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Tuesday 22 September

Quantum Physics

Post-Selection-Based Stochastic Quantum Battery

Aman Verma, Roson Nongthombam and Amarendra K. Sarma

Quantum batteries are quantum systems that store useful energy, which can be subsequently extracted to perform work. In this paper, we investigate the charging dynamics of the quantum battery under continuous measurements and post-selection. By post-selecting the trajectories with no quantum jump $|1\rangle \rightarrow |0\rangle$ in a three-level system, the dynamics is effectively confined to a two-level $(|2\rangle-|1\rangle)$ manifold. We analyze the resulting charging dynamics under both $\sigma_x$- and $\sigma_y$-driving protocols and investigate the roles of detuning, coherent driving, non-linearity, and measurement backaction. The generation and dynamics of quantum coherence, which plays a pivotal role in quantum battery, are also analyzed under post-selection. The advantage and comparison of our results over those of a generic two-level system and with the Lindblad dynamics are also highlighted. Our work explores the interplay between the measurement axis and the driving protocols that govern the charging rate, ergotropy, and quantum coherence of the non-Hermitian, highly tunable, and controllable quantum battery.
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Optimization and Control

Open Quadratic Optimization Programs

Alberto Padoan

The paper studies open optimization programs, that is, parametric optimization programs viewed as open systems, and their interconnection. Each open optimization program is assigned a behavior and interconnection is defined through variable sharing. For a class of open quadratic programs (QPs), we characterize closure and well-posedness under interconnection and, for modules interconnected by adding their objectives, analyze the convergence of a sequential module-by-module solution method, deriving error bounds for the resulting approximation. We further illustrate that the variables through which modules are interconnected indicate an appropriate behavioral description, and each description requires dedicated interconnection rules. This perspective suggests a route toward compositional analysis and design of optimization architectures.
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Computer Vision

Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa

Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye et al. (+4 more)

Purpose: To develop and evaluate an interpretable artificial intelligence (AI) framework for glaucoma screening from low-cost portable, handheld retinal fundus photographs in a West African population and to compare its performance with clinical tabletop fundus imaging. Methods: We used data from a community-based study of 681 participants (1,362 eyes) in Nigeria, comprising 414 glaucoma, 478 glaucoma suspect, and 470 non-glaucoma eyes. Fundus photographs were acquired using the low-cost handheld, portable Volk Viva retinal camera and the Canon CR-2-AF tabletop camera. We fine-tuned component models separately to each device to perform vessel segmentation, cup and disc boundary segmentation, and feature extraction to detect optic nerve head features. A final classification model combined these components to classify scans as glaucoma, glaucoma suspect or non-glaucoma. Feature-weight analysis and Gradient-weighted Class Activation Mapping were used for interpretation. Results: The models performed well on both Volk Viva and Canon CR-2-AF images: Vessel segmentation: 0.98 Dice Coefficient (DC) (Volk) and 0.94 DC (Canon); Cup and disc segmentation: 0.95 DC (Volk) and 0.96 DC (Canon); Optic nerve head feature detection: area under the receiver operating characteristic curve (AUCs) of 0.83$\pm$0.03 (Volk) and 0.87$\pm$0.04 (Canon); Classification model: AUCs of 0.85$\pm$0.01 (Volk) and 0.93$\pm$0.01 (Canon). Reports for each image, present model decision confidence scores and decision-rationale visualizations to support clinical interpretation. Conclusions: Volk Viva results were reasonably comparable to Canon CR-2-AF in the component models and not far behind in classification. This shows that interpretable AI combined with low-cost, portable imaging may enhance community-level glaucoma screening, especially in settings with limited specialist access and resources.
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All authors Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye, Martin Buist, Michael A. Hauser, Olusola Olawoye, Michaël J.A. Girard

Robotics

The Cartesian Hand: In-Hand Manipulation with All-Linear Fingers

Boxi Xia, Bokuan Li, Ryan Shin et al. (+3 more)

Robotic manipulation has increasingly pursued human-like dexterous hands with many articulated degrees of freedom, offering rich manipulation capabilities at the cost of mechanical and control complexity. At the other extreme, parallel grippers are simple and robust, but provide little ability to manipulate an object after grasping it. Operating articulated objects such as threaded containers, manufacturing tools, and laboratory instruments often requires a second gripper, an external fixture, or coordinated arm motion. We introduce the Cartesian Hand, a 7-DoF end-effector that rethinks dexterous manipulation by combining independent grasping and relative manipulation within a single end-effector using only linear motion. Two independently actuated parallel grippers hold different parts of an object, while four translating fingertips generate relative motion between the grasped parts. Its configuration-independent fingertip kinematics allow manipulation to be composed from simple linear motion primitives. The Cartesian Hand is particularly suited to objects structured around common mechanisms such as threads, pivots, linear guides, plungers, and triggers. We demonstrate cap opening and closing, pipetting, pumping, two-handle manipulation, screwdriving, trigger actuation, and in-grasp reorientation across 35 objects spanning laboratory, manufacturing, and household settings. The same manipulation procedures transfer from a fixed-base robot arm to a humanoid, where we demonstrate bimanual laboratory manipulation using two Cartesian Hands. These results show that versatile in-hand manipulation capability can emerge from a mechanically simple architecture when independent grasping and relative motion are designed directly into the end-effector. We will open-source all software and hardware design. Our website is https://generalroboticslab.com/cartesian_handv1.
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All authors Boxi Xia, Bokuan Li, Ryan Shin, Zijiang Yang, Jiaxun Liu, Boyuan Chen

Robotics

Induced Riemannian Metrics for Motion Planning with Constraints

Phone Thiha Kyaw, Thomas Cohn, Miguel Angel Rogel Garcia et al. (+1 more)

In constrained motion planning problems, task and loop-closure constraints restrict a robot's motion to a curved, lower-dimensional submanifold of its configuration space. Planners measure path length with a metric, which sets the cost of moving in each direction. Under the Euclidean metric, this cost is the same everywhere, whereas under a general Riemannian metric, such as the kinetic-energy metric, the cost can vary with direction and configuration. Existing methods often describe the submanifold either implicitly, as a constraint level set, or explicitly, through a parameterization. The implicit representation is typically combined with the Euclidean metric of the configuration space, and the explicit representation with the parameter domain, so the path length that a planner minimizes depends on the representation. Instead, we measure path length with the induced metric, which the submanifold inherits from a Riemannian metric on the configuration space. The implicit and explicit representations yield the same induced metric, expressed in different coordinates, and hence the same geometry. This result holds for any Riemannian metric on the configuration space, not only the Euclidean one. The choice of metric is therefore independent of the choice of representation. Using this result, we extend planning under a Riemannian metric from unconstrained spaces to constraint submanifolds by applying the induced metric in both a sampling-based planner and a trajectory optimizer. For an explicit representation, the induced metric also accounts for the distortion that the parameterization introduces. In experiments on a bimanual manipulation setup with two Franka arms under end-effector task constraints, we compare the Euclidean and kinetic-energy metrics.
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All authors Phone Thiha Kyaw, Thomas Cohn, Miguel Angel Rogel Garcia, Jonathan Kelly

Operator Algebras

Covariant representations of actions of inverse semigroups: a new approach to the reduced and essential crossed-product C*-algebras

Astrid an Huef and Ilija Tolich

We consider an action of an inverse semigroup on a C*-algebra $A$ and use it to construct a groupoid of germs with unit space the spectrum of $A$. Motivated by the representation theory of C*-algebras of groupoids, we construct a concrete family of covariant representations for the action. We use this family to give new definitions of the reduced and essential crossed product C*-algebras that avoid, respectively, passing to the double commutant and local multiplier algebra of $A$. Our reduced crossed product is isomorphic to the one defined by Exel, Buss and Meyer, and when the inverse semigroup is quasi-countable our essential crossed product is isomorphic to the one defined by Kwaśniewski and Meyer.
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Computer Vision

C2FXNet: Coarse-to-Fine Scene Expert for Unified Object Detection across Adverse Weather

Tianle Fang, Zhenbing Liu, Chong Yin et al. (+2 more)

Object detection in adverse weather remains challenging because severe degradations weaken visual quality and disrupt semantic feature representations across diverse scenes. Existing methods usually rely on condition-specific designs, which limits their ability to generalize within a unified detector. In this paper, we propose a Coarse-to-Fine Scene Expert Network (C2FXNet) that achieves unified detection through hierarchical scene guidance. Specifically, C2FXNet introduces a dual-level guidance mechanism consisting of a Multi-step Reasoning Router (MRR), which performs GRU-based recurrent scene reasoning over compressed multi-scale visual cues and frozen coarse scene prototypes, and a Fine Scene Refinement (FSR) module, which uses image-specific semantic cues to modulate high-level features for local variation handling. Furthermore, a Scene-aware Mixture-of-Experts (SMoE) dynamically combines scene-specific experts under the joint guidance of MRR and FSR. By coupling coarse scene reasoning with fine-grained semantic refinement, C2FXNet enables robust multi-scene detection without scene-specific training. Extensive experiments on RTTS, ExDark, and our newly constructed Adverse Weather Dataset (AWD) demonstrate that C2FXNet consistently outperforms state-of-the-art methods across foggy, dark, and clear conditions, reaching 63.70%, 71.14%, and 54.19% mAP on RTTS, ExDark, and AWD, respectively. The source code will be released at https://github.com/PolarisFTL/C2FXNet.
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All authors Tianle Fang, Zhenbing Liu, Chong Yin, Bolun Li, Haoxiang Lu

Machine Learning

Graph Domain Adaptation Does Not End with Representation Learning

Ziqian Liu, Yongxue Xu, Enze Zhang et al. (+3 more)

Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and graph structure. Existing methods primarily adapt graph representations through propagation redesign, distribution alignment, or source-to-target transition modeling, but still rely on a single graph-propagating path for target prediction. This leaves open whether an adapted graph representation exhausts the predictive evidence available in the target domain, since the graph-aware expert and graph-free local expert may exhibit different failure modes under topological shifts. To address this limitation, we propose EviGDA, an Evidence-Augmented Graph Domain Adaptation framework that complements graph representation adaptation with a graph-free local expert. The graph-aware expert performs message passing and entropy-aware marginal alignment, while the graph-free local expert learns solely from source node features and labels without graph propagation or target alignment. The two experts are optimized independently and combined only at inference through a task-level constant probability mixture, preserving complementary evidence without joint training, learned routing, or target pseudo-labels. Extensive experiments on ten datasets and 16 transfer tasks show that EviGDA outperforms state-of-the-art baselines. Complementary prediction paths add value beyond graph representation alignment.
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All authors Ziqian Liu, Yongxue Xu, Enze Zhang, Jiaqi Zhang, Hao Wang, Maolin Wang

Nuclear Theory

Physical interpretation and limits of photon–dilepton radial-flow tomography

Lipei Du

Thermal photons and dileptons provide complementary information on the temperature and collective expansion of matter created in relativistic heavy-ion collisions. Their combination has been proposed as an electromagnetic probe of radial flow: a comparatively flow-insensitive dilepton invariant-mass inverse slope is used to infer the photon inverse slope expected without transverse flow and compared with the observed photon slope. Here the physical basis and leading limitation of this construction are examined using a controlled expanding-fireball calculation. The dilepton and flow-free photon inverse slopes are tightly correlated because they respond similarly to changes of the underlying thermal scale, although the numerical relation depends on the spectral window and electromagnetic source. The flow-induced change of the photon inverse slope correlates more strongly with the photon-emission-weighted velocity component along the photon momentum than with a bulk-like radial-flow average. Prethermal source variations can bias the inferred flow-free photon reference and generate apparent flow even when the direct photon spectral-flow signal vanishes, while multiple photon and dilepton windows provide independent response patterns that can help diagnose this source ambiguity. These results clarify both what photon–dilepton radial-flow tomography measures and the source consistency required for its interpretation.
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Numerical Analysis

The Scott-Vogelius element is inf-sup stable on Freudenthal meshes for $k \geq 4$

Siqi Ding, Pingbing Ming, Haijun Yu et al. (+1 more)

The Scott-Vogelius element is a classical divergence-free mixed finite element for the Stokes problem that has attracted decades of research attention, yet its theoretical framework remains incomplete. In two dimensions, the inf-sup stability on Freudenthal and other regular meshes has been rigorously established. In three dimensions, Zhang established inf-sup stability on Freudenthal meshes for $k\ge6$ in 2011, while numerical evidence indicates that inf-sup stability remains true for $k=4,5$. In this paper, we strengthen Zhang's approach and prove that the Scott–Vogelius element is inf-sup stable on Freudenthal meshes for every velocity degree $k\ge 4$, thereby resolving a conjecture proposed by Farrell, Mitchell, and Scott in 2024. The proof proceeds by explicit constructions on local patches and does not rely on computer verification.
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All authors Siqi Ding, Pingbing Ming, Haijun Yu, Quanfan Zhu

Robotics

MotionForge: A Data Generation Pipeline and Large-Scale Benchmark for Long-Horizon Manipulation of Dynamic Objects with Domain Shifts

Mohan Liu, Dengchen Mei, Haotian Xian et al. (+7 more)

Recent advances in learning-based robot policies have demonstrated promising progress, yet they are predom- inantly evaluated in static or quasi-static environments. In dynamic manipulation, objects and scenes continuously evolve while the robot perceives, reasons, and acts. However, recent dynamic simulation benchmarks largely focus on short-horizon, reactive interactions with simple motion patterns and offer limited support for both systematic evaluation under domain shifts and model-agnostic real-time execution protocols. To bridge these gaps, we introduce MotionForge, the first large- scale simulation benchmark and data-generation pipeline tailored to jointly evaluate domain shifts and long-horizon interaction in dynamic manipulation. MotionForge comprises 40 dynamic interaction tasks spanning 11 distinct motion patterns, with dedicated support for 17 long-horizon tasks. Our benchmark introduces two key novelties: (1) a systematic evaluation protocol for assessing policy robustness under both single-factor (e.g., only backgrounds shift) and joint domain shifts (e.g., simultaneous shifts of objects, backgrounds, lighting, and speed); and (2) a decoupled, latency-aware execution protocol where the environ- ment continuously evolves independently of policy inference time. Extensive evaluations of representative general-purpose robot policies on our benchmark reveal substantial limitations under joint domain shifts. These findings expose a critical gap between current policy capabilities and the requirements of robust long- horizon manipulation of dynamic objects under domain shifts, establishing MotionForge as a comprehensive testbed for future research in embodied AI.
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All authors Mohan Liu, Dengchen Mei, Haotian Xian, Ruyang Han, Jiayi Sun, Xuanyu Chen, Haitian Zhang, Luxi Li, Kaimin Mao, Lin Wang

Robotics

MatcherCompass: A Deployment-Aware Benchmark to Guide Image Matcher Selection in the Wild

Hyunwoo Kim and Giseop Kim

Field robots operating across time of day and sensing modalities require accurate image correspondences within onboard time and resource budgets. However, accuracy and runtime reported for individual methods on a single device provide limited guidance for choosing a matcher and its configuration on a target platform. We present MatcherCompass, a deployment-aware benchmark for choosing local feature matchers in field robotics. Under common input and pose-evaluation procedures, we compare nine classical and learned matching pipelines across four image resolutions and supported numerical precisions. Four visual conditions cover viewpoint variation, day–night matching in visible and thermal imagery, and daytime visible–thermal matching. We evaluate pose accuracy using the area under the error–recall curve (AUC) at $5^\circ$, $10^\circ$, and $20^\circ$, and measure runtime, GPU memory, and energy per image pair on four GPU platforms spanning workstation and onboard computers. The results show that changes in hardware, input resolution, and numerical precision can move a matcher across a runtime budget boundary, altering the feasible choices. We organize the measurements into a selection guide that returns all configurations satisfying user-specified time and resource limits, together with their accuracy under the selected visual condition. MatcherCompass provides measured evidence for choosing matching pipelines that fit a robot's sensing conditions and computing hardware. Project page: https://matchercompass.github.io/.
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Robotics

SG-CPG: Severity-Gated Central Pattern Generators for Adaptive Quadruped Locomotion under Continuous Actuator Degradation

Adarsh Kumar Kosta and Kaushik Roy

An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scales its response with injury severity. This graded adaptation allows locomotion to persist despite partial loss of limb strength, rather than requiring a discrete transition between healthy and failed. Inspired by this behavior, we propose SG-CPG, a central pattern generator (CPG) for quadruped locomotion under continuous actuator degradation. SG-CPG preserves a frozen healthy CPG policy and introduces two severity-driven gates: a residual gate that re-coordinates all four legs and an amplitude gate that progressively shortens the weakened leg's stride as degradation increases. We emulate progressive degradation through two mechanisms: lowering the joint torque ceiling (ceiling mechanism) and scaling its low-level controller gains (gain mechanism), representing distinct forms of actuator weakening. Our simulations on a Unitree Go2 show that SG-CPG maintains a trot gait with 100% survival across an omnidirectional command schedule under 95% joint strength loss while tracking commands within 8%. Under a lowered torque ceiling, removing either severity path, the residual's severity observation or the amplitude gate, raises clipping at the weakened joint from 4.4% to 13.6% and 26.3% of steps at an 80% loss. On a real Go2, SG-CPG survives 28 of 29 forward and turning trials with up to 93% calf torque degradation. These results show that severity-gated adaptation can extend a healthy locomotion policy to progressive actuator degradation without treating the fault as a discrete failure.
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Artificial Intelligence

How Strongly Should Task State Influence an LLM Agent?

Chenyu Zhang, Wonbin Kweon and Jiawei Han

Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent systems either keep this state as text in the prompt and rely on the model to read that text, or move the state into a module that enforces it, and each system is evaluated as a whole, so no one knows how much reliability comes from the state being shown, told, or enforced. We fix the task rules, the model, and paired episodes and vary how strongly task state reaches the agent: a raw transcript, an exact checklist, per-turn directives from a state machine compiled from the brief and advanced only by execution receipts, or an enforcement gate on that machine that refuses state-violating actions; every episode is scored by exact payload matching against dynamic ground truth. Across three models, two reasoning regimes, and two domains, four findings hold without per-turn reasoning: displaying accurate state is unreliable, an unverified ledger the agent writes itself beats an accurate checklist it is shown, directives help in proportion to the model's obedience, and enforcement needs no obedience but is bounded by the correctness of its state and by the matcher that maps requests to steps; per-turn reasoning at a 235B agent compresses these separations without repairing the text rungs. The same gate, compiled from $\tau^2$-bench's airline policy, raises a 235B agent's pass$^1$ from 0.39 to 0.54 and changes nothing for a 35B agent that rarely violates the policy; on PM-Bench, where acting turns on recognizing a cue rather than on state, showing the record is the best rung–matching or beating both gates and reversing the ledger-over-checklist finding–and enforcing the matcher's judgement drops a 35B agent below its raw transcript. Enforcement pays when failures are state-decidable and frequent, and hurts when the gate's judgement is wrong.
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Computer Vision

Initialization and Stopping Tolerance in CPU Dermoscopic Segmentation

Wenhao Xu, Yixian Kong, Ting Pan et al. (+2 more)

Contour initialization and numerical stopping can jointly affect the evaluation of active-contour segmentation. We examine their interaction using the open-source scikit-image Chan-Vese implementation on a resized ISIC 2017 mirror. A fixed development set of 100 images selects a common input channel; all 600 images in the repository's held-out partition are then evaluated. Otsu thresholding is compared with checkerboard-, disk-, and Otsu-initialized contours under default and tighter level-set tolerances. At the default tolerance, Otsu initialization increases mean image Dice from 0.6011 to 0.6660 relative to checkerboard initialization, a paired difference of 0.0649 (95% image-bootstrap interval [0.0452, 0.0860]). Otsu thresholding alone achieves 0.6897. The default disk initializer stops after one iteration on 471 images. Tightening the tolerance reduces the Otsu-seed advantage over checkerboard initialization to 0.0197, with most runs reaching the 500-iteration limit. The default-tolerance advantage also reverses between small- and large-lesion strata. These findings show that an improvement over a generic initializer can coexist with deterioration relative to the threshold baseline. Evaluations should retain the unrefined mask as a comparator and report the initial-field definition, stopping tolerance, and observed iteration counts together.
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All authors Wenhao Xu, Yixian Kong, Ting Pan, Feilong Wang, Changwei Wang

Computer Vision

Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learning

H. Yue, C.-C. Chen, C.-N. Hsiao et al. (+4 more)

Atomic-scale phase imaging is central to resolving defects, interfaces, and weakly scattering atoms that govern the behavior of nanoscale materials. Electron ptychography delivers sub-ångström phase sensitivity but remains an offline technique, because its iterative reconstruction is computationally expensive and sensitive to experimental calibration, preventing live use during data acquisition. Here, a ptychography-supervised local inference framework is presented that converts four-dimensional scanning transmission electron microscopy (4D-STEM) into an acquisition-compatible phase-imaging workflow. Physics-constrained reference phase maps reconstructed from a single experimental AuPd dataset serve as teacher labels for a compact model that predicts local phase patches directly from diffraction measurements, without explicit probe input or online iterative optimization. Full-field images are assembled by deterministic overlap stitching. The workflow reaches an online latency of about 0.27 ms per probe position and a throughput of about 20,000 positions per second, an approximately 1,000-fold speed-up over GPU-accelerated ePIE, while preserving atomic-scale lattice contrast and reciprocal-space fidelity. Without fine-tuning, the same model transfers across materials (WS2), defocus conditions (high-entropy alloy nanoparticles), and instruments (hBN at 300 kV). The approach amortizes ptychographic redundancy into a fast, generalizable workflow that enables real-time atomic-scale phase imaging for materials microscopy.
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All authors H. Yue, C.-C. Chen, C.-N. Hsiao, J. Cheng, Y. Liu, X.Z. Liao, Steve F. Shu

Geometric Topology

Every Link Has Infinitely Many Explicit Generalised T-Link Presentations

Thiago de Paiva and Yi Liu

Generalised $T$-links provide a simple description of all links in $S^3$ as closures of products of standard twisting blocks, parametrised by finite sequences of integers. We prove that every link admits infinitely many pairwise distinct generalised $T$-link presentations. Starting from any such presentation, we give explicit parameter transformations that preserve the represented link and generate families of pairwise distinct presentations depending on arbitrarily many independent integer parameters.
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Cryptography and Security

C-to-Rust Fallacy: Automatic Refactoring != Memory Security

Hung-Mao Chen, Xu He, Bo Lu et al. (+2 more)

Rust has emerged as the leading system programming language, offering strong memory and type safety guarantees without compromising performance. This positions it as a compelling alternative to traditional languages like C and C++, which are susceptible to memory security bugs. However, manually transforming C to Rust requires in-depth domain knowledge of the Rust language features, which requires significant effort for developers. To address this, tools for automatic C-to-Rust refactoring aim to generate safe Rust code leveraging static analysis and Large Language Models (LLMs). While these tools claim to achieve safety by reducing the unsafe Rust, the correlation with improving security is not clear. In this paper, we conduct a comprehensive empirical study on the reliability, safety, and correctness of various C-to-Rust refactoring methods. Specifically, we evaluate C2Rust-analyze, CROWN, C2SaferRust, and FLOURINE using a dataset of 116 C programs with memory security bugs from the NIST Juliet Test Suite. Based on 464 Rust programs generated by these tools, our evaluation focuses on three key aspects: the compilation correctness of the refactored programs, the effectiveness in mitigating original C bugs, and the tendency to introduce additional Rust bugs. The results indicate that 342 Rust programs fail to compile, 177 Rust programs inherit memory security bugs from the original C programs, and 77 new Rust bugs are introduced. We examine the rationale behind tool design and analyze the root cause of errors across various refactoring methods. Our findings indicate that current automated refactoring tools deliver memory safety as they define it, but not the broader memory security when adopting them.
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All authors Hung-Mao Chen, Xu He, Bo Lu, Xiaokuan Zhang, Kun Sun

High Energy Physics (Experiment)

Search for the $^{16}\text{O}(ppp) \rightarrow ^{13}\text{C} \pi^+ \pi^+ e^+$ Decay Mode in Super-Kamiokande Using Machine Learning Techniques

The Super-Kamiokande Collaboration: J. Feng, K. Abe, Y. Asaoka et al. (+296 more)

We report a new partial lifetime limit of $4.2 \times 10^{32}$ years for the trinucleon decay mode $^{16}\text{O}(ppp) \rightarrow ^{13}\text{C} \pi^+ \pi^+ e^+$, obtained from a search conducted using the Super-Kamiokande detector with 0.401 megaton-years of exposure across five operational periods (SK-I: 1996–2001, SK-II: 2002–2005, SK-III: 2006–2008, SK-IV: 2008–2018, SK-V: 2019–2020). This represents an improvement of six orders of magnitude over previous experimental constraints. The analysis utilizes a convolutional neural network (CNN) incorporating an attention mechanism—a computational technique that enables the model to focus on the most relevant regions of Cherenkov ring patterns—to enhance event classification, thereby improving the sensitivity of the search. This is the first application of a CNN to a nucleon decay search in Super-Kamiokande. Furthermore, the large dataset available in Super-Kamiokande (hereafter "SK") strengthens the statistical power of the study, enabling a more stringent constraint than those set by prior experiments.
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All authors The Super-Kamiokande Collaboration: J. Feng, K. Abe, Y. Asaoka, M. Harada, Y. Hayato, K. Hiraide, T. H. Hung, K. Ieki, M. Ikeda, J. Kameda, Y. Kataoka, S. Mine, M. Miura, S. Moriyama, K. Nakagiri, M. Nakahata, S. Nakayama, Y. Noguchi, G. Pronost, K. Sato, H. Sekiya, R. Shinoda, M. Shiozawa, Y. Suzuki, A. Takeda, Y. Takemoto, H. Tanaka, S. Chen, Y. Itow, T. Kajita, R. Nishijima, K. Okumura, T. Tashiro, T. Tomiya, X. Wang, F. J. de Garay Arcones, P. Fernandez, L. Labarga, D. Samudio, C. Yanagisawa, B. Jargowsky, E. Kearns, J. Mirabito, L. Wan, T. Wester, B. W. Pointon, J. Bian, B. Cortez, N. J. Griskevich, Y. Jiang, M. B. Smy, H. W. Sobel, V. Takhistov, J. Hill, B. D. Xu, D. Jung, D. H. Moon, R. G. Park, B. S. Yang, K. Scholberg, C. W. Walter, T. P. Leplumey, O. Drapier, A. Ershova, M. Ferey, Z. Hu, E. Le Blévec, Th. A. Mueller, P. Paganini, C. Quach, R. Rogly, T. Nakamura, J. S. Jang, R. P. Litchfield, L. N. Machado, F. J. P. Soler, J. G. Learned, K. Choi, S. Cao, T. V. Ngoc, L. H. V. Anthony, N. W. Prouse, M. Scott, Y. Uchida, V. Berardi, N. F. Calabria, M. G. Catanesi, N. Ospina, E. Radicioni, A. Langella, G. De Rosa, G. T. Burton, G. Collazuol, M. Feltre, M. Mattiazzi, L. Ludovici, M. Gonin, L. Périssé, B. Quilain, H. Tanigawa, Z. Utagawa, M. Fukazawa, S. Horiuchi, A. Kawabata, Y. M. Liu, Y. Maekawa, Y. Nishimura, A. Oka, R. Akutsu, M. Friend, T. Hasegawa, Y. Hino, T. Ishida, T. Kobayashi, T. Matsubara, T. Nakadaira, Y. Oyama, A. Portocarrero Yrey, K. Sakashita, T. Sekiguchi, N. Bhuiyan, F. Di Lodovico, T. Katori, R. Kralik, N. Latham, R. M. Ramsden, V. Siccardi, S. Aoyama, H. Bambara, Y. Inaba, H. Ito, M. Nishigami, T. Sone, A. T. Suzuki, Y. Takeuchi, S. Wada, H. Zhong, L. Feng, S. Han, J. Hikida, N. Fujimoto, M. Kawaue, T. Kikawa, F. Nakanishi, T. Nakaya, R. A. Wendell, U. Limbu, S. J. Jenkins, N. McCauley, M. Fanì, M. J. Wilking, Z. Xie, Y. Fukuda, H. Menjo, Y. Yoshioka, J. Lagoda, J. Zalipska, T. Yano, M. Mori, J. Jiang, Y. Asano, K. Hamaguchi, H. Ishino, Y. Koshio, S. Ohshita, T. Tada, T. Ishizuka, G. Barr, D. Barrow, D. Wark, A. Holin, F. Nova, M. Jo, S. Jung, J. Yoo, L. Kneale, T. Peacock, P. Stowell, H. Okazawa, S. M. Lakshmi, S. Hong, E. Kwon, M. W. Lee, J. W. Seo, I. Yu, Y. Ashida, A. K. Ichikawa, S. Kobayashi, K. D. Nakamura, S. Abe, S. Goto, H. Hayasaki, S. Kodama, Y. Kong, Y. Masaki, Y. Mizuno, T. Muro, Y. Nakajima, W. Cai, Y. Endo, M. Sekiyama, N. Taniuchi, T. Yamazumi, M. Yokoyama, P. de Perio, S. Fujita, C. Jesús-Valls, K. Martens, Ll. Marti, A. D. Santos, K. M. Tsui, M. R. Vagins, M. Kuze, S. Izumiyama, R. Matsumoto, C. Ise, M. Ishitsuka, M. Sugo, M. Wako, K. Yamauchi, Y. Nakano, A. Yankelevich, F. Cormier, R. Gaur, M. Hartz, A. Konaka, X. Li, B. R. Smithers, S. Chen, Y. Wu, A. Q. Zhang, B. Zhang, H. Adhikary, M. Girgus, P. Govindaraj, M. Posiadala-Zezula, Y. S. Prabhu, S. B. Boyd, R. Edwards, D. Hadley, M. O'Flaherty, B. Richards, A. Ali, B. Jamieson, C. Bronner, D. Horiguchi, A. Minamino, Y. Sasaki, K. Hosokawa, Y. Kanemura, S. Miki, K. Shimizu, B. Zaldivar, M. C. Jang, S. H. Lee, B. Bodur, A. Beauchêne, N. Iovine, D. Martin, M. Kobayashi, R. Okazaki, M. Jakkapu, K. Nakamura, T. Tsukamoto, J. Gao, J. Migenda, S. Zsoldos, Y. Takagi, J. R. Hu, K. Yasutome, A. Tarrant, M. Mandal, M. Jia, C. K. Jung, W. Shi, S. Sakai, T. Tano, L. Cook, S. Samani, J. Y. Yang, J. E. P. Fannon, M. Malek, J. M. McElwee, M. D. Thiesse, L. F. Thompson, S. T. Wilson, S. Tairafune, A. Eguchi, E. Watanabe, J. Xia, K. Terada, R. Asaka, M. Shinoki, T. Yoshida, V. Gousy-Leblanc, M. Nicholson, S. Amanai, R. Shibayama, R. Shimamura

Quantum Physics

$\mathsf{BQP} \subseteq \mathsf{IP}$ Does Not Relativize

Adam Bouland, Andrew Huang, Anand Natarajan et al. (+2 more)

We construct an oracle relative to which $\mathsf{BQP} \not\subseteq \mathsf{IP}$, resolving a long-standing open question in quantum complexity theory. Together with recent work due to Aaronson et al., our work also gives the first oracle separation between $\mathsf{IP}$ and $\mathsf{MIP}$, answering a question dating back to Fortnow's thesis. Our separation is based on the Forrelation problem, where given Boolean functions $f$ and $g$, the goal is to determine if $f$ is correlated with the Fourier spectrum of $g$. While this task is solvable by a query-efficient quantum algorithm, we show that it admits no classical interactive protocol with polynomial communication and a polynomial-query verifier. Our proof is based on (i) a new structural result showing how to approximate Avg-Max circuits (which are well-known to capture the power of interactive proofs in the oracular setting) by convex functions with small first and second derivatives and (ii) a novel analysis establishing that the Forrelation distribution suggested by Aaronson and Ambainis fools such functions. Our results imply that any prover-efficient classical interactive protocol for $\mathsf{BQP}$ must rely on non-relativizing techniques. This might serve as a partial explanation for the lack of progress towards doubly-efficient, unconditionally sound classical verification of quantum computation.
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All authors Adam Bouland, Andrew Huang, Anand Natarajan, Itay Shalit, Avishay Tal

Data Structures and Algorithms

Linear-Query Deterministic Approximation for Non-monotone Submodular Maximization under a Knapsack Constraint

Zihui Liu and Zhijie Zhang

Submodular maximization under a knapsack constraint (SMK) is a fundamental combinatorial optimization problem with broad applications across machine learning and data mining. Motivated by large-scale applications where query efficiency is paramount, we study non-monotone SMK and focus on deterministic algorithms with linear query complexity. Prior deterministic linear-query algorithms achieve at best a $1/5-\varepsilon$ approximation, falling short of the $1/4-\varepsilon$ ratio attainable by randomized algorithms. We close this gap by presenting a deterministic $(1/4-\varepsilon)$-approximation with $O(n\log^2(1/\varepsilon)/\varepsilon^2)$ queries. Our approach partitions the analysis based on the cost of the largest optimal element $r$: when the cost of $r$ is moderate, we refine the threshold-twin-greedy framework via residual-budget enumeration to tighten the analysis; when the cost of $r$ is large, we reduce the problem to bicriteria submodular maximization. As a secondary contribution, we obtain a $(1/2-\varepsilon, O(1/\varepsilon))$-bicriteria approximation with $O(n\log(1/\varepsilon)/\varepsilon^2)$ queries, improving over the previous $O(n^2/\varepsilon)$ query bound.
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Artificial Intelligence

Toolcompass: Guiding Tool Trialing, Not Suppressing It

Junlin Fang, Chong Zhang, Do Nguyen-Thanh et al. (+3 more)

Large language model (LLM) agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e., excessive trials waste the interaction budget, whereas selective trials enable exploration of unfamiliar tools. Existing outcome-based post-training leaves wasteful trials unguided, while turn-level supervision may suppress necessary exploration. We introduce ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions. Specifically, ToolCompass models each function class as a von Mises–Fisher distribution and jointly reduces intra-function variation across domains and increases inter-function separation. This structure transfers experience from seen tools to functionally similar unseen tools, directing exploration away from unrelated alternatives. ToolCompass requires no ground-truth call traces or unseen-tool access and incurs no inference overhead. Experiments on AppWorld and FTRL show consistent gains across GRPO, RFT, and DMPO. improves AppWorld OOD task success by up to 10.71 percentage points over vanilla post-training and performs best among competitive baselines on both benchmarks.
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All authors Junlin Fang, Chong Zhang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du

Artificial Intelligence

Seeing Is Not Perceiving: When Synthetic Consumers Can and Cannot Pretest Visual Marketing

Yi-Lin Tsai and Yung-Hsiu Lai

Marketers now deploy generative AI agents as synthetic consumers to pretest visual assets such as logos, packaging, and advertising at a fraction of human-panel cost. However, this procedure assumes that a model seeing a visual cue can also perceive its consumer meaning, which is largely untested. We stress-test the assumption using six canonical visual marketing experiments, varying the two levers managers control: model generation (GPT-4o-mini vs. GPT-5.4-mini) and input format (plain text vs. JSON). Every resulting configuration passed the manipulation checks; however, none of the configurations reproduced more than two of the six human effects, and the remainder were nonsignificant. The one exception was a significant reversal of the human pattern. Providing conceptual or empirical evidence through in-context learning steers average responses toward the human effect. Yet steering has a limit: even when it succeeds, a configuration reproduces less than half of the natural spread of human responses and so understates consumer heterogeneity. We integrate these results into an AI governance protocol (Calibrate, Intervene, Deploy) that delineates when synthetic consumers can responsibly screen creatives and when human panels remain necessary.
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Differential Geometry

Hamiltonian-Type Spectral Functionals for Time-Dependent Perturbed Hodge–de Rham Operators

Sining Wei and Yong Wang

Let $g_t$ be a smooth family of Riemannian metrics on an oriented manifold of even dimension $n\geq4$, and let $D_t=d+\delta_{g_t}+\Psi_t$ on the fixed exterior bundle, with $\Psi_t$ self-adjoint. We evaluate a scalar-weighted residue functional obtained from the operator expression in Hawkins' Hamiltonian action using the coefficient derivative $\partial_tD_t$. The velocity contribution depends only on $\partial_tg_t$. A direct symbol calculation reduces the lapse correction to a divergence and a Dirichlet term; for a nonconstant test function, integration by parts retains a mixed gradient term. For compact manifolds with a warped collar, we specify compatible factorizations of the weighted KKW term and of the commutator correction. Their boundary residues are computed explicitly. The collar-derivative and perturbation contributions cancel in the weighted KKW factorization, and the remaining boundary terms involve normal derivatives of the scalar weights. The closed formula is recovered when the boundary is empty.
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Machine Learning

Marginal Log-Likelihood Increments under Dirichlet-Smoothed Markov Estimation

Levin David Schwab

For a Dirichlet-smoothed transition model, the effect of adding one workflow trace to the training archive is an exact change in reference-weighted log likelihood. We derive that change and show that it is a weighted reduction of Kullback–Leibler divergence between the reference conditionals and the model. From this form we obtain an upper bound on the gain available to any acquisition, which expresses a millinat difference as a share of what is attainable, an exact covariance identity for the effect of the reference weighting, and a sign criterion for the interaction between two candidates, from which the batch objective is neither submodular nor supermodular. A case study on the BPI Challenge 2012 loan-application log measures all three and finds a positive selection result in one of the four combinations of reference weighting and budget unit. There, of two regressors fitted to identical descriptors and identical labels, the one that predicts individual increments far more accurately, median $R^2$ 0.87 against 0.62, realizes the smaller share of the attainable gain, 61 against 69 per cent, so ranking accuracy for individual traces is neither necessary nor sufficient for batch quality.
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Robotics

Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform

Yuanzhe Dong, Jie Cao and Shuman Wang

Lower cost open source robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contemporary robotics. However, translating a complete research workflow, spanning mechanical assembly, electrical setup, simulation, policy learning, system identification, and physical deployment, into a coherent course for novice learners remains challenging. We present an integrated robotics course framework that organizes these activities around a shared robotic artifact. The framework combines parallel disciplinary tracks, sequencing based on technical dependencies, progressive integration of simulation and hardware, layered performance checkpoints, and structures for balancing collaborative work with individual accountability. We illustrate the framework through a high school curriculum organized around a robot project in which pairs of students assemble an open source humanoid robot, train a walking policy in simulation, and deploy it on the physical platform. The framework was developed through an iterative design process that included formative review by five experts in robotics research, engineering, secondary STEM education, and curriculum design. Expert feedback highlighted three central design tensions: authenticity versus cognitive load, system integration versus timely visible progress, and team construction versus individual accountability. These tensions informed the final framework presented in this paper. This work offers a structured approach for adapting robotics research workflows into interdisciplinary precollege courses; future classroom studies are needed to examine implementation and student learning.
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Quantum Physics

Post-selected enhancement of phase sensitivity in a two-path interferometer

Ryuya Fukuda, Katsuki Tanaka, Masataka Iinuma et al. (+1 more)

Post-selected metrology can exceed the standard quantum limit of single photon interference by concentrating all of the available Fisher information in a subset of post-selected outcomes. Here we apply this principle to a standard two-path interferometer, activating the Fisher information concentrated in the low probability output port by transferring the phase sensitivity to the circular polarizations of the photon. We obtain a phase gradient of the post-selected interference fringe that corresponds to that of a four-photon NOON state, providing a direct comparison of the performance of multi-photon interference with single-photon post-selection.
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All authors Ryuya Fukuda, Katsuki Tanaka, Masataka Iinuma, Holger F. Hofmann

High Energy Astrophysical Phenomena

A Multi-Mission Detection Framework for High-Energy Transients: Application to a 13-year Fermi/GBM and Swift/BAT Sample

Ariel Perera, Barak Zackay and Tejaswi Venumadhav

The detection of faint high-energy transients is becoming increasingly important in time-domain and multi-messenger astronomy, particularly for identifying electromagnetic counterparts to binary neutron star mergers, high-energy neutrino events, and other weak transient phenomena. At present, the sensitivity of gamma-ray and hard X-ray searches is often limited by the detection threshold of individual instruments, leaving a population of faint transients inaccessible to single-mission analyses. To address this limitation, we develop a joint detection framework that combines trigger information from multiple detectors and assigns each candidate a joint association probability, $p_{\text{joint}}$, as a function of the individual detector signal-to-noise ratios. We apply this framework to archival Fermi/GBM and Swift/BAT rates data from 2013-2025. The method effectively separates coincident signals from accidental background associations, suppressing inconsistent coincidences while enhancing the significance of faint events observed by both instruments. We find that, under optimal conditions, a two-mission search can increase the accessible volume for short gamma-ray bursts by up to $60\%$. This framework provides a practical foundation for generalized multi-mission searches, enabling more sensitive exploration of the faint high-energy transient sky and improving the prospects for future multi-messenger discoveries.
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Combinatorics

A colorful quantitative Helly theorem for volume

Grigory Ivanov

We prove a colorful quantitative Helly theorem for volume with the optimal number $2d$ of colors. If every rainbow intersection from $2d$ finite families of convex sets in $\R^d$ has volume at least one, then the intersection of one family has volume at least $d^{-O(d^2)}$. We also prove a colorful quantitative Steinitz theorem for origin-centered ellipsoids of different shapes. The proof uses a common normalization of positive operators and a lift that produces two rainbow bases with large determinants.
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Fluid Dynamics

A unified multirate lattice Boltzmann framework for thermosolutal dendritic solidification

Yang Liu, Xiaomin Wu and Chengjie Zhan

Thermosolutal dendritic solidification involves interface evolution, solute diffusion, heat transfer, and melt flow over markedly different time scales. In lattice Boltzmann simulations, a single numerical time interval may place different transport processes in unfavorable relaxation ranges, while asynchronous updates require consistent transfer of phase-change contributions. To address these issues, a unified multirate multiple-relaxation-time lattice Boltzmann method is developed for thermal, solutal, and thermosolutal dendritic solidification. The coupled fields share a common moment-space framework but evolve at different update rates. The concentration and temperature source terms are separated into transport-related and phase-change contributions, and each resolved phase increment is used to immediately transfer the corresponding solutal and latent-heat contributions. The method reproduces characteristic dendritic morphologies and tip-velocity trends under pure diffusion and forced convection, with weak sensitivity to the tested update factors. Directional solidification over Lewis numbers \(Le=1\)–\(1000\) captures the transition from nearly planar to cellular and strongly branched growth, including vertically aligned dendrites and solute-rich interdendritic channels for saline water, in qualitative agreement with experiments. Source-coupling ablation shows that delayed coarse-step transfer produces increasingly strong local source pulses and eventual loss of numerical stability as time-scale separation increases, whereas phase-step instantaneous transfer remains stable over the tested conditions. These results demonstrate the applicability of the proposed framework to dendritic solidification with strongly separated transport time scales. The source code is publicly available in the DendriteLBM repository.
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