consolidation. Through Hippocampal-Metacognitive Reflection, HiMeco diagnoses failures in perception-exploration-planning (PEP), then compares execution attempts to derive reusable strategies and guidance for avoiding recurring errors. It also abstracts interaction programs into parameterized skills, using evidence contracts and execution-based validation to determine their retention and reuse. These strategies and skills guide subsequent interactions, with memory updated only between rounds and model parameters kept frozen. HiMeco achieves state-of-the-art performance on ESI-Bench, and it also improves accuracy on EmbSpatial-Bench by 3.94 percentage points without evolution.
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All authors Xin Li, Xiaohuan Gu, Yifan Wang, Shaobin Ling, Tianli Shi, Jingao Xu, Zhimeng Yin, Dapeng Wu, Yun Wang