arXiv:2607.10017v1 Announce Type: new
Abstract: Academic and project maps are often produced through a fragmented workflow: researchers locate boundaries, manage shapefiles, join tabular data, assemble locator insets, add cartographic decorations, and export figures through desktop GIS or multi-package Python scripts. This creates an accessibility barrier for non-GIS users and a reproducibility problem when data sources, styling choices, and manual edits are not captured in executable form. We present AcadGIS, a free and open-source Python package that creates publication-oriented research maps from high-level commands under one namespace, import acadgis as agis. AcadGIS provides place-name boundary access, automated study-area locator layouts, thematic cartography, raster and vector layers, curated Earth-observation products, terrain and hydrology context, and configurable PNG, PDF, and SVG export without requiring desktop GIS expertise or hand-managed shapefiles. Its design combines one-import access to the scientific-Python stack, publication-oriented defaults with progressive control, local caching, source attribution, and figure specifications based on code, named data, and a pinned package version. Through three representative use cases, we demonstrate how common paper, thesis, and project maps can be expressed as compact, inspectable scripts. Source code: https://github.com/riponcm/AcadGIS.
Science Journals
arXiv:2607.09938v1 Announce Type: new
Abstract: Generative Artificial Intelligence (GenAI) coding tools are transforming visualization education. They can assist with implementation and design, but they can also let students bypass intended learning trajectories. In this paper, we share our retrospective experience managing and teaching AI use in an upper-level visualization course. We implemented prompt injections, asked oral checkout questions, and taught two AI coding labs. Prior to our coding labs, at least half of the students had already used AI tools in their assignments. In both AI coding labs, refinement accounted for about half of students' prompting logs, and explanation was almost absent. In the lab where AI coding was optional, 44 of 78 (56.4%) submissions preferred the scaffolded instructions over designing their own prompts. Students' final projects were more polished than in our previous offering, but also more visually homogeneous. Our reflections point to the need for clearer AI use boundaries and instruction on prompting, and for teaching students to question generic AI designs and adapt them to their data and story.
arXiv:2607.10548v1 Announce Type: cross
Abstract: Pseudo-labeling based on Optimal Transport (OT) has become an effective mechanism for enhancing short text clustering. Existing OT methods are short in modeling semantic consistencies between samples, which may assign different pseudo-labels to semantically similar samples. These erroneous pseudo-labels can cause the model to produce inferior clusters. This paper proposes a novel short text clustering framework, which remedies the neglect of semantic consistency in existing OT methods, generating reliable pseudo-labels to facilitate clustering. Specifically, the proposed approach first designs an instance-level attention mechanism to capture semantic relationships between samples, which are then integrated into the OT formulation to endow the transport process with neighborhood semantic awareness. By solving the proposed OT formulation, reliable pseudo-labels are obtained that simultaneously account for sample-to-sample semantic consistency and sample-to-cluster global structure information. These pseudo-labels are then used as supervisory signals to guide the model to achieve accurate clustering. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods. The code is available at: \href{https://github.com/YZH0905/CAOT-STC}{https://github.com/YZH0905/CAOT-STC}.
arXiv:2607.10103v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly embedded in high-impact workflows, yet their ability to generate fluent text at scale has amplified risks of provenance ambiguity, model misuse, and large-scale content laundering. LLM watermarking, embedding invisible signatures into model outputs, has emerged as a promising technical layer for attribution, auditing, and downstream trust decisions. However, the literature has grown rapidly and unevenly: existing categorizations often mix orthogonal design choices, making it difficult to compare methods, reason about guarantees, or translate research results into deployable systems. This survey provides a systematic, deployment-oriented review of LLM watermarking. We organize the space by the core questions practitioners must answer: where a watermark is embedded (generation-time vs. training-time, token vs. representation), who can detect it (public vs. private detection authority), what is assumed (access to logits, sampling control, secret keys, model ownership), and which threat models are targeted (paraphrasing, translation, summarization, style transfer, token manipulation, and adaptive removal). We synthesize the main families of techniques-including sampling biasing, code-based schemes, representation- and training-based approaches-and analyze their security-utility trade-offs through the lens of detectability, robustness, and distribution shift. We further review attack and evasion strategies, evaluation protocols and metrics (false positive control, calibration, robustness curves), and open challenges such as cross-model transfer, multi-modal pipelines, collusion, and governance constraints. Finally, we provide practical guidance for selecting watermark designs under real operational requirements and identify research directions needed for reliable, accountable LLM deployment.
arXiv:2607.10841v1 Announce Type: new
Abstract: Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be misaligned, which can significantly deteriorate the performance of the learned models. This is especially common in remote sensing, when aerial or satellite images are co-registered with labels from another source (e.g., OpenStreetMap). In this work, we propose a novel approach for training on misaligned labels, where we simultaneously learn the label alignment. Our align and segment (AnS) approach builds on the spatial transformer module to transform the misaligned labels using an affine transformation to provide a better learning target for a canonical semantic segmentation network. We prevent shortcut learning of misaligned labels in these semantic segmentation networks through a self-supervised regularization loss and show that it is complementary to data augmentation, especially for systematically misaligned training data. A decisive characteristic of our AnS approach is that it learns without requiring any golden labels. We experimentally show on both synthetic and real-world data from different cities that our approach enables high-quality building segmentation and precise label-image alignment at the same time. Code and derived datasets are available at https://github.com/venkanna37/align-and-segment
arXiv:2607.10842v1 Announce Type: new
Abstract: A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infeasible or unsafe outputs. Hybrid approaches that employ model predictive control (MPC) to address this problem can be unstable, as poor trajectory initializations from the diffusion model prevent the MPC from converging to a safe and feasible solution. To overcome these challenges, we propose D-SafeMPC, which enhances the interaction between diffusion and control. Our method guides the reverse diffusion process with control barrier functions (CBFs) and control Lyapunov functions (CLFs) and employs an iterative-projection scheme where an MPC refines the trajectory at each denoising step. This steers sampling toward safe, goal-directed regions and provides reliable MPC warm starts. In simulations on a Franka manipulator across four scenarios (one static-obstacle and three dynamic-obstacle settings) and in a sim-to-real experiment on a physical Franka robot, D-SafeMPC improves safety, task success rates, and planning efficiency over state-of-the-art baselines. To facilitate reproducibility, our source code and experimental configurations are available in a repository at https://github.com/erdiphd/D-SafeMPC
arXiv:2607.10046v1 Announce Type: new
Abstract: Reversible logic has long promised substantial reductions in energy dissipation, yet prior demonstrations have not scaled to commercially relevant systems. This work presents a quantitative framework for evaluating reversible logic through a process termed CMOS conversion, in which a conventional CMOS design is transformed into a functionally equivalent reversible implementation and compared using common performance metrics. The framework combines planning equations, kinetic-inductor energy-storage models, a four-phase 4LC energy-recycling power supply, and RLC-based simulation methods that account for data-dependent loading effects. The analysis identifies inductor loss as a fundamental limitation of conventional approaches and shows that high-energy-density kinetic inductors provide essential design margin for scaling reversible systems. Using representative device parameters, the framework suggests that selected cryogenic CMOS qubit controller circuits could be converted to reversible logic using available or near-term technologies. Rather than claiming commercialization of reversible logic in general, the paper provides a methodology for assessing its feasibility and potential benefits across future applications.
arXiv:2607.10690v1 Announce Type: new
Abstract: Incremental scene reconstruction is essential for real-world applications. Although 3D Gaussian Splatting shows strong potential, most existing approaches require offline conversion of the optimized Gaussians into an intermediate implicit field for explicit mesh extraction, which hinders seamless integration with downstream tasks. To address this limitation, we propose a novel online framework that incrementally reconstructs and updates high-fidelity explicit meshes by directly triangulating a dense geometric Gaussian representation, which supports both high-quality rendering and incremental surface reconstruction. Moreover, we present a direct meshing algorithm that efficiently extracts and updates the mesh from the Gaussian set. To ensure mesh accuracy, we enforce a plane-based pulling constraint that dynamically aligns 3D Gaussian primitives to the approximated local surface. Furthermore, our framework significantly reduces memory and computational overhead during long-sequence processing by dynamically freezing fully optimized historical regions. Experiments on public datasets demonstrate that our method outperforms conventional Gaussian-based methods on both rendering quality and reconstruction accuracy.
arXiv:2607.10740v1 Announce Type: new
Abstract: The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns and suppressing noise appropriately. Multi-Scale Convolution with Optimal Transport Attention (MSC-OT) is proposed in this paper. MSC-OT is a useful architecture to optimize the attention mechanism. It combines multi-scale convolution with Sinkhorn optimal transport method based on inverted embedding. The inverted embedding approach embeds each variable as a token and allows the model to capture cross-variate relationships better. MSC-OT consists of two part: (1) Multi-Scale Convolution Enhancement, that applies multi-scale convolutions to attention score matrices based on inverted embedding, capturing local structural patterns in the variate-interaction space induced by compressed temporal representations; (2) Sinkhorn Optimal Transport Regularization, that formulates attention computation as an optimal transport problem and employs iterative matrix scaling to ensure balanced information flow across variates. Adaptive Fusion Strategy utilizes softmax-normalized learnable weights to dynamically combine base attention, convolution-enhanced, and OT-regularized scores. Experiments on widely-used datasets, including ETT, Electricity, Traffic, Solar-Energy, and Exchange-Rate, show that MSC-OT achieves well performance in both short-term and long-term forecasting tasks. Ablation experiments further validate the effectiveness of each proposed component and their synergistic contributions to improving prediction accuracy for multivariate time series forecasting.
arXiv:2607.10578v1 Announce Type: new
Abstract: Existing hypotheses represent a concept in an LLM as a single point, a linear direction, or a Gaussian cluster, yet it remains unclear how and why such structures emerge. Here, we show that concept geometry can be precisely characterized via Laguerre Geometry, in which a concept is defined as a region--a Laguerre-Voronoi cell or a union of cells--allowing us to strictly define, measure, and separate concepts. Building on this formulation, we show that finer-grained concept structures, such as inclusion and hierarchy, are naturally revealed by the Laguerre weights. We then push this geometry inside the transformer. Decomposing each layer into piecewise-linear operators, we show that a token's hidden trajectory is governed by two coupled mechanisms: a static tree of self-contained piecewise-linear flow, and a dynamic transport that hops the trajectory across trees when cross-token attention fires. This decomposition yields Geometric Lens, a training-free, hyperparameter-free method for reading out the exact concept a hidden vector encodes at any layer. We also develop Laguerre Autoencoder, a 2D visualizer that renders both the decision geometry and a model's full reasoning trajectory in one view. Finally, we move beyond explanatory geometry toward actionable interpretability, showing that Geometric Lens recovers the correct factual token when a model is prompted with in-context interference. The code is available on GitHub.
arXiv:2607.10650v1 Announce Type: new
Abstract: Fixed-point logics provide an expressive intermediate framework for reasoning about temporal properties of programs. One of the key approaches to solving their validity checking problem is via transformations from least fixed points to greatest fixed points ($\mu$-to-$\nu$ transformations), which generalizes a reduction from termination verification to safety verification studied in binary reachability analysis. In this paper, we introduce game-semantic interpretations of $\mu$-to-$\nu$ transformations. We first introduce a new $\mu$-to-$\nu$ transformation based on parity relations. We show that solving $\mu$-to-$\nu$-transformed fixed-point equation systems corresponds to finding winning strategies in the game semantics of the original fixed-point equation systems. We apply the same game-semantic framework to interpret two existing $\mu$-to-$\nu$ transformations, one by Kobayashi et al.\ and the other by Unno et al, and show that they admit analogous game-semantic interpretations. Furthermore, we show that the game introduced by Tsukada et al.\ corresponds to an alternative characterization of the winning condition. On the implementation side, we propose optimization techniques for efficiently solving our new $\mu$-to-$\nu$ transformation. We implement these techniques in a fixed-point logic solver, compare our approach with existing solvers, and demonstrate the effectiveness of the proposed optimizations through experiments.
arXiv:2607.10231v1 Announce Type: new
Abstract: Tree crowns are a challenging target for resilient AI because they are not static objects: their spectral response, internal texture, translucency, and apparent boundaries change substantially across the growing season. We develop PhenoEmbed, a self-supervised crown-centric temporal embedding model trained with contrastive and masked reconstruction objectives on HeideBench, an 18-date UAV multispectral time-series benchmark for forest crown phenology in D{\"o}lauer Heide. The model treats seasonal crown dynamics as phenological appearance change driven by leaf emergence, canopy closure, senescence, and leaf-off conditions. Segmented tree crown polygons are retained as object anchors to extract aligned crown-centered crops through time, allowing one 256-dimensional vector summarizing seasonal crown appearance to be learned per tree. On 5,885 crop-safe crowns, the exported embeddings show structured low-dimensional organization, with the first two principal components explaining 25.1\% of variance and nearest-neighbor retrieval producing a median top-1 cosine similarity of 0.946. Compared with handcrafted temporal features and a learned mean-pooling baseline, PhenoEmbed yields substantially more compact nearest-neighbor structure, while ablations show that the contrastive loss, masked reconstruction loss, and explicit seasonal time features each affect the structure of the learned embedding space. These results support PhenoEmbed as a reusable forest crown representation learner and motivate future downstream tests of whether such features improve tree-level models under seasonal change.
arXiv:2607.10608v1 Announce Type: new
Abstract: Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments. Existing work mostly treats memory as a supply problem, asking what experience to write, how to store it, and which entry to retrieve for the next task. Yet we still lack a clear account of how models consume retrieved memory across a multi-step action trajectory. This consumption process matters because it determines not only what memories should be retrieved, but also what models and control policies are needed to use them safely. To diagnose this process, we propose Entry--Propagation--Recovery (E-P-R), a trajectory-level framework that asks where memory first changes an action, whether that change carries forward, and whether the agent can recover after leaving a correct path. We instantiate E-P-R on WebArena and on MemTrapBench, a controlled benchmark we build to isolate these phases. We find that the main failure often begins at entry: agents adopt conflicting memory at the first exposed decision point even when it is task-wrong. Repeated exposure then amplifies this early error, while recovery after divergence is weak. Together, these effects create a compliance trap: across models, conflicting memory induces similar compliance rates, but once agents comply, their success rates collapse to a low floor. Stronger agents therefore suffer larger absolute damage because each compliance event erases more baseline capability. These results suggest that memory-augmented agents should be evaluated not only by retrieval quality or final success rate, but by how they consume memory throughout the trajectory.
arXiv:2607.10670v1 Announce Type: new
Abstract: This study experimentally and numerically investigates the electrohydrodynamic (EHD) interaction produced by a surface dielectric barrier discharge (SDBD) plasma actuator at atmospheric pressure. The non-thermal dielectric barrier discharge generates ionic wind, which is characterized using a symmetric annular actuator composed of concentric ring and disk electrodes. Unlike conventional linear SDBD actuators that primarily produce tangential airflow, this annular configuration generates a predominantly vertical ionic-wind jet. The effects of electrode diameter D and thickness delta on the induced wind velocity perpendicular to the electrode plane are systematically examined. The experimental results show a maximum wind velocity of 3.42 m s^{-1} for an optimized electrode configuration with D = 32 mm and delta = 0.06 mm. Numerical plasma-fluid simulations support the experimental trends and provide spatial distributions of airflow velocity, electrohydrodynamic volumetric force, electron temperature, and gas pressure in the plasma region. Additional diagnostics based on ozone concentration measurements and Schlieren imaging show that electrodes with larger diameters, particularly 22 and 32 mm, enhance the height and development of the vertical flow, while increasing electrode diameter also promotes ozone production. The results demonstrate an important trade-off between ionic-wind performance and reactive byproduct generation. These findings provide practical guidance for optimizing annular dielectric barrier discharge plasma actuators for active flow control, air purification, ozone-assisted disinfection, and biomedical plasma applications.
arXiv:2607.10551v1 Announce Type: cross
Abstract: Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D--3D vertebral registration. Although calibration quality is typically evaluated using reconstruction-based metrics such as reprojection error, its influence on projection-domain consistency remains poorly understood. This study presents a synthetic framework for evaluating how intrinsic calibration perturbations affect vertebral fluoroscopic projections and downstream registration performance.
CT-derived vertebral models and controlled cone-beam imaging geometry were used to generate DRRs with both ground-truth and perturbed intrinsic calibration parameters while maintaining identical anatomy and acquisition pose. Projection-domain changes were quantified using anatomical landmark displacement, contour distance, silhouette overlap, image similarity, and landmark-based 2D--3D registration accuracy in anterior--posterior (AP) and lateral (LAT) views.
Results show that even small intrinsic calibration perturbations produce measurable changes in vertebral projection geometry, contour morphology, landmark localization, and DRR appearance. Sensitivity is strongly view dependent, with LAT projections exhibiting substantially greater deformation and anatomical displacement than AP projections. These projection inconsistencies also degrade downstream 2D--3D registration, particularly rotational alignment accuracy.
The findings demonstrate that projection-domain consistency complements conventional reconstruction-based calibration metrics and provides a practical framework for assessing calibration robustness. This approach may improve the reliability of DRR generation and fluoroscopy-guided vertebral registration in image-guided spinal applications.
arXiv:2607.10707v1 Announce Type: cross
Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration. The benchmark includes FiveQubit, Steane, Planar3x3, and Planar5x5 codes, four noise families, and five evaluation regimes: interpolation, unseen-p transfer, unseen-noise transfer, few-shot unseen-code adaptation, and few-shot held-out-size adaptation. We compare a classical Meta-MLP teacher-trained baseline with variational quantum circuit (VQC) meta-decoders selected through hardware-aware quantum architecture search over qubit count, circuit depth, and entangling topology. The Meta-MLP achieves teacher-label accuracies of 0.9993, 0.9118, 0.9342, 0.6304, and 0.7548 across the five regimes, while the hardware-aware VQC achieves 0.9400, 0.8495, 0.8415, 0.5678, and 0.7143. However, logical-level evaluation shows that high teacher-label accuracy alone is insufficient in the most challenging Planar5x5 setting. During interpolation, the raw logical-failure ratios relative to the teacher are 12.08 and 25.91 for the Meta-MLP and VQC, respectively, whereas confidence-gated fallback reduces them to 1.71 and 1.11. These results support confidence-aware selective recovery rather than unconditional teacher replacement.
arXiv:2607.10329v1 Announce Type: new
Abstract: Vision Language Models (VLMs) offer powerful multimodal ability but also expose users to text-based privacy attacks where adversaries crawl online photos and query VLMs to extract sensitive attributes. Existing reversible adversarial example (RAE) methods protect images in purely visual tasks but fail in multimodal settings, and current adversarial examples on VLMs rely on high frequency noise that severely degrades visual quality. We propose CloakDiff, the first framework for reversible, high fidelity privacy protection against text-based query attacks in VLMs. CloakDiff produces imperceptible adversarial examples by combining diffusion based adversarial editing with an invertible network that embeds the original image for lossless recovery. It perturbs both pixel space embeddings and manipulates latent cross attention maps to ensure strong cross-model and cross-prompt transferability while preserving global visual structure. To further enhance fidelity, we design EDM Heuristic Sampling, a principled diffusion schedule for adversarial guidance. Experiments on multiple datasets and VLMs demonstrate that CloakDiff delivers multimodal privacy preservation with high visual quality and reversibility.
arXiv:2607.10783v1 Announce Type: new
Abstract: Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive. Moreover, annotating WSIs at the pixel level is labor-intensive and time-consuming. Therefore, it is important to investigate whether low-magnification pathology images with limited annotations (i.e., image-level instead of pixel-level labels) can achieve performance comparable to high-magnification images. This paper presents a systematic benchmark study on weakly supervised histopathological image segmentation under different low-resolution storage settings. Starting from high-resolution image patches, we simulate lower-magnification inputs and reconstruct them to the original size using interpolation and deep learning-based reconstruction methods before applying the weakly-supervised segmentation pipeline. This framework enables a quantitative evaluation of how weakly supervised methods respond to different levels of resolution degradation. Experimental results show that reconstruction quality metrics alone are insufficient to predict downstream segmentation performance. In particular, the study identifies a critical degradation point where the localization of small-scale structures declines significantly. These findings provide practical guidance for designing efficient digital pathology storage systems while maintaining reliable automated analysis. Code is available at https://github.com/Dung-Dx/LowMagWSS
arXiv:2607.10627v1 Announce Type: new
Abstract: We present Spectral Consistent Flow (SC-Flow), a 3D medical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical image translation as a stochastic Brownian bridge process that directly constructs a mapping between source and target modalities by predicting the support regularized mean velocity field. To mitigate modality entanglement, over-smoothing, and artifacts induced by the implicit low-pass modulation of the latent average velocity, we introduce a Spectral Consistency Corrector that dynamically regularizes the evolution of the power spectral density via learnable frequency-domain gain modulation. This mechanism establishes an explicit bridge between spatial textures and spectral energy flow, enabling the model to recover fine-grained anatomical fidelity while maintaining global structural coherence. Extensive experiments on four datasets demonstrate that SC-Flow delivers significantly more accurate, consistent, and robust performance across various translation scenarios.
arXiv:2607.09723v1 Announce Type: new
Abstract: We present a formulation of the special theory of relativity which bears on F.A. Lindemann's assertion that this theory could have been reached "by pure logic soon after Isaac Newton". We start with the "intuitively plausible" pair of Galilean spatial transformations. These simple relations possess a rich structure of ten properties. From these, one discerns an axiomatic structure (and a synchrony convention) leading to the well-known Lorentz-type transformations which contain a universal constant, $V^2$. Analysis of Fizeau's experiment (1851) shows that $V^2 = c^2$, where $c$ is the speed of light in vacuo. Hence one obtains the Lorentz transformation. Requisites for such a formulation (Galileo's relativity principle, analytical mechanics, the method of changing a postulate, etc.) emerged during the 1600s and 1700s. These observations provide a framework for Lindemann's assertion. We also consider inertially-moving systems of charge, and derive electromagnetic field equations and a force law by applying the Lorentz-type transformations to the theory of electrostatics. The results are independent of any choice of units, and from their dependence on $V^2$ one can infer how certain phenomena manifest in each of the three possible types of space-time.
arXiv:2607.10931v1 Announce Type: cross
Abstract: Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional alignment: aligning functional data across individuals before training population-level decoders. The core issue is to strike the balance between aligning functional features and preserving the anatomical structure, while maintaining computational efficiency. We introduce a new functional alignment method for fMRI, SpectralOT, that embeds cortical geometry into Laplace-Beltrami eigenmodes along functional data to regularize the alignment.
arXiv:2607.10615v1 Announce Type: new
Abstract: We present a reproducible, parameter-driven software workflow for optimizing approximate mutually unbiased basis (AMUB) configurations in arbitrary dimensions d using a Lie-algebra unitary parameterization. The workflow is designed for portable execution across CPU, Apple MPS, CUDA-capable GPU, and HPC backends, using a Taylor-series matrix exponential layer as an accelerator compatibility pathway. As a dimension-six case study, we optimize unanchored configurations across 100 random seeds for basis counts n = 3, 4, 5, 6 in complex128 and complex64 arithmetic. The workflow recovers exact three-basis configurations, identifies a recurrent four-basis partial-exact hub-and-triangle structure, and finds no near-exact pairs for n = 5 or n = 6 in the reported campaigns under the primary tolerance. As a hardware-execution check, we embed the representative d = 6, n = 4 transition unitaries into three-qubit 8x8 unitaries and execute the resulting circuits on the 156-qubit Heron processor ibm-marrakesh using subspace post-selection. The measured QPU pairwise losses are dominated by a hardware and compilation noise floor of approximately 0.02-0.08, associated with compiled circuits averaging 37 native CZ gates, which obscures the distinction between classically near-exact and defective pairs. The results provide a reproducible computational framework for exploring AMUB landscapes, together with an initial assessment of the challenges involved in executing optimized dimension-six unitaries on current quantum hardware.
arXiv:2607.09950v1 Announce Type: new
Abstract: We study unconstrained bilinear zero-sum games, a fundamental model in online learning, adversarial optimization, and multi-agent decision-making. We introduce the implicit midpoint gradient descent rule, which we derive from continuous-time follow-the-regularized leader dynamics via symplectic integration methods. We prove that implicit midpoint gradient descent inherits several powerful properties from the continuous-time dynamics, including bounded orbits, fast ergodic convergence to Nash equilibria, and learning-rate-independent stability guarantees. This is the first traditional online optimization approach to simultaneously achieve these properties in unconstrained bilinear zero-sum games. Finally, computational experiments demonstrate that the proposed method significantly outperforms the standard methods, optimistic and alternating gradient descent.
arXiv:2607.10939v1 Announce Type: cross
Abstract: A graph class is $k$-WQO if its $k$-labeled graphs are well-quasi-ordered under label-preserving induced subgraph embeddings. We show that every hereditary graph class that is $2$-WQO has bounded clique-width. Combined with the recent result of Dumas and Lopez, this confirms a long-standing conjecture of Pouzet: A hereditary graph class is $2$-WQO if and only if it is $k$-WQO for all $k\geq 2$, if and only if it is $\forall$-WQO, that is, its labeled graphs are well-quasi-ordered for every possible well-quasi-ordered label set.
Our proof builds on a recent structure/non-structure dichotomy for the model theoretic notion of monadic dependence by Dreier, M\"ahlmann, and Toru\'nczyk. Through the non-structure characterization by forbidden induced subgraphs, we show that every hereditary $2$-WQO graph class is monadically dependent. Leveraging the Ramsey-theoretic structural properties provided by monadic dependence, we then establish bounded clique-width by ruling out the existence of large well-linked sets, which are the canonical obstructions for clique-width.
arXiv:2607.10007v1 Announce Type: new
Abstract: Ground and excited electronic states in highly symmetric systems typically possess high degrees of spatial degeneracy as a consequence of point-group symmetry. However, many current quantum-chemical methods struggle to accurately describe the strong correlation effects inherently present in these states, thereby precluding the ability to obtain meaningful insights into the electronic structure of the underlying systems. Consequently, many of their important chemical and spectroscopic properties cannot be reliably computed and predicted. In this article, a new theoretical framework is described that unifies the symbolic treatment of non-Abelian symmetry in QSym$^2$ and the recently developed state-specific multi-reference coupled cluster theory termed Generalised Normal Ordered Coupled Cluster (GNOCC) to describe such difficult ground and excited states in a balanced and targeted manner. This is ensured by the ability of QSym$^2$ to exploit symmetry orbits to restore any broken spatial symmetries and generate symmetry-adapted multi-determinantal wavefunctions, as well as the ability of GNOCC to dynamically correlate arbitrary spin eigenfunctions in a size-extensive and spin-free manner. To illustrate the capabilities of this framework, several ground and excited states in three model systems are examined in detail: (i) octahedral $(\textrm{H}_6)^{2+}$, (ii) octahedral $\textrm{H}_6$, and (iii) tetrahedral $\textrm{Li}_4$. The results demonstrate that the proposed method can target both degenerate and non-degenerate states, while delivering improved numerical performance relative to conventional single-reference coupled-cluster approaches.