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01.
arXiv (quant-ph) 2026-06-24

Fermi surface change and $d$-wave superconductivity in the square lattice Kondo-Heisenberg model

作者:
Alexander Nikolaenko↗Riccardo Rende↗Luciano Loris Viteritti↗Subir Sachdev↗Ya-Hui Zhang↗

arXiv:2606.23799v1 Announce Type: cross Abstract: We study the two-dimensional Kondo-Heisenberg model on a square lattice, with the conduction electrons away from half-filling, using neural network quantum states. Mapping the ground-state phase diagram as a function of the Kondo and Heisenberg couplings, we identify (i) at weak Kondo coupling, antiferromagnetic Néel order with a Fermi surface whose enclosed area counts only the conduction electrons and is insensitive to the Néel order, and (ii) at strong coupling, a heavy Fermi liquid with a Fermi surface whose enclosed area counts both the conduction electrons and the spins. In the crossover between these regimes, we find $d_{x^2-y^2}$ superconductivity, evidenced by off-diagonal long-range order in the pair-pair correlations and a pairing-amplitude dome that coexists with the underlying magnetic phase. Our results establish Fermi volume change and unconventional superconductivity as intrinsic features of the two-dimensional Kondo-Heisenberg model.

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02.
arXiv (CS.CV) 2026-06-16

Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models

作者:
Dianqiao Lei↗Lianlei Shan↗

Existing Vision-Language-Action (VLA) models predominantly rely on explicit Chain-of-Thought (CoT) reasoning to bridge perception and action. While effective, this paradigm suffers from high computational costs and error propagation in multi-step tasks. In this paper, we propose Adaptive Variable Alignment VLA (AVA-VLA), a novel Latent Reasoning VLA framework that models reasoning as a sequence of unobservable latent variables, bypassing the need for explicit text generation. However, latent trajectories are inherently susceptible to noise interference and misalignment with downstream objectives. To address this, we introduce a Reinforcement Learning-based Denoising mechanism that treats latent state generation as a sequential decision process, optimizing reasoning trajectories via task-level rewards. Furthermore, we incorporate an Early-Exit Strategy that adaptively terminates reasoning based on state confidence, enabling a dynamic trade-off between depth and efficiency. Extensive experiments on embodied decision benchmarks demonstrate that AVA-VLA achieves a 6x inference speedup over explicit CoT methods while attaining a 98.3% average success rate on LIBERO, improving both efficiency and long-horizon stability over full-reasoning baselines.

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03.
arXiv (CS.CV) 2026-06-18

RUB: Evaluating Residual Knowledge in Unlearned Models

作者:
Hao Xuan↗Xingyu Li↗

Machine Unlearning (MUL) has emerged as a key mechanism for privacy protection and content regulation, yet current techniques often fail to guarantee the complete removal of sensitive information. While most existing works focus on verifying the execution of unlearning, they overlook the critical question of whether models remain robust against adversarial attempts to recover forgotten knowledge. In this work, we advocate for the principle of Robust Unlearning, which requires models to be both indistinguishable from retrained counterparts and resilient against diverse adversarial threats. To instantiate this principle, we propose a unified benchmark, RUB (Robust Unlearning Benchmark), that systematically evaluates the robustness of unlearning algorithms across classification, image-to-image reconstruction, and text-to-image synthesis. Within this framework, we introduce the Unlearning Mapping Attack (UMA) as a generalizable method to detect residual information, and demonstrate how existing attack strategies can be adapted into this framework as long as they conform to the generic UMA framework. Our experiments across discriminative and generative tasks reveal that state-of-the-art unlearning methods remain vulnerable under these evaluations, even when passing standard verification metrics. By positioning robustness as the central criterion and providing a benchmark for adversarial evaluation, we hope RUB paves the way toward more reliable and secure unlearning practices. The codebase and model checkpoints in RUB will be published.

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04.
arXiv (CS.CV) 2026-06-18

Semantic Robustness Certification for Vision-Language Models

作者:
Peiyu Yang↗Paul Montague↗Feng Liu↗Andrew C. Cullen↗Amardeep Kaur↗Christopher Leckie↗Sarah M. Erfani↗

Vision-language models (VLMs) are now widely used in downstream tasks. However, real-world applications often expose VLMs to distribution shifts induced by semantic variation (e.g., shape, size, and style). Robustness certification determines if a model's prediction changes when transformations are applied to its input. While most certification frameworks study geometric or pixel-level transformations over inputs, this work proposes a novel framework that enables certifying VLM robustness under semantic-level transformations. Leveraging the open-vocabulary capability of VLMs, we use text prompts as semantic proxies to construct transformations parameterized by an extent that controls the degree of semantic variation. By characterizing the VLM decision boundary in closed form, our framework quantitatively certifies extent intervals for which the predicted class remains unchanged under the semantic transformation. Our framework is the first to certify VLM robustness under semantic-level variations without requiring additional data for each variation, making it practical to apply. Experiments on both synthetic and real-world data show that our framework enables certifying robustness under diverse semantic variations across scenarios.

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05.
arXiv (CS.AI) 2026-06-11

Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

作者:
Ge Song↗Kiarash Naghavi Khanghah↗Anandkumar Patel↗Rajiv Malhotra↗Hongyi Xu↗

arXiv:2606.11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios. The framework integrates analytical physics priors, which are systematically extracted from scientific literature via Large Language Models, into a privileged teacher model. We employ a Graph-Masked Attention layer to capture the complex physical dependencies among input variables showing strict setpoints or a combination of static and high-frequency temporal signatures. This privileged knowledge is distilled into a lightweight student predictor for inference. The feasibility and robustness of the framework are evaluated through a comprehensive experiment across five diverse manufacturing processes. To ensure statistical reliability, given the small dataset sizes, a repeated K-fold cross-validation technique is employed to quantify model stability and generalization. Results indicate that the proposed framework consistently achieves high predictive accuracy across all evaluated domains. Most importantly, the architecture demonstrates significant fault tolerance by maintaining robust predictive performance even in scenarios where LLM-derived analytical priors are suboptimal or incomplete. Furthermore, the student predictor achieves an inference frequency exceeding 6000 Hz, which facilitates real-time edge deployment on standard industrial hardware. This work provides a scalable solution for bridging the gap between theoretical physics and real-time industrial monitoring in data-limited environments.

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06.
arXiv (math.PR) 2026-06-19

Optimal Sparsification of Gaussian Processes

作者:
Shivam Nadimpalli↗

arXiv:2606.19763v1 Announce Type: new Abstract: We prove an optimal dimension-free sparsification theorem for suprema of centered Gaussian processes. Given a bounded set $T\subseteq\mathbb{R}^n$, we show that the supremum of the canonical Gaussian process on $T$ can be $L^2$-approximated by the supremum of a shifted subprocess indexed by only $\exp(O(1/\varepsilon^2))$ points, with error at most $\varepsilon$ times the Gaussian width of $T$. In particular, the size of the approximating process is independent of both the ambient dimension and the cardinality of the original index set. This improves a recent sparsification theorem of De, Nadimpalli, O'Donnell, and Servedio (2026) by an exponential factor, and we show that the dependence on $\varepsilon$ is tight up to constants in the exponent. As consequences, we obtain an exponentially improved junta theorem for norms over Gaussian space and sharpen results on learning, property testing, and polyhedral approximation of convex sets under the Gaussian measure. The proof is based on an interpolation argument that combines Sudakov's minoration with the Brascamp–Lieb inequality.

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07.
arXiv (CS.CV) 2026-06-15

Trimodal Glioma Representation Alignment via Volumetric Contrastive Learning

作者:
Denise Marini↗Eleonora Grassucci↗Danilo Comminiello↗

Glioma grading and survival prediction require the integration of heterogeneous information collected at different spatial and biological scales. Histopathology describes tissue morphology, mRNA expression captures molecular activity, and magnetic resonance imaging provides a non-invasive view of tumor extent and radiological heterogeneity. Existing glioma prognosis models often combine only two of these sources, while their alignment objectives remain mostly pairwise. This paper introduces GLORIA, a novel trimodal framework for GLioma Omics - Radiology - hIstopathology Alignment. GLORIA processes whole-slide image regions, gene-expression profiles, and 3D MRI volumes through modality-specific encoders, projects them into a shared latent space, and aligns them with a Gramian contrastive loss that measures the volume spanned by the three modality embeddings. The aligned representations are fused through a cross-modal gating module and optimized jointly for three-class glioma grading and overall survival prediction. We evaluate GLORIA on a matched TCGA-GBM/LGG and BraTS21 cohort, comprising 132 patients with all three modalities. On the shared trimodal test set, GLORIA improves over the bimodal WSI-mRNA baseline in all the metrics considered.

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08.
bioRxiv (Bioinfo) 2026-06-08

DDI_single: Single-Sequence-Based Protein Domain Assembly

作者:
Shengyi↗

Domains are the basic units of protein structure and function. Appropriate inter-domain organization is critical to enable cooperative execution of multiple related functions. It is thus a crucial step to determine the full-length structure of multi-domain proteins for the purpose of elucidating their functions and designing new drugs to regulate these functions. Existing structure prediction algorithms are generally better at solving the internal conformation of domains, rather than modeling the relative positions between domains. To address the challenge of accurately determining multi-domain protein conformations, we develop a single-sequence-based domain assembly algorithm called DDI_single. DDI_single directly extracts features from the amino acid sequence using the protein language model ESM-1b, and accurately predicts the interactions between residue pairs of structural domains through a novel gated cross-attention module, thus achieving the correct assembly of structural domains. With the knowledge of domain definition, DDI_single achieves more than 20% higher accuracy in the task of predicting the relative distances of residue pairs between domains than that of the single-sequence-based structure prediction algorithm trRosettaX_single. When assembling domains with known spatial conformations, DDI_single correctly assembles 74.4% of the samples in the test set (TM-score>0.5). When assembling domains with unknown spatial conformations, in cases where the internal spatial conformations of domains are correctly modeled, DDI_single correctly assembles 73.9% of the samples.

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09.
arXiv (CS.AI) 2026-06-11

Sustainability assessment using multimodal AI agents

作者:
Zhihan Zhang↗Alexander Metzger↗Yuxuan Mei↗Felix H\"ahnlein↗Zachary Englhardt↗Tingyu Cheng↗Gregory D. Abowd↗Shwetak Patel↗Adriana Schulz↗Vikram Iyer↗

arXiv:2507.17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale. However, a traditional life cycle assessment (LCA) of an electronic device, which maps materials and processes to environmental impacts, often requires proprietary or unavailable data. Here, we reimagine conventional sustainability assessment by introducing a multimodal multi-agent AI system that emulates the collaborative process between LCA professionals and stakeholders (such as product managers and engineers) to automatically estimate the carbon footprint of electronic devices. The agents iteratively construct a complete life-cycle inventory by leveraging a structured data abstraction and software tools that mine information from the public internet, including repair communities and government regulatory databases. This reduces data gaps and data collection from weeks or months of expert time to under one minute. The system can calculate carbon footprint within 19% of expert LCAs with zero proprietary data (typical of the variation between human LCAs). We also show that by encoding domain-specific knowledge, environmental impact estimation can be reframed as a data-driven prediction task, in which both unknown products and emission factors are represented as weighted combinations of similar ones with known emissions.

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10.
arXiv (CS.AI) 2026-06-24

MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching

作者:
Kamil Kwarciak↗Marek Wodzinski↗

arXiv:2606.24433v1 Announce Type: cross Abstract: Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied. We investigate completion through continuous-time generative modeling and introduce PCFM, a PTv3-backed flow matching approach for medical point cloud completion. We evaluate on SkullFix and SkullBreak, and additionally on the more recent Mandibular Defect dataset. We build strong baselines by adapting PTv3 to a deterministic encoder-decoder completion model and by instantiating diffusion completion (PCDiff) with both PVCNN and PTv3 denoisers. PCFM with PTv3 is competitive with the deterministic PTv3 baseline and achieves state-of-the-art generative performance across datasets, while requiring substantially fewer sampling steps than diffusion. At the best operating points, PTv3 also yields clear throughput gains, providing up to a 7$\times$ speed-up for PCFM compared to a PVCNN backbone. Finally, we study empirical scaling trends by varying model size and point cardinality, showing consistent gains with higher point resolution and informative trade-offs across model scales.

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11.
arXiv (CS.LG) 2026-06-25

EveLoad: Cognitive Workload Recognition from Event-Based Eye Movements

作者:
Guorui Lu↗Shaohua Guan↗Zhen Xu↗Qinyu Chen↗

arXiv:2606.25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be adjusted according to the user's cognitive state to avoid overload and under-challenge. Emerging extended reality and robot-assisted rehabilitation environments provide controllable training tasks, but they require unobtrusive sensing methods that can capture rapid ocular dynamics during interaction. Existing eye-movement-based cognitive workload recognition methods mainly rely on frame-based eye trackers, which often suffer from limited temporal resolution and degraded robustness under rapid eye movements. In contrast, event cameras provide microsecond-level temporal resolution, high dynamic range and low latency, making them suitable for capturing fine-grained ocular dynamics. Many previous studies rely on free-viewing or similar paradigms, where gaze locations can vary across tasks. As a result, models may learn associations between gaze-location distributions and cognitive workload, rather than workload-related eye movement characteristics themselves. In this work, we introduce EveLoad, which, to the best of our knowledge, is the first event-based eye-movement dataset with graded cognitive workload annotations, collected from 20 healthy participants under spatially constrained and task-driven conditions using a controlled N-back-guided fixation paradigm. Based on this dataset, we establish a benchmark for cognitive workload recognition with six workload levels and propose a learning framework that encodes spatiotemporal event representations. Experimental results show that our approach achieves an average subject-specific accuracy of 96.36% and 96.13% under mixed random split evaluation. These results suggest that event-based eye movements may provide a useful sensing pathway for future workload-aware rehabilitation.

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12.
arXiv (CS.CV) 2026-06-17

Contactless Respiratory Monitoring on Heterogeneous Mobile Robots: A Multimodal Edge-Computing Framework

作者:
Milind Rampure↗Shadman Sakib↗Haley Patel↗Zahid Hasan↗Nirmalya Roy↗

Respiratory-rate (RR) monitoring is a critical component of remote triage and victim assessment in emergency response, disaster recovery, and infectious-disease scenarios, where minimizing physical contact can reduce responder risk and improve operational safety. However, field deployment of contactless RR monitoring remains challenging due to variable illumination, posture changes, platform heterogeneity, and the impracticality of wearable sensors in hazardous environments. In this paper, we present a modality-adaptive contactless RR monitoring framework for heterogeneous mobile robots with onboard edge computing. The proposed system combines brightness-adaptive sensor selection across RGB, thermal, near-infrared (NIR), and low-light cameras, keypoint-guided chest ROI extraction for posture-robust monitoring, and a signal-quality-index (SQI)-based filtering mechanism for reliable respiratory estimation. We implement and evaluate the framework on three robotic platforms spanning quadruped and wheeled locomotion and multiple edge-computing architectures. Experiments conducted across diverse lighting conditions, subject poses, and robot-to-subject distances demonstrate that the framework generalizes across platforms without per-platform algorithmic retuning, while revealing modality-specific operational boundaries. RGB provides the broadest coverage up to 8m, NIR remains effective up to 6m, thermal is reliable only at short range, and low-light sensing supports monitoring in complete darkness up to 8m. Overall, the results demonstrate the feasibility of multimodal contactless RR monitoring on mobile robots and support its use as a foundation for autonomous triage and victim assessment in hazardous search-and-rescue settings.

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13.
arXiv (CS.CL) 2026-06-16

Enhancing LLM Safety Through a Theoretical Minimax Game Lens

作者:
Yihe Deng↗Yu Yang↗Junkai Zhang↗Wei Wang↗Bo Li↗

The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from benign content. While substantial safety datasets are available in English, multilingual safety modeling remains underexplored due to limited open-source safety datasets in other languages. Even within English datasets, safe yet sensitive corner-case content is scarce, leading to shortcut learning by models and non-trivial false-positive rates. To mitigate these issues, we introduce a novel minimax reinforcement learning (RL) framework wherein a data generator and a classifier model co-evolve, facilitating the production of high-quality synthetic multilingual safety data. We theoretically formalize this interaction as a minimax game and rigorously demonstrate convergence to a Nash equilibrium. Empirical evaluations confirm that our synthetic data generation method significantly enhances the classifier model performance, enabling a substantially smaller model to surpass the state-of-the-art by nearly 10% on English benchmarks while achieving 4.5x faster inference speed. These results establish a scalable and efficient methodology for synthetic data generation, advancing the development of safer and more robust multilingual LLM deployments.

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14.
arXiv (CS.LG) 2026-06-16

Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces

作者:
Sadanand Singh↗Allam Reddy↗Manan Chopra↗

arXiv:2606.16961v1 Announce Type: new Abstract: We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidden-cell surface-completion RMSE in the 0.94-1.56 vol-point range across both markets and mask rates 10-50%. The hybrid predictor attains 0.83 vol points at 50% masking against 7.00 for the smile re-fit alone, an eightfold reduction obtained at no additional inference cost. Under structurally-correlated hole patterns that emulate the withdrawal of an entire tenor of strikes, the smile re-fit incurs 9.6-13.1 vol points of error while the learned model remains at 1.5-1.9, isolating a regime in which the generative model is the only viable predictor. Joint training on BTC and ETH improves the in-distribution model on both markets by 9-27% relative to the better-performing single-symbol counterpart, indicating a substantially shared vol-surface manifold across the two largest cryptocurrencies over the observation window. The hybrid is calendar- and butterfly-arbitrage-free at the listed strikes, a property that the parametric smile re-fit alone fails at high mask rates. The per-snapshot reconstruction error of the trained model flags the late-October ETF-anticipation rally and the August $17$, $2023$ flash crash as elevated-error periods without supervision. All training and evaluation infrastructure is released to support reproducible follow-on work.

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15.
arXiv (quant-ph) 2026-06-15

Note on the local calculation of decoherence of quantum superposition in the static black holes

作者:
Ran Li↗Zhong-Xiao Man↗Jin Wang↗

arXiv:2606.14178v1 Announce Type: cross Abstract: We investigate the decoherence of a quantum spatial superposition of a static particle in Schwarzschild and Reissner-Nordstr\"{o}m black holes. By treating the particle as a localized classical source coupled to a quantum scalar field, we reformulate the decoherence process in the Danielson-Satishchandran-Wald (DSW) gedankenexperiment through coherent state generation and derive the local expression for the decoherence functional in terms of the Wightman function. In the long-time limit, the decoherence rate is shown to be characterized by the low-frequency behavior of the Wightman function. We then employ the asymptotic matching method to calculate the analytical expressions of the Wightman functions in the Boulware, Unruh, and Hartle-Hawking vacua. We show that the decoherence behavior depends on the quantum state of the environmental field. While the Boulware vacuum gives vanishing decoherence for a static superposition, the thermal effects associated with Hawking radiation in the Unruh and Hartle-Hawking vacua can induce nonvanishing decoherence.

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16.
arXiv (CS.AI) 2026-06-25

Learning Non-Vacuous Generalization Bounds from Optimization

作者:
Chengli Tan↗Jiangshe Zhang↗Junmin Liu↗Yihong Gong↗

arXiv:2206.04359v3 Announce Type: replace-cross Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compressed nets. In this study, we present a simple yet non-vacuous generalization bound from the optimization perspective. We achieve this goal by leveraging that the hypothesis set accessed by stochastic gradient algorithms is essentially fractal-like and thus can derive a tighter bound over the algorithm-dependent Rademacher complexity. The main argument rests on modeling the discrete-time recursion process via a continuous-time stochastic differential equation driven by fractional Brownian motion. Numerical studies demonstrate that our approach is able to yield plausible generalization guarantees for modern neural networks such as ResNet and Vision Transformer, even when they are trained on a large-scale dataset (e.g. ImageNet-1K).

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17.
arXiv (CS.CV) 2026-06-16

RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

作者:
Haochen Hu↗Yanrui Bin↗Chih-yung Wen↗Bing Wang↗

Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise-level indices, guiding a multi-step denoising process for level-wise supervision. This mechanism prevents low-quality labels from degrading the model while maximizing their utility during training. Furthermore, a Fourier-based refinement network is incorporated to explicitly reconstruct high-frequency components. Extensive evaluations demonstrate that our method consistently outperforms SOTA approaches in restoration quality. The code and pre-trained model will be available once accepted in link.

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18.
arXiv (CS.CV) 2026-06-25

Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data

作者:
Po-Ting Lin↗

Path-traced synthetic stereo data underlie a large fraction of modern disparity-estimation training pipelines. We report a previously unrecognised property of such data: while the Monte Carlo (MC) noise streams of the two cameras are statistically independent, the underlying variance fields – deterministic per-pixel functions of the rendering integrand – are highly correlated once aligned by the ground-truth disparity warp. Across 20 scenes rendered with Mitsuba~3, the warped Pearson correlation reaches $\rho{=}0.754{\pm}0.016$ across 20 scenes at $\mathrm{SPP}{=}512$, and on a representative scene remains essentially invariant ($\rho{=}0.778{\pm}0.001$) over a $16\times$ range of samples per pixel. The effect is strongest in Lambertian regions ($\rho{\approx}0.78$) and substantially weaker in glass ($\rho{\approx}0.30$), as predicted by an integrand decomposition into view-independent and view-dependent components. A residual-shuffle intervention that breaks the cross-view alignment while preserving the clean image degrades the GT cost margin by $33\%$ on non-glass and the variance-based winner-take-all accuracy on glass by $4.3\times$, confirming the structure functions as a matching cue. This signal is unique to MC-rendered data and constitutes a candidate sim-to-real shortcut whose impact on trained networks remains to be quantified.

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19.
medRxiv (Medicine) 2026-06-17

Low-Density Lipoprotein Cholesterol and Dementia Risk: Integrating Mendelian Randomization and Target Trial Emulation Within the Heart-Brain Axis

作者:
Mukumbi↗Liu↗Shi↗Toyli↗Hung↗G.-U↗Chen↗Q.-H↗Sha↗Chiu↗P.-Y↗Zhou↗…

Background: The heart-brain axis links cardiovascular and neurodegenerative disease through shared vascular and inflammatory mechanisms. Although low-density lipoprotein cholesterol (LDL-C) is an established causal factor in atherosclerotic cardiovascular disease (ASCVD), its relationship with dementia remains uncertain, with midlife elevations associated with increased risk but late-life associations often appearing null or inverse. To address this cholesterol paradox, we integrated mendelian randomization (MR) with an active-comparator new-user target trial emulation. Methods: We applied a triangulated causal inference framework integrating two-sample MR with observational target trial emulation. Genetic variants associated with LDL-C were used as instrumental variables to evaluate Alzheimer disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and any dementia (AnyDem), with causal estimates derived using inverse-variance weighted models and sensitivity analyses for heterogeneity and pleiotropy. In parallel, an active-comparator new-user design compared statin versus ezetimibe initiation among adults aged 60 years or older using propensity score (PS) overlap weighting and Cox proportional hazards models to evaluate cardiovascular and dementia outcomes. Results: Genetically predicted LDL-C was associated with increased risk of DLB (OR 1.65, 95% CI 1.30-2.10; p

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20.
arXiv (quant-ph) 2026-06-24

Erasure cost of a quantum process: A thermodynamic meaning of the dynamical min-entropy

作者:
Himanshu Badhani↗Dhanuja GS↗Swati Choudhary↗Vishal Anand↗Siddhartha Das↗

arXiv:2506.05307v5 Announce Type: replace Abstract: The erasure of information is fundamentally an irreversible logical operation, carrying profound consequences for the energetics of computation and information processing. We investigate the thermodynamic costs associated with erasing (and preparing) quantum processes. Specifically, we analyze an arbitrary bipartite unitary gate acting on logical and ancillary input-output systems, where the ancillary input is always initialized in the ground state. We focus on the adversarial erasure cost of the reduced dynamics - that is, the minimal thermodynamic work cost to erase the logical output of the gate for any logical input, assuming full access to the ancilla but no access to any purifying reference of the logical input state. We determine that this adversarial erasure cost is directly proportional to the negative min-entropy of the reduced dynamics, thereby giving the dynamical min-entropy a clear operational meaning. The dynamical min-entropy can take positive and negative values, depending on the underlying quantum dynamics. The negative value of the erasure cost implies that the extraction of thermodynamic work is possible instead of its consumption during the process. A key foundation of this result is the quantum process decoupling theorem, which quantitatively relates the decoupling ability of a process with its min-entropy. This insight bridges thermodynamics, information theory, and the fundamental limits of quantum computation.

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21.
arXiv (CS.LG) 2026-06-24

Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making

作者:
Yue Pei↗Hongming Zhang↗Chao Gao↗Martin M\"uller↗Yingying Zhang↗Mengxiao Zhu↗Hao Sheng↗Ziliang Chen↗Liang Lin↗Haogang Zhu↗

arXiv:2508.16420v3 Announce Type: replace Abstract: Target-conditioned sequence models provide a simple interface for controllable offline decision making, but the requested target return can be an unreliable control signal, especially when the target return lies in underrepresented regions of the dataset. This paper proposes Doctor, a hybrid sequence modeling and reinforced verification framework for controllable target-conditioned offline decision making. Doctor trains a shared masked trajectory Transformer with two complementary objectives: masked trajectory reconstruction for candidate generation and in-sample value learning for action-value verification. At inference time, the model samples multiple nearby target returns, generates candidate actions in parallel, and selects the action whose verified value is closest to the requested target return. We analyze this verifier-guided selection rule and show that its value-level alignment error is bounded by candidate-value coverage around the target return and verifier accuracy. Experiments on D4RL and EpiCare show that Doctor improves target-return alignment under reduced high-return coverage, remains competitive on standard offline return-maximization benchmarks, and enables a single policy to modulate between conservative and aggressive operating points in a simulated clinical decision-making task. These results suggest that reinforced verification can improve the controllability of target-conditioned policies.

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22.
medRxiv (Medicine) 2026-06-22

Building accessible resources to empower communities: the case of the Lupus Mexican Registry

作者:
Martinez↗Sanchez-Aguirre↗Sevilla-Parra↗Olivares-Martinez↗Bravo-Garcia↗Hernandez-Ledesma↗A. L↗Aguilar↗L. A↗Dominguez-Frausto↗C. A↗Garcia↗…

Motivation: Although SLE data in Latin America is increasing, clinical datasets remain difficult to access and interpret, highlighting the need for accessible tools that support data-driven precision medicine, citizen science, and public health initiatives. Results: We developed a user-friendly platform that enables us to explore LupusRGMX data through interactive queries, report generation, statistical modeling, and comprehensive insights. This resource supports community-oriented research, improves the visibility of underrepresented populations in lupus research, and provides a useful tool to enhance data accessibility. Availability and implementation: Developed in R using Shiny and bslib for interactive visualization and interface design. Available at https://github.com/NeuroGenomicsMX/Lupus_App_2.0 and https://lupusrgmx.liigh.unam.mx/shiny/lupus/

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23.
arXiv (CS.CV) 2026-06-18

Native Active Perception as Reasoning for Omni-Modal Understanding

作者:
Zhenghao Xing↗Ruiyang Xu↗Yuxuan Wang↗Jinzheng He↗Ziyang Ma↗Qize Yang↗Yunfei Chu↗Jin Xu↗Junyang Lin↗Chi-Wing Fu↗Pheng-Ann Heng↗

Passive models for long video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to grow with video duration. Although interactive frameworks have emerged, they often rely on global pre-scanning, and their context cost still scales with video length. We propose OmniAgent, the first native omni-modal agent that formulates video understanding as a POMDP-based iterative Observation-Thought-Action cycle. OmniAgent executes on-demand actions to selectively distill audio-visual cues into a persistent textual memory, effectively decoupling reasoning complexity from raw video duration. To operationalize this, we introduce (1) Agentic Supervised Fine-Tuning to bootstrap native active perception via best-of-N trajectory synthesis with dual-stage quality control, and (2) Agentic Reinforcement Learning with TAURA (Turn-aware Adaptive Uncertainty Rescaled Advantage), which leverages turn-level entropy to steer credit assignment toward pivotal discovery turns. Crucially, OmniAgent exhibits positive test-time scaling, where performance improves as the number of reasoning turns increases, validating the efficacy of active perception. Empirical results across ten benchmarks (e.g., VideoMME, LVBench) demonstrate that OmniAgent achieves state-of-the-art performance among open-source models. Notably, on LVBench, our 7B agent outperforms the 10$\times$ larger Qwen2.5-VL-72B (50.5% vs. 47.3%).

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24.
arXiv (CS.AI) 2026-06-16

Co-Scraper: query-aware DOM Pruning and Reusable Scraper Synthesis for Lightweight Web Data Extraction

作者:
Shoupeng Wang↗Jiantao Qiu↗Wuyang Zhang↗Conghui He↗

arXiv:2606.14821v1 Announce Type: cross Abstract: The abundant and heterogeneous nature of web content necessitates automated information extraction, and generating scrapers that can be reused across similar web pages offers an effective solution for scalable data extraction. In this work, we propose Co-Scraper, a two-stage framework capable of handling the hierarchical complexity of long HTML documents. By integrating a query-aware DOM pruning mechanism with stable extraction strategy induction, Co-Scraper can effectively transforms web content into executable programmatic wrappers using a fine-tuned Qwen3-8B model. On the test set of SWDE, Co-Scraper achieves state-of-the-art performance with an F1 score of 94.78% and a reuse success rate of 90.39%. This framework significantly enhances the accuracy and resilience of data extraction, providing a highly efficient approach for web data acquisition tasks.

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25.
arXiv (CS.AI) 2026-06-25

Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models

作者:
Xinyu Lian↗Walid Krichene↗Beichen Huang↗Masahiro Tanaka↗Olatunji Ruwase↗Li Zhang↗Minjia Zhang↗

arXiv:2606.25519v1 Announce Type: new Abstract: Quantization is widely used to reduce the inference cost of large language models, but its effect on reasoning models is not fully captured by final-answer accuracy or per-token latency. We show that low-bit post-training quantization can introduce a hidden test-time compute cost: quantized reasoning models often generate longer chains of thought even when they still answer correctly. Across mathematical reasoning, code generation, scientific question answering, and agentic tool-use benchmarks, we find that INT4/INT3 quantization can preserve accuracy but increase reasoning-token usage, offsetting the expected per-token speedup. To measure this effect, we introduce the CoT Token Inflation Ratio, which compares reasoning length between quantized and full-precision models averaged across all evaluation benchmarks. We further show that token inflation is accompanied by behavioral changes in the reasoning trace, including more intermediate steps and greater semantic repetition. These changes translate into measurable end-to-end real-world serving penalties. Finally, we evaluate mitigation strategies and find that prompting and decoding-time sampling offer inconsistent accuracy-length trade-offs, while quantization-aware training shows more promise in reducing both accuracy degradation and token inflation. Our results suggest that reasoning-token usage should be reported alongside accuracy when evaluating quantized reasoning models.

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