Lattice Field Theory
Counterdiabatic quantum optimization for efficient state preparation in the Schwinger model
Ethan Laval, Bipasha Chakraborty and Stefano Cipolla
The
Quantum Approximate Optimization
Algorithm (QAOA), whose design is underpinned by the adiabatic
theorem, is one of the leading variational
quantum algorithms for preparing the ground states of gauge theories. A recently proposed variant, DC-QAOA, incorporates counterdiabatic driving to accelerate the adiabatic process, reducing both
circuit depth and runtime. In this work, we present a systematic analysis, from both theoretical and computational perspectives, of DC-QAOA and related counterdiabatic variants for preparing the ground state of the Schwinger model, the paradigmatic (1+1)-dimensional
lattice gauge theory. Benchmarking these methods against standard QAOA, we show that counterdiabatic driving improves ground-state preparation while lowering
circuit depth requirements. Our results establish counterdiabatic protocols as a practical route towards efficient
quantum simulation of gauge theories.
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Machine Learning
Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition
Philip H. Lee, Shreeram Suresh Chandra and John H.L. Hansen
Multimodal emotion recognition (MER) increasingly pairs
EEG with
speech, treating internal neural signals and external vocal expression as informative views of affect. In practice, naive fusion underperforms the stronger single modality, because
EEG artifacts inject noise that corrupts the shared representation. We introduce EmoSpeechBrain, a
multimodal framework built on the insight that noise suppression is a precondition for effective fusion. Its
EEG encoder uses differential
attention, taking the difference between two
attention maps to cancel shared noise and isolate discriminative neural activity. An
attention-based gating adapter aligns both modalities in a shared space and weights each one's contribution to the prediction. On two
datasets - PME4 and EAV, EmoSpeechBrain improves MER accuracy by up to 12.9% over other state-of-the-art (SOTA)
EEG encoders, and surpasses unimodal
speech and
EEG baselines by up to 13.1% and 23.1%. These results show that once
EEG noise is suppressed, fusion delivers gains that naive combination cannot.
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Cryptography and Security
Short Paper: Prefix Count Limits Can Increase First-Hit Discovery in Card Reissuance
Wasif Faisal and Suprava Saha Dibya
When a card number is compromised, an attacker may search for active numbers sharing its prefix. An issuer might respond by reissuing cards from heavily populated prefixes into less populated ones. We show that this intuitive count control can backfire. For fixed search regions, exposure weights, and total activity, we derive exactly when reducing the maximum prefix count increases the chance that a budgeted search finds an active number. In matched synthetic simulations over 50,000 candidates, targeted replacement meets the count limit in every run but raises supplied-12-digit-prefix discovery relative to equal-volume random replacement in three of twelve settings. Thus a lower prefix count does not by itself certify lower enumeration risk.
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Networking
Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models
Andrea Masini, Sudipta Acharya, Paolo Bellavista et al. (+2 more)
Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop
language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic control (tc) configurations. The framework integrates an Active Queue Management (AQM)-based digital twin (DT) semantic model, automated metadata extraction, critique-driven refinement, and
Retrieval-Augmented Generation (RAG)-based knowledge reuse to improve semantic consistency and configuration reliability. We evaluate multiple open-source large
language models (LLMs) and small
language models (SLMs), together with Claude Sonnet-4.6, on 100 Request for Comments (RFC) 9315-compliant traffic-shaping intents. Across both translation stages, Intent2Tc achieves high semantic fidelity, configuration accuracy, and deployment readiness, with Claude Sonnet-4.6 reaching 0.98 semantic similarity, 1.0 semantic unit coverage, and 0.045 normalized edit distance. Furthermore, RAG reduces
token consumption and inference latency while enabling compact models such as Phi-4-mini to approach the performance of substantially larger models. Linux tc serves as the target configuration platform, demonstrating the practical applicability of the proposed framework.
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All authors Andrea Masini, Sudipta Acharya, Paolo Bellavista, Luca Foschini, Burak Kantarci
Robotics
Augmented Reality Interfaces for Human-Robot Collaboration: Development of a ROS 2-Based Sensor Streaming Framework and Validation via SLAM Algorithms
Alessandro Rubert, Stefano Ghidoni and Matteo Terreran
In recent years, Human-
Robot Collaboration (HRC) has taken on a central role in Industry 4.0 and collaborative robotics, demanding communication channels that are increasingly bidirectional, intuitive, and efficient. In this context, Augmented Reality (AR) presents itself as a fundamental enabling technology, capable of both displaying information to the operator and gathering spatial data about the surrounding environment. This thesis presents the development of a sensor streaming framework that connects the Magic Leap 2 AR headset with the ROS 2 (
Robot Operating System) ecosystem. Using the Unity development environment and the ROSTCP-Connector package, an on-board application for the headset was developed, capable of acquiring real-time data from the integrated sensors (pose tracking, cameras, and environmental sensors) and publishing it to dedicated ROS 2 topics. In order to test the accuracy, latency, and robustness of the generated data stream, the framework was validated using SLAM (Simultaneous Localization and Mapping)
algorithms known in the literature. The experimental results demonstrate that the proposed architecture ensures stable data transmission, laying the groundwork for safe real-time interaction and shared spatial awareness, and opening up new perspectives for the control and supervision of robotic systems in complex HRC scenarios.
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Operating Systems
ActKV: Efficient LLM Agents through Action-Guided KV Cache Management
Zihan Wang, Cheng Tang, Lei Gong et al. (+4 more)
Agentic LLM inference accumulates long KV caches across iterative observation-
reasoning-action loops, imposing substantial
memory overhead and limiting serving throughput. Existing compression methods emphasize overall output quality, overlooking the asymmetric importance of actions in driving task progress. Our key idea is to establish a compression criterion that values KV entries by their contribution to action generation and prioritizes action quality. However, iterative execution, dynamic
memory demands, and scattered action-critical entries pose challenges to eviction policies, budget allocation, and paged
memory integration. To this end, we propose ActKV, the first KV cache compression framework tailored for agentic LLM inference. (i) Action-oriented KV cache eviction exploits stable action access patterns to retain entries critical to future actions, supporting reliable task progress under compression. (ii) Confidence-driven adaptive budget allocation uses LLM's intrinsic confidence to adapt the budget to evolving action-critical
memory demands. (iii) Page-aware compression management standardizes compression into three primitives with customized kernels, realizing practical throughput gains. On long-trace tasks, ActKV retains an average of 98.53% of FullKV's accuracy with only 25.98% of its peak KV cache
memory. It also achieves 3.97 times and 3.58 times FullKV's
token and task throughput, delivering state-of-the-art performance.
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All authors Zihan Wang, Cheng Tang, Lei Gong, Chao Wang, Wenqi Lou, Teng Wang, Xuehai Zhou
Robotics
InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
Xingyu Miao, Zizun Li, Baole Fang et al. (+45 more)
World Action Models (WAMs) jointly model visual dynamics and action generation for generalist
robot manipulation. A central challenge is to integrate priors from large-scale pretrained models—including visual dynamics, scene semantics, geometry, and motion—into a unified framework for
robot action generation. We introduce InternW0-$\Delta$, a unified WAM pretrained on a heterogeneous corpus that outperforms prior methods across simulation
benchmarks and real-
robot platforms. InternW0-$\Delta$ combines pretrained visual dynamics, scene-level semantics, 4D
geometric and motion priors, and action generation within a Mixture-of-
Transformers (MoT) framework. A pretrained
video expert and an action expert interact under semantic guidance from a frozen VLM, while a pretrained 4D foundation model injects
geometric and motion priors through training-only
distillation. We further introduce Causal Imprint, which learns future-relevant scene changes from training-only future supervision and provides predictive representations directly to the action expert without future-
video rollout at inference. For large-scale joint training, we construct a heterogeneous corpus of
robot demonstrations, UMI data,
egocentric human demonstrations, and Ego2
Robot data, curated and aligned under a common state-action representation. The resulting corpus contains over 20K hours of processed training data, to our knowledge the largest open-source corpus of its kind. We pretrain InternW0-$\Delta$ on this corpus and demonstrate strong performance across simulation
benchmarks and real-
robot platforms. We will open source the training code, model weights, infrastructure, data-processing pipeline, and processed data where licenses permit. Project page: https://internrobotics.github.io/InternW0-Delta/
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All authors Xingyu Miao, Zizun Li, Baole Fang, Kaiwen Song, Tenghui Wang, Hanxue Zhang, Yating Wang, Xudong Li, Yuping He, Xueyuan Wei, Chao Gao, Xijie Yang, Yingxiang Xu, Kerui Ren, Wenqi Guo, Jianjun Zhou, Xinzhe Wang, Weiguang Zhao, Ni Yang, Zetao Cai, Yufei Xue, Hengjie Li, Zeyu He, Yuanzhen Zhou, Rong Fu, Jianyang Zhang, Siwei Cui, Fuxian Huang, Yunsong Zhou, Xing Gao, Yifei Yao, Qiaojun Yu, Kailin Li, Ming Zhou, Mu Huang, Xinyue Li, Wenze Cui, Bingqi Jiang, Xueyue Zhu, Junting Dong, Haoyu Guo, Tao Lu, Mulin Yu, Bowen Zhou, Bin Zhao, Tianfan Xue, Weinan Zhang, Chunhua Shen
Analysis of PDEs
Hamilton's gradient estimates and Liouville theorems for $u_{t}=\Delta u^{m}+au\log u+bu$ on Riemannian manifolds
Jun Sun and Jiaming Yang
In this paper, we apply Nash-Moser iteration and Saloff-Coste's Sobolev inequalities to derive gradient estimates for positive solutions to a class of nonlinear
parabolic equations of the form \begin{equation*} u_{t}=\Delta u^{m}+a(x, t)u\log u+b(x, t)u, \quad 1-\frac{2}{n}<m<1 \quad \text{or}\quad 1<m<1+\frac{1}{1+\sqrt{2n}}, \end{equation*} on a complete Riemannian
manifold $(M,g)$ of dimension $n$, where $a(x, t)$, $b(x, t)$ are $C^{1}$ functions. For $a=b=0$, this equation becomes the porous medium equation (PME) or the fast
diffusion equation (FDE) when $m>1$ or $m<1$, respectively. As consequences, we obtain Liouville type
theorems for the corresponding constant coefficient equations when \begin{equation*} 1-\frac{1}{\sqrt{n-1}}<m<1\quad \text{and}\quad 1<m<1+\frac{1}{\sqrt{n-1}}. \end{equation*}
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Human-Computer Interaction
PANEL: An Open-Source, Self-Hosted Web Platform for Human Evaluation of Generative Models
Matteo Spanio, Andrea Poltronieri and Martín Rocamora
Human judgement is the reference measure for evaluating generative models, yet the software used to collect it lags behing the methodology. Researchers adapt listening-test frameworks designed for perceptual protocols such as MUSHRA, rely on closed commercial survey platforms, or implement single-use web applications. Live arenas such as Chatbot Arena and Music Arena rank publicly deployed systems at scale, but do not support controlled comparisons of a laboratory's own models with its own participants. We present PANEL, an open-source, self-hosted platform for such studies. A study is authored in the browser and distributed as a single link, with audio,
video, image, and text stimuli, seven question types, and screening and skip logic. The platform reports per-question summaries, across-condition significance tests, pairwise win rates and Bradley–Terry scores, and supports power analysis from pilot data. Consent versioning, self-service withdrawal, retention enforcement, and audit logging support GDPR-compliant operation. Each study exports as a machine-readable specification. PANEL is available at https://github.com/matteospanio/panel.
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Quantum Physics
Shaping of the finite-size chaos crossover in Floquet circuits by coherent-mismatch architecture
Xiangjun Tan, Wenqi Liu, Hanjie Zhu et al. (+2 more)
We study how the spatial profile of a deterministic coherent gate perturbation affects finite-size quasienergy statistics and operator dynamics in U(1)-symmetric XXZ Floquet
circuits. Using a shared homogeneous reference, we compare a single modified bond with an alternating perturbation applied across the whole chain. After separating
symmetry sectors and checking several known integrable forms, we find that the alternating
circuits move toward circular orthogonal ensemble level statistics and change operator-space
entanglement and out-of-time-order correlations at smaller local amplitudes than the single defects. For intermediate numbers of modified bonds, the response is nonmonotonic and also depends on where the bonds are placed, so bond density alone is not enough. A perturbative analysis identifies the additive weight of many modified bonds, while coherent spatial interference reshapes the coupling between nearby eigenstates.
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All authors Xiangjun Tan, Wenqi Liu, Hanjie Zhu, Wenkai Bai, Zhanning Wang
Computational Engineering
AlphaOpsBench: Benchmarking End-to-End Alpha Strategy Operationalization in Prediction Markets
Huaiyu Jia, Mingxuan Zhao, Jincheng Gao et al. (+4 more)
Large
language models increasingly generate quantitative trading strategies, yet existing
benchmarks assume standardized assets, numerical features, or directly compilable strategy representations—assumptions that prediction-
market strategies violate, since a coarse idea may leave the traded outcome, causal information source, signal definition, threshold, sizing, order policy, exit, and settlement behavior unspecified. We introduce AlphaOpsBench, which evaluates end-to-end operationalization from source-grounded economic hypotheses to auditable executable programs over 581 source-preserving strategy records and a lifecycle-scale Polymarket
dataset with 1.28 million binary
markets, 183.6 million cleaned executions, settlement evidence, and limit-order-book history, comparing Direct generation against a Staged design-then-code protocol. In a corrected independent-generation study over 36 controlled tasks and 24 preregistered real strategies, strict end-to-end validity remains rare: Direct and Staged obtain 35/180 and 20/180 canonical passes on the controlled cohort and no confirmed pass on the real cohort, and repeated generations vary substantially in model-owned economic choices. By contrast, 775,725 of 783,655 scheduled historical replays complete, showing that replayability is a far weaker property than source-faithful operationalization. Financial outcomes depend on the declared execution model and available historical evidence, and fee and liquidity experiments show that execution costs alter subsequent trading paths rather than acting only as ex-post deductions. AlphaOpsBench thus separates strategy fidelity, behavioral validity, historical executability, and financial performance in an evidence-aware
benchmark for LLM-based quantitative research in prediction
markets.
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All authors Huaiyu Jia, Mingxuan Zhao, Jincheng Gao, Zifan Peng, Wentao Zhang, Siguang Li, Shuo Sun
Metric Geometry
Unbalancing unit vectors
Zilin Jiang, Jeck Lim and Skand Parvatikar
We show that for every $n$ unit vectors $v_1, \dots, v_n$ in the $d$-dimensional Euclidean space, there exist signs $\varepsilon_1, \dots, \varepsilon_n \in \{\pm 1\}$ such that $\lVert \varepsilon_1 v_1 + \dots + \varepsilon_n v_n \rVert \ge \sqrt{2n - d}$, and we characterize the equality cases.
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Distributed Computing
Adaptive Switching Between Leader-Based and Leaderless BFT Protocols
Sudip Bhujel, Yue Li, Ning Zhang et al. (+3 more)
Byzantine fault-tolerant (BFT) protocols are known for providing operational consistency and resilience in distributed systems. However, evolving network conditions, often driven by the network's inherent dynamism or
adversarial influence, make it suboptimal to rely on a static protocol at all times. Existing BFT protocol adaptation solutions switch only among leader-based protocols and coordinate each switch through a separate consensus round, leaving them ineffective at handling severe asynchrony or situations in which an adaptive adversary targets the network's leader. We propose BFTide, a protocol adaptation architecture that enables a BFT system to intelligently and swiftly switch to a suitable protocol as network conditions shift. BFTide integrates a novel protocol switching layer that embeds protocol transition logic into the ongoing BFT operation, enabling safe and low-overhead transitions between partially synchronous leader-based protocols and asynchronous leaderless protocols. It further incorporates an offline-trained reinforcement learning policy that allows nodes to propose protocols at runtime based on observed system metrics. Experimental results show that BFTide reduces transaction latency under adverse network conditions compared with static BFT protocols and the state-of-the-art BFT protocol adaptation scheme BFTBrain (NSDI'25), while maintaining comparable throughput. The switching layer adds a modest 10-21% overhead to median latency when idle and requires no separate consensus round per switch.
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All authors Sudip Bhujel, Yue Li, Ning Zhang, Y. Thomas Hou, Wenjing Lou, Yang Xiao
Group Theory
On the order of a finite group with trivial centre and bounded conjugacy classes
Ilya Gorshkov
A group is called an $n$-$\bfc$-group if each of its conjugacy classes is finite and contains at most $n$ elements. By a
theorem of B.~H.~Neumann the derived subgroup of such a group is finite of $n$-bounded order, whereas the order of the group itself need not be bounded, as extraspecial $p$-groups show. We prove that an $n$-$\bfc$-group with trivial centre is finite of order at most $n^{64(\log n)^{5}}$. Passing to the quotient by the hypercentre, we deduce that no assumption on the structure of the group is needed at all: for an arbitrary finite $n$-$\bfc$-group $G$ one has $|G:Z_\infty(G)|<n^{64(\log n)^{5}}$. The finiteness of the group is required here only for the correct definition of the hypercentre.
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Robotics
Modeling and Generative-AI-Based Design of Load-Adaptive Gravity Balancing Mechanisms
Ryotaro Kayawake, Kazuki Abe, Shota Miyake et al. (+2 more)
Load-adaptive
gravity balancing mechanisms (LA-GBMs) can accommodate various loading conditions by passively changing their characteristics in response to payload variations. However, their design is difficult because both the desired mechanism motion and static
equilibrium under variable payloads must be satisfied simultaneously. This study proposes a general design methodology for LA-GBMs that does not depend on specific mechanism architectures or mechanical elements. The necessary conditions for the potential fields of LA-GBMs are formulated, and two general forms are derived: an affine form representing the effect of payload mass and a factorized form representing state transitions associated with load adaptation and
gravity balancing. These forms are then provided to generative AI as design requirements to generate candidate potential functions. The generated functions are analytically verified in terms of their conformity to the two general forms and the conditions required for valid LA-GBMs. Furthermore, the obtained potential functions are decomposed into individual terms, and an example of a method for constructing an LA-GBM by combining springs, counterweights, and function-generating linkage mechanisms is presented. By using potential functions as an intermediate representation, the proposed framework enables the generation of LA-GBM design candidates without prescribing a mechanism architecture in advance. Mechanical realizability and manufacturability of the generated potential fields remain important issues for future work.
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All authors Ryotaro Kayawake, Kazuki Abe, Shota Miyake, Masahiro Watanabe, Kenjiro Tadakuma
Complex Variables
A Criterion for the Sphericity of a CR Manifold
M. A. Stepanova
We formulate and prove a simple criterion for the sphericity of a CR
manifold (that is, for its equivalence to its model surface). We discuss the differences between the general case, in which the model surface is weighted homogeneous of arbitrary degree, and the case of Levi-nondegenerate
manifolds, whose model surface is quadratic.
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Chemical Physics
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode et al. (+1 more)
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem. Multiscale simulations introduce an additional challenge: robust uncertainty quantification across scales. Even within one scale, computations are often multistage, producing a sequence of target quantities, each dependent on the previous, and each with some uncertainty. Conformal methods have emerged as a model agnostic framework for recalibrating surrogate predictions to produce sets which contain the truth at a user-specified rate. For multistage workflows, we require uncertainty calibration for multiple chemical properties and atomistic configurations, and we want to propagate uncertainty sets to downstream quantities of interest. Such propagation should capture the error cancellations which occur in many downstream targets in materials science; for instance, an approximate energy difference is often more accurate than individual energy predictions. We present the first exploration of multivariate conformal methods for chemical properties, including Bonferroni-corrected hyperrectangles, hyperellipsoidal sets based on the Mahalanobis distance, and custom loss functions within conformal risk control. Calibration is applied directly to predicted energies, atomic forces, and virial stresses, then propagated to elastic constants and vacancy formation energies employing a variety of commonly considered approximate protocols in materials modeling. We highlight the benefits of building correlation predictions into the conformal procedure, making it possible to build sets which capture near
symmetries and error cancellation. We conclude with a discussion of the interplay of the employed approximate computational protocol and conformal guarantees.
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All authors Katharine Fisher, Michael Herbst, James Kermode, Youssef Marzouk
Robotics
Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski et al. (+2 more)
End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamental mismatch between training and execution. Beyond the commonly studied effects of
covariate shift and causal confusion, we identify a complementary factor for this open-loop/closed-loop gap: waypoint-based supervision and displacement metrics do not ensure that the intermediate trajectory is physically coherent or easy for the controller to track. We observe that these inconsistencies concentrate primarily at intermediate waypoints, while the predicted endpoint remains comparatively reliable. Based on this observation, we introduce Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that anchors the trajectory to the vehicle's executed history, preserves the policy's predicted endpoint, and reshapes the intermediate waypoints to improve feasibility. ECO requires no map, privileged simulator state, or additional training, and can be inserted between a broad range of waypoint-emitting policies and their controllers. Across two closed-loop simulators, it improves the aggregate closed-loop score of all six evaluated generative and regression-based driving policies, and the gains tend to increase with how often the base plans violate motion limits. On HUGSIM, ECO improves VaVAM from 18.1 to 31.0 HD-Score (+71%), achieving 1st place on the HUGSIM Closed-Loop Driving Challenge. Similarly, on AlpaSim, ECO increases the scene scores of VaVAM and DiffusionDrive by 123% and 22%, respectively. These results show that for a broad collection of end-to-end driving models, repairing the intermediate geometry of predicted trajectories without changing the policy's predicted endpoint can substantially improve closed-loop performance.
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All authors Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta
Computation and Language
Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding
Zhaoyuan Xia, Qinghongbing Xie, Yung Xiang Hue et al. (+7 more)
Long-context understanding requires large
language models (LLMs) to reason over lengthy documents, conversations, and code, yet task-relevant evidence is often sparse and scattered amid substantial irrelevant and redundant content. We propose Highlight-Then-Summarize (H2S), a compress-then-reason paradigm that first identifies source-grounded, question-relevant evidence and then integrates it into a compact, question-conditioned summary before producing the final answer. To train this behavior, we construct H2S-
Dataset, comprising 6,647 examples from 11
benchmark families with an average context length of 43.9K
tokens, and introduce H2S-RL, which provides process-level rewards for evidence selection and summary construction in addition to final-answer correctness. We evaluate on H2S-Bench, a seven-task long-context suite. Under a shared 128K input and 4K output budget, H2S-14B achieves an average score of 32.60, outperforming Qwen3.8-27B by 10.17 points and obtaining the strongest overall result among the evaluated open-source models. H2S-14B also achieves the highest Evidence-Summary Quality score and retains 97.1% of its 16K-budget performance with only a 4K output budget. These results show that explicitly selecting and integrating evidence improves long-context
reasoning while enabling more compact generation.
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All authors Zhaoyuan Xia, Qinghongbing Xie, Yung Xiang Hue, Jianguang Jiang, Gaofeng Lu, Zhenyu Jiao, Xing Yuan, Dai Dai, Tong Mo, Long Zeng
Artificial Intelligence
Evaluating Budgeted Context Projection with Unexecuted Companion Runs
Guangzhe Zhang
Context projection can shorten individual requests while changing whether an
agent finishes within its budget. We examine how sequential evaluation obscures this trade-off when a capped first continuation prevents its companion from running. In a recorded ReVerPi source-reading campaign, 15 pairs with two final answers yield 12 historically scored successes per arm. Retaining all 27 intervention boundaries distinguishes observed failures from ten unexecuted companions and bounds projected-minus-full success between $-9$ and $+1$ tasks. Under the archived scoring contract, a frozen projection selector has a success difference from full context of $[-3,0]$; outside four fitting tasks, it is $[-4,-1]$ across 23 boundaries. Excluding one task whose platform premise is not established by retained actor-input evidence changes the nonfitting range to $[-3,0]$ across 22 boundaries, so strict inferiority is not robust to that exclusion. Among eleven historically joint-success pairs, projection uses 25% fewer aggregate logical
tokens but more
tokens for the median pair and 55 rather than 35 suffix requests. These retrospective results concern one adaptively assembled campaign, not population performance. The case motivates accounting that retains every boundary, preserves unknown outcomes and policy dependencies, checks task premises, and separates bounded completion from success-conditioned resource use.
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Optimization and Control
Optimization with Region-Reduced ReLU Neural Networks
Christoph Plate, Caroline Ganzer, Mirko Hahn et al. (+5 more)
Optimization of mathematical models involving integer decisions and neural networks with ReLU activation (ReLU ANNs) is a challenging task. Nevertheless, such models are an enabling technology in many application domains. A prominent example is superstructure optimization in chemical engineering, where ReLU ANNs are frequently employed as surrogate models for complex nonlinear processes. We survey recent developments in this area. We argue that in addition to network size and training options of the ANNs, the ReLU activation geometry and the number of linear regions on the domain of interest have a strong impact on computational optimization performance. While standard model compression approaches such as structured
pruning reduce network size, they do not explicitly address
geometric considerations. Therefore, we propose a novel region-reduced model compression approach that combines the stabilization of unstable
neurons and the merging of redundant
neurons to reduce the number of linear regions while maintaining predictive accuracy through error compensation. We evaluate our method against standard compression approaches on multiple optimization use cases. First, the two-dimensional peaks function for which we can visualize the activation geometry. Second, on optimization over individual surrogate ReLU ANNs for three chemical processes, and third, on a hybrid superstructure optimization problem that involves the three ReLU ANNs, additional process submodels, and binary variables. The results for the superstructure problem demonstrate the large potential of region reduction with a decrease of 40% to 50% in computational time, yielding solutions closer than 1% to the reference at negligible effort of obtaining the compressed model.
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All authors Christoph Plate, Caroline Ganzer, Mirko Hahn, Alexander Klimek, Heyuan Liu, Sebastian Sager, Kai Sundmacher, Hanna Wilhelm
Game Theory
How Much Must a Private Mempool Hide? Exact Leakage Thresholds for Sandwich Attacks
Tingyi Lin, Jiazhuo Li and Ruoran Lai
Private and encrypted mempools hide pending transactions to stop sandwich attacks and other forms of maximal extractable value (MEV), but what they hide is rarely everything: a transaction's pair, direction, and a coarse range for its size can still leak. How much leakage makes sandwiching pay? We answer exactly for a fee-free constant-product automated
market maker, the pricing rule behind Uniswap v2. Traders observe an interval containing the victim's size and bid in a first-price auction for the right to sandwich it, and the winning front-run must keep the victim's trade executable at every size in the interval. The answer turns on the smallest size consistent with the leak. It alone determines the feasible front-runs, the largest feasible front-run is optimal for pointwise, expected, and worst-case profit alike, and the guaranteed profit has a closed form. When execution is costly, a
privacy layer that wants to rule out sandwiches profitable at every consistent size may therefore reveal anything about the size except a lower bound above an explicit threshold; the upper end of the range is irrelevant. With two or more symmetric traders, every pure-strategy perfect
Bayesian equilibrium of the auction hands the entire expected net rent to the auctioneer. If the direction is hidden too, no non-contingent first leg front-runs both possible directions, while post-trade arbitrage can survive even perfect pre-trade hiding.
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Computer Vision
ContraFM-S2O: Flow Matching-Based One-step SAR-to-Optical Image Translation Model with Contrastive Learning
Mingqian Yu, Wei-kuan Chiang, Qiurui Wang et al. (+1 more)
In recent years,
diffusion models and GAN-based models have become the mainstream approaches for SAR-to-optical image translation, owing to their advantages, such as high-quality generation and stable training. However, they have shortcomings such as high inference latency and the generated optical images suffer from low detail fidelity, often resulting in blurred edges and loss of fine textures. Thus, we propose ContraFM-S2O, which is a flow matching-based model for SAR-to-optical image translation. Unlike conventional
diffusion models, ContraFM-S2O learns to predict the velocity field in training and solves ODE instead of SDE during inference to improve the sampling efficiency. In addition, ContraFM-S2O replaces instantaneous velocity with average velocity along the interpolation path to realize one-step SAR-to-optical image translation and uses contrastive learning to improve the quality of the generated optical images. Experiments show our model achieves state-of-the-art on SAR2Opt and QXS
datasets, outperforming baselines, and reduces inference latency via one-step generation.
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All authors Mingqian Yu, Wei-kuan Chiang, Qiurui Wang, Peilin Zhao
Fluid Dynamics
Controlling three-dimensional falling liquid films: from weighted-residual models to direct numerical simulation
Oscar A. Holroyd, Susana N. Gomes and Radu Cimpeanu
We consider the problem of controlling a three-dimensional falling
liquid film using same-fluid blowing and suction through the substrate. Building upon recent results for the corresponding two-dimensional problem, we employ a reduced-dimensional weighted-residual model for the film height and downstream flux as the basis for controller design. Linear stability analysis is used to estimate the number of actuators required to stabilise the film, while a linear-quadratic regulator (LQR) framework is used to construct feedback controls for the linearised system. The resulting gain
matrix is then combined with observations from direct numerical simulation (DNS) of the three-dimensional Navier-Stokes equations, allowing controls derived from the reduced-dimensional model to be applied directly to the full flow. Through systematic numerical tests, we demonstrate that these controls successfully stabilise the flat-film solution across a physically relevant range of Reynolds numbers and domain configurations. We further show that both the number and placement of actuators play a crucial role in control performance, with spanwise instabilities requiring additional actuator coverage in the cross-stream direction. Although the extension from two to three dimensions substantially increases the cost of controller construction, this cost is incurred entirely offline during the computation of the gain
matrix, making the overall framework computationally tractable. Our results demonstrate that LQR feedback controls derived from a three-dimensional weighted-residual model retain predictive value when applied to fully resolved DNS, extending previous two-dimensional control methodologies and helping bridge the gap between theoretical control design and experimentally relevant falling-film flows.
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Image and Video Processing
Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning
Mohamed Azzam, Ruobing Liu, Esther C. Ugwueke et al. (+8 more)
Congenital heart disease (CHD) is the most common birth defect, yet a large fraction of cases remain undetected on prenatal ultrasound, in part because current artificial-intelligence methods assume that the key diagnostic frames have already been isolated from a study, by a clinician or by a view classifier. We remove that assumption and address CHD screening directly at the level of the whole ultrasound study. We propose a two-stage framework that first learns transferable frame representations by self-supervised masked-autoencoder pre-training on unlabeled fetal ultrasound, then identifies cardiac frames with a disease-robust module and aggregates them with a
transformer-based multiple instance learning (MIL) model that produces a case-level diagnosis from study-level labels alone. The model further returns its highest-scoring frames for clinician review, and a hierarchical head separates critical from non-critical CHD. On the internal test set of our multi-source development cohort (FUSE), the proposed cardiac-gated MIL model reaches an area under the curve (AUC) of 0.985 with a specificity of 0.990, outperforming the reproduced NATMED ensemble (AUC 0.861, specificity 0.600) and the FetalCLIP foundation model (AUC 0.867, specificity 0.710). On an independent external cohort, all models initially perform near chance, but label-free CORAL adaptation raises the proposed model from an AUC of 0.513 to 0.944, whereas whole-study and view-dependent baselines do not recover. These results indicate that whole-study MIL with disease-robust cardiac-frame identification is an accurate and deployable route to prenatal CHD screening.
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All authors Mohamed Azzam, Ruobing Liu, Esther C. Ugwueke, Ziyang Xu, Shibiao Wan, Alex Foy, Abraham Zabih, Jason Christensen, Neil Hamill, Ling Li, Jieqiong Wang
Materials Science
Polar discontinuity screening by charge disproportionation in ferroelectric-nickelate superlattices
Edith Simmen, Jonathan Amara, Philippe Ghosez et al. (+1 more)
We use density functional theory (DFT) to demonstrate rearrangement of charge disproportionation as a new mechanism for screening polar discontinuities at heterointerfaces. We focus on SmNiO$_3$, a III-III perovskite characterized by an insulating ground state that originates from disproportionation of the electrons at the Ni sites. When interfaced with ferroelectric, II-IV perovskite BaTiO$_3$, this system can display a polar discontinuity whose magnitude is determined by the nature of the interface (NiO$_2$-BaO or SmO-TiO$_2$) and the orientation of the BaTiO$_3$ spontaneous polarization. In the orientation maximizing the interface charge, SmNiO$_3$ compensates by transferring 1 $e$/f.u. between the two interfaces and modifying the disproportionation sequence from Ni$^{4+}$/Ni$^{2+}$ to Ni$^{2+}$/Ni$^{2+}$ at the SmO-TiO$_2$ interface and to Ni$^{4+}$/Ni$^{4+}$ at the NiO$_2$-BaO interface. While this mechanism is potentially universal in all perovskites exhibiting charge disproportionation, our formal analysis of BiNiO$_3$/BaTiO$_3$ superlattices suggests that larger polar discontinuities may need more complex disproportionation patterns for screening.
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All authors Edith Simmen, Jonathan Amara, Philippe Ghosez, Nicola A. Spaldin
Robotics
RECAST: From Log Replay to Closed-Loop Driving Simulation with View-Complete Actors
Zijun Zhao, Liewen Liao, Kang Shen et al. (+2 more)
Closed-loop driving simulation requires rendered observations to remain reliable as the ego vehicle and surrounding actors move beyond their recorded trajectories, exposing views absent from the source log. Existing data-driven simulators reconstruct dynamic actors from sparse observations, which can result in rendering artifacts under these viewpoint changes. We introduce RECAST (REconstructing Controllable Actors for Simulation and Testing), a 3D Gaussian Splatting framework that generates a view-complete actor from a single segmented vehicle observation in a driving log and registers the generated actor in the reconstructed scene. RECAST supports planner-in-the-loop rendering under controlled ego-actor interactions. To adapt an image-to-3D prior to real vehicles, we further introduce RECAR, a
dataset of approximately 20K real vehicles with 600K background-free RGBA images spanning diverse vehicle colors and types. We use two-stage adaptation to improve vehicle generation from real driving-log observations. At the actor level, RECAST reduces $\mathrm{FD}_{\mathrm{incep}}$ from 9.788 to 7.992 relative to unadapted TRELLIS. At the scene level, under actor motion beyond logged trajectories, RECAST reduces $\mathrm{FD}_{\mathrm{incep}}$ from 129.35 to 112.10 and increases $\mathrm{CLIP}_{\mathrm{margin}}$ ($\times1000$) from 0.14 to 3.47 relative to Street Gaussians. We demonstrate planner-in-the-loop simulation with the image-conditioned planner GTRS-Dense. Compared with native Street Gaussians actors, RECAST increases the no-collision (NC) rate from 22.2% (12/54) to 63.0% (34/54) and the mean minimum predicted time-to-collision (TTC) from 0.798 s to 2.150 s. These experiments show that RECAST supports closed-loop planner evaluation under controlled ego-actor interactions beyond log replay. Visit our project page at https://zijunkr.github.io/RECAST/
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All authors Zijun Zhao, Liewen Liao, Kang Shen, Songan Zhang, Ming Yang
Differential Geometry
Hamilton-Type Gradient Estimates for Porous Medium and Fast Diffusion Equations on Riemannian Manifolds for all exponents
Jun Sun and Jiaming Yang
We establish local Hamilton-type gradient estimates for positive $C^{2,1}$ solutions of $u_t=\Delta u^m$ on complete Riemannian
manifolds whose
Ricci curvature is bounded from below. For every fixed $m>1$ and every fixed $0<m<1$, there exist $\beta=\beta(m,n)>0$ and $C=C(m,n)>0$ such that a solution $0<u\leqslant A$ in $B_{2R}(x_0)\times(t_0-T,t_0]$ satisfies the following local gradient estimate \begin{equation*} \sup_{B_R(x_0)\times(t_0-T/2,t_0]}|\nabla u^\beta| \leqslant C A^\beta \left(\frac1R+\sqrt{k}+\frac{A^{(1-m)/2}}{\sqrt T}\right), \end{equation*} where $\mathrm{Ric}_M\geqslant-k$. The proof uses an intrinsic quantitative alternative to locate, around each prescribed positive point, a cylinder on which the solution has a controlled upper-to-lower ratio. A local gradient estimate on this cylinder is combined with a stopping argument. As a consequence, every uniformly bounded positive ancient solution on a connected complete
manifold with nonnegative
Ricci curvature is constant.
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Quantum Physics
Quantum algorithms for the exponentiation of Toeplitz matrices and applications in partial differential equations
Xabier Gutiérrez, Nicola Mariella, Javier González-Conde et al. (+2 more)
We present
quantum algorithms to approximate the exponential of banded Toeplitz
matrices. These
matrices have central importance in many PDE related problems, but their
quantum implementation is hindered by their possibly large norms. Constructing directly the exponential we can circumvent this limitation. By relating the lower/upper shift operators to circulant and skewcirculant generators, which are diagonalised by the QFT, we construct (i) an LCU-based block encoding of banded Toeplitz
matrices, (ii) an efficient, controllably truncated Pauli-string decomposition of the circulant eigenphases with a closed-form error bound, and (iii) a specialized QFT-Trotter product formula. As an application we build a block encoding of the propagator of the discretised heat equation with periodic, Dirichlet and Neumann boundary conditions, using a frequency cutoff.
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All authors Xabier Gutiérrez, Nicola Mariella, Javier González-Conde, Sergiy Zhuk, Mikel Sanz
Machine Learning
Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness
Bin Li, Dongdong Wang and Siyang Lu
Large
language models (LLMs) have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic empirical analysis across three public log
datasets, examining different adaptation strategies, model architectures, parameter scales, and quantization settings. Our results reveal substantial performance differences across adaptation strategies, while model scaling yields varying detection gains across
datasets. We further observe that models with comparable detection accuracy can exhibit markedly different computational costs, and that low-bit quantization largely preserves detection performance in the evaluated configurations. Finally, we examine detection robustness under structural, semantic, and label noise at different perturbation levels. These findings provide empirical insights into the performance, efficiency, and robustness of LLM-based log anomaly detection, highlighting practical considerations beyond conventional accuracy-oriented evaluation.
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