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01.
medRxiv (Medicine) 2026-06-22

How knowledge shapes community stigma and social support for women seeking abortion in the Democratic Republic of Congo: A cross-sectional study.

Authors:
Etsou↗Kanda↗Ngwanza↗Mpoyi↗Bokamba↗Mulunda↗J.-C↗Lobo↗

Background The Democratic Republic of Congo (DRC) bears one of the highest maternal mortality ratios globally (746 per 100,000 live births), with nearly 11% of deaths attributable to complications of unsafe abortion. Despite ratification of the Maputo Protocol and related national policies, access to safe abortion remains limited, largely due to entrenched stigma. Social support, encompassing emotional, informational, and instrumental assistance, is critical in shaping womens abortion-seeking behaviors and health outcomes. This study examines the influence of community-level knowledge on stigma and social support for women seeking abortion care. Methods A cross-sectional survey was conducted from May 2024 to June 2024 among 1,715 adults in Kinshasa and North Kivu provinces. Analyses focused on a sub-sample of 574 respondents reporting familiarity with women who had undergone abortion. Structural Equation Modeling (SEM) was applied to estimate direct and indirect pathways linking community knowledge, stigma, and social support. Results Two core knowledge indicators, recognition of abortion as a safe medical procedure and awareness of legal conditions for access, were significantly associated with outcomes. A one-unit increase in knowledge corresponded to a 0.39-point increase in social support and a 0.19-point reduction in stigma. Enhanced knowledge promoted empathetic attitudes, reinforced practical support, and mitigated moralizing judgments toward women seeking abortion. Conclusions Strengthening community knowledge emerges as a strategic lever to reduce abortion-related stigma and enhance social support in the DRC. These findings underscore the importance of integrating stigma-reduction and knowledge-enhancement interventions into reproductive health programs to improve womens access to safe and dignified abortion care.

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02.
arXiv (quant-ph) 2026-06-17

Universal Design and Physical Applications of Non-Uniform Cellular Automata on Translationally Invariant Lattices

Authors:
Xiang-You Huang↗Jie-Yu Zhang↗Peng Ye↗

arXiv:2605.13379v2 Announce Type: replace Abstract: Motivated by recent theoretical and experimental advances, hyperbolic lattices have emerged as a paradigmatic setting in which geometry becomes an active organizing principle of quantum systems. Their negative curvature, exponential volume growth, and non-Abelian translation symmetry make them fundamentally distinct from Euclidean lattices and give rise to rich geometry-dependent physics, but also hinder the direct application of well-established analytical and computational approaches originally developed for physical systems defined on Euclidean lattices. To establish a unified framework for geometry-dependent physics on Euclidean and hyperbolic lattices, we develop higher-order non-uniform cellular automata (NUCA) as a local-to-global construction for translationally invariant regular lattices. This construction derives geometry-dependent update rules through a lattice-deforming procedure that embeds hyperbolic lattices into a Euclidean square lattice, thereby encoding hyperbolic geometry while preserving physical locality. It thus provides a systematic route toward quantum and classical physics on hyperbolic lattices. We demonstrate the framework in three applications ranging from quantum many-body physics to non-equilibrium statistical physics. First, on the hyperbolic $\{5,4\}$ lattice, a linear NUCA generates exactly solvable subsystem symmetry-protected topological (SSPT) models and spontaneous subsystem symmetry-breaking models. Second, as a quantum generalization, we construct non-uniform Clifford quantum cellular automata (CQCA) for the hyperbolic cluster state. Third, we formulate a probabilistic NUCA for directed percolation (DP) on the hyperbolic lattice.

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03.
arXiv (CS.AI) 2026-06-17

DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

Authors:
Dake Bu↗Wei Huang↗Andi Han↗Hau-San Wong↗Qingfu Zhang↗Taiji Suzuki↗Atsushi Nitanda↗

arXiv:2604.24357v2 Announce Type: replace-cross Abstract: Diffusion language models generate without a fixed left-to-right order, leaving token ordering as a central algorithmic choice. Existing systems mainly use random masking or confidence-driven ordering, which respectively suffer from train–test mismatch and myopic exploration. We introduce DPRM (Doob -transform Process Reward Model), a plug-in token-ordering module that keeps the host architecture, denoising objective and supervision unchanged, and modifies only the ordering policy. DPRM starts from confidence-driven ordering and gradually shifts to process-reward-guided ordering through online estimates. We characterize the exact DPRM policy as a reward-tilted Gibbs reveal law, prove convergence of its stagewise Soft-BoN approximation, show that the online bucketized controller tracks the exact DPRM score at empirical-Bernstein rates, and establish a sample-complexity advantage under tractable optimization assumptions. Across nine hosts covering language reasoning, test-time scaling, protein, single-cell, molecular, DNA, text-to-image generation, and VQA, DPRM order variants improve several language, DNA, and multimodal settings while also identifying boundary cases where confidence-only ordering or task-specific utilities are preferable. Code is available at: https://github.com/DakeBU/DPRM-DLLM

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04.
arXiv (CS.LG) 2026-06-24

WiFi-Based People Counting Using Beam-Steerable Antennas: A Test-bed Study

Authors:
Riccardo Bersan↗Anay Ajit Deshpande↗Sanaz Kianoush↗Daniele Piazza↗Stefano Savazzi↗

arXiv:2606.23710v1 Announce Type: cross Abstract: Ubiquitous perception through RF signals is a pivotal opportunity for future technology: it enables personalized services such as smart living, remote healthcare, automated logistics or interaction through free-space gestures. The ubiquity of Wi-Fi and cellular networks presents a promising platform for the development of innovative sensing tools. Future standards will also introduce dedicated sensing features which, for example, will allow routers to work as frequency modulated continuous wave radios targeting radar applications. Most of the current chip designs support ad-hoc firmware for CSI extraction with MIMO arrangements of the transmitter (TX) and receiver (RX) antennas and OFDM subcarriers. The CSI describes the phase shift and amplitude attenuation of multiple propagation paths on each subcarrier. The latest IEEE 802.11be standard (Wi-Fi 7) offers a wider subcarrier bandwidth of 160MHz (up to 320MHz), providing at least 120 usable pilot subcarriers for CSI or CIR estimation. Additionally, Wi-Fi signals have been recently exploited to track daily human movements and behaviors, while Wi-Fi signal variations have been shown to differ between different people and can consequently be used for their re-identification.

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05.
medRxiv (Medicine) 2026-06-23

Isolation And Characterization Of Bacteria Associated With Urethritis In Women Within Child Bearing Age Attending Local African Health Clinics

Authors:
James↗S. C↗F. O↗

Background: Urethritis in women of childbearing age constitutes a significant but underreported burden of reproductive morbidity in Sub-Saharan Africa, where diagnostic constraints often necessitate suboptimal syndromic management. Methods: To identify the localized etiological profile, mid-stream urine and urethral swab specimens were prospectively collected from symptomatic women attending local clinics, subjected to standard microbiological culture, and characterized using rigorous phenotypic and biochemical diagnostic protocols. Results: Microbiological analysis successfully isolated a high prevalence of both Gram-negative and Gram-positive uropathogens, predominantly Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, demonstrating distinct phenotypic traits characteristic of the regional microbial ecology. Conclusion: The pronounced isolation of these specific bacterial agents highlights the critical inadequacy of generalized empirical treatments and underscores the urgent need for tailored diagnostic criteria in resource-limited African healthcare settings.

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

Plug-and-Play image restoration with Stochastic deNOising REgularization

Authors:
Marien Renaud↗Jean Prost↗Arthur Leclaire↗Nicolas Papadakis↗

Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images. We propose a new PnP framework, called Stochastic deNOising REgularization (SNORE), which applies the denoiser only on images with noise of the adequate level. It is based on an explicit stochastic regularization, which leads to a stochastic gradient descent algorithm to solve ill-posed inverse problems. A convergence analysis of this algorithm and its annealing extension is provided. Experimentally, we prove that SNORE is competitive with respect to state-of-the-art methods on deblurring and inpainting tasks, both quantitatively and qualitatively.

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07.
medRxiv (Medicine) 2026-06-24

Food insecurity, caloric intake and nutritional status among children under 5 years old: a predictive modelling analysis of the MAL-ED multi-country cohort

Authors:
Checchi↗Ferguson↗Hamad↗Ouchtar↗Ratnayake↗Singh↗Tanvir↗van Zandvoort↗Dahab↗

Background For children at risk of acute malnutrition, being able to predict and forecast dietary intakes and/or nutritional evolution would support decision-making, particularly in crisis settings where ground data collection is unfeasible or scant. We explored whether statistical models could offer accurate predictions of caloric intake or anthropometric (weight-for-height Z score, WHZ) changes, given intake, household food insecurity and other plausible predictors. Methods We reanalysed data from the Malnutrition and Enteric Disease (MAL-ED) multi-country (Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa, Tanzania) birth cohort (2009-2014), which consistently tracked household food insecurity experience, dietary intake, anthropometry, infectious disease symptoms, breastfeeding and other variables among children 9 to 35 months old. We quantified the performance on cross-validation of three models: (M1) change in WHZ as a function of household food insecurity; (M2) change in WHZ as a function of caloric intake; (M3) caloric intake as a function of household food insecurity. We compared random forests, lasso regressions, additive models and generalised boosted regressions. All models included age, sex, birth weight, urban versus rural residence, breastfeeding status and the longitudinal prevalence of diarrhoea, acute respiratory infection and fever as additional predictors. Results Altogether, M1, M2 and M3 leveraged 2957, 23,651 and 2013 longitudinal child observations, respectively. Both at country and individual level, there was low correlation among the key variables of interest. All three models featured low performance and moderate to extreme regression dilution, even when fitted to each country cohort separately. Discussion This secondary analysis based on data from a rigorous observational study suggests that statistical prediction of key variables along the causal pathway to childhood acute malnutrition may not be feasible. These negative findings may in part be explained by error in predictor measurement and the narrow range of both predictor and outcome values in the MAL-ED cohort, relative to the more extreme scenarios common to crisis settings. They also imply that mechanistic models requiring caloric intake as an input cannot rely on a statistical shortcut of this kind and must instead depend on empirical data or scenario assumptions.

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08.
arXiv (CS.LG) 2026-06-15

Gradient boosting for extremes: sampling theory and application to insurance

Authors:
St\'ephane Lhaut↗Olivier Lopez↗

arXiv:2606.14268v1 Announce Type: cross Abstract: We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions in the context of Peaks-over-Threshold modeling. After an orthogonal reparametrization of the GP likelihood that diagonalizes its Fisher information matrix, we cast the estimation problem within the Empirical Risk Minimization (ERM) framework and derive non-asymptotic error bounds for the boosting estimator. Our analysis accounts for three distinct sources of error in the process: statistical fluctuations, the approximation bias inherent to the asymptotic nature of the GP model-controlled under second-order regular variation-and the approximation error associated with the finite number of boosting iterates, making explicit the resulting bias-variance trade-off. We illustrate the practical benefits of the reparametrization through simulations, showing that it significantly reduces gradient correlation during training and improves convergence stability. The methodology is applied to a medical malpractice insurance dataset from the Texas Department of Insurance, comprising over 18 000 closed claims. The gradient boosting approach yields a good fit for the tail of settlement cost distributions and reveals that the number of days to settlement is the dominant predictor of tail heaviness, consistent with earlier findings in the reserving literature.

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09.
arXiv (CS.CV) 2026-06-18

Generalized Kullback-Leibler Divergence Loss

Authors:
Jiequan Cui↗Beier Zhu↗Qingshan Xu↗Zhuotao Tian↗Xiaojuan Qi↗Bei Yu↗Hanwang Zhang↗Richang Hong↗

In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE) loss and (2) a Cross-Entropy loss incorporating soft labels. Thanks to the decoupled structure of DKL loss, we have identified two areas for improvement. Firstly, we address the limitation of KL loss in scenarios like knowledge distillation by breaking its asymmetric optimization property along with a smoother weight function. This modification effectively alleviates convergence challenges in optimization, particularly for classes with high predicted scores in soft labels. Secondly, we introduce class-wise global information into KL/DKL to reduce bias arising from individual samples. With these two enhancements, we derive the Generalized Kullback-Leibler (GKL) Divergence loss and evaluate its effectiveness by conducting experiments on CIFAR-10/100, ImageNet, and vision-language datasets, focusing on adversarial training, and knowledge distillation tasks. Specifically, we achieve new state-of-the-art adversarial robustness on the public leaderboard – RobustBench and competitive knowledge distillation performance across CIFAR/ImageNet models and CLIP models, demonstrating the substantial practical merits. Our code is available at https://github.com/jiequancui/DKL.

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10.
bioRxiv (Bioinfo) 2026-06-19

Morpho-FM: spatial molecular reconstruction from routine H&E histology using transcriptomic foundation-model priors

Authors:
Huang↗J.-J↗Feng↗Qu↗L.-H↗Zheng↗L.-L↗

Routine haematoxylin and eosin (H&E) histology captures tissue architecture at clinical scale, but lacks a direct molecular readout of the transcriptional programmes that organise tumour epithelium, stroma, vasculature and immune compartments. Spatial transcriptomics provides this context, yet cost, workflow complexity and sparse sampling limit routine use. Most existing histology-to-expression models are trained de novo on small paired cohorts and therefore remain weakly constrained when extrapolating from sparse measurements to dense, tissue-wide molecular maps. Here we introduce Morpho-FM, a weakly supervised framework that predicts spatial gene expression from routine H&E whole-slide images by conditioning a pretrained single-cell transcriptomic foundation-model prior on local histological neighbourhoods. A lightweight morphology-to-transcriptome adapter maps cached whole-slide histology features into a transcriptomic decoder, enabling prediction at measured locations, dense full-section reconstruction, and re-aggregation to the original measurement support. Across harmonized prostate cancer benchmarks, Morpho-FM achieved the strongest overall performance among five representative methods, reaching mean per-gene Pearson correlations of 0.286 in rotating single-slide evaluation and 0.298 in multi-slide held-out validation. The framework reproduced this advantage across kidney cancer sections, achieved a mean correlation of 0.210 across 56 directed single-slide evaluations and retained measurable predictive signal after external transfer to clear-cell renal cell carcinoma sections. Controlled ablation analyses identified pretrained transcriptomic initialization as a reproducible source of performance gain exceeding that attributable to changes in the histology feature backbone. Beyond predictive accuracy benchmarks, Morpho-FM recovered ERBB2-enriched tumour compartments, boundary-associated molecular gradients, and annotation-aligned tissue domains across Xenium and HER2ST breast cancer datasets. Together, these results support transcriptomic foundation-model priors as an effective constraint for morphology-conditioned molecular decoding and demonstrate the potential of Morpho-FM to extend spatial transcriptomic insight across routine pathology sections.

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11.
bioRxiv (Bioinfo) 2026-06-16

AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software

Authors:
Xie↗Cheng↗Cai↗Shireman↗Kendziorski↗

Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present AutoZyme, an agentic framework for scientific software optimization. Given a target function, AutoZyme builds benchmarks, identifies bottlenecks, and iteratively tests code changes, retaining only those that improve runtime while preserving output. We evaluated AutoZyme on 45 functions, improving runtime without substantial memory increases in over 95% of cases considered. Across 38 functions from Seurat, Scanpy and related packages in genomics and bioinformatics, AutoZyme reduced runtime by a median of 8.52-fold, with the largest reductions exceeding 676-fold. The optimized functions are distributed through AutoZyme-Library as drop-in replacements for existing analysis pipelines. We also release AutoZyme as a reusable framework for optimizing additional user-specified packages and functions.

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12.
arXiv (CS.CV) 2026-06-16

RGFVR: Reference-Guided Face Video Restoration with Flow Matching

Authors:
Cem Eteke↗Batuhan Tosun↗Eckehard Steinbach↗

Face video restoration from degraded observations is challenging, as it requires simultaneously recovering visual fidelity, temporal consistency, and subject identity. Existing approaches are often either reference-free, which can lead to identity loss when person-specific facial details are lost, or subject-specific, which limits generalization to unseen identities. We propose a subject-agnostic, reference-guided framework for identity-preserving face video restoration. Our method introduces bimodal perceptual-descriptive identity conditioning into a pretrained flow-based text-to-video generator and employs a two-stage training strategy to strengthen identity guidance during restoration. Experiments show that our approach improves restoration fidelity, temporal consistency, and identity preservation, achieving superior performance under challenging video degradations, including downsampling, blur, noise, and compression artifacts. The code is available under: https://github.com/batuhanntosun/RG-FVR.

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13.
arXiv (CS.AI) 2026-06-17

TRACE: Learning to Compute on Circuit Graphs

Authors:
Ziyang Zheng↗Jiaying Zhu↗Jingyi Zhou↗Qiang Xu↗

arXiv:2509.21886v3 Announce Type: replace Abstract: Learning to compute, the ability to model the functional behavior of a circuit graph, is a fundamental challenge for graph representation learning. Yet, the dominant paradigm is architecturally mismatched for this task. This flawed assumption, central to mainstream message passing neural networks (MPNNs) and their conventional Transformer-based counterparts, prevents models from capturing the position-aware, hierarchical nature of computation. To resolve this, we introduce TRACE, a new paradigm built on an architecturally sound backbone and a principled learning objective. First, TRACE employs a Hierarchical Transformer that mirrors the step-by-step flow of computation, providing a faithful architectural backbone that replaces the flawed permutation-invariant aggregation. Second, we introduce function shift learning, a novel objective that decouples the learning problem. Instead of predicting the complex global function directly, our model is trained to predict only the function shift, the discrepancy between the true global function and a simple local approximation that assumes input independence. We validate this paradigm on various circuits modalities, including Register Transfer Level graphs, And-Inverter Graphs and post-mapping netlists. Across a comprehensive suite of benchmarks, TRACE substantially outperforms all prior architectures. These results demonstrate that our architecturally-aligned backbone and decoupled learning objective form a more robust paradigm for the fundamental challenge of learning the functional behavior of a circuit graph.

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14.
arXiv (CS.CV) 2026-06-18

FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization

Authors:
Quanjian Song↗Yefeng Shen↗Mengting Chen↗Hao Sun↗Jinsong Lan↗Xiaoyong Zhu↗Bo Zheng↗Liujuan Cao↗

Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot support low-latency and interactive garment control, which is crucial for applications such as e-commerce and content creation. This paper studies how to achieve interactive multi-garment video customization while preserving motion coherence using only single-garment video data. We present FashionChameleon, a real-time and interactive framework for human-garment customization in autoregressive video generation, where users can interactively switch garment during generation. FashionChameleon consists of three key techniques: (i) Instead of training on multi-garment video data, we train a Teacher Model with In-Context Learning on a single reference-garment pair. By retaining the image-to-video training paradigm while enforcing a mismatch between the reference and garment image, the model is encouraged to implicitly preserve coherence during single-garment switching. (ii) To achieve consistency and efficiency during generation, we introduce Streaming Distillation with In-Context Learning, which fine-tunes the model with in-context teacher forcing and improves extrapolation consistency via gradient-reweighted distribution matching distillation. (iii) To extend the model for interactive multi-garment video customization, we propose Training-Free KV Cache Rescheduling, which includes garment KV refresh, historical KV withdraw, and reference KV disentangle to achieve garment switching while preserving motion coherence. Our FashionChameleon uniquely supports interactive customization and consistent long-video extrapolation, while achieving real-time generation at 23.8 FPS on a single GPU, 30-180$\times$ faster than existing baselines.

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15.
arXiv (CS.AI) 2026-06-24

Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

Authors:
Jinming Yang↗Xinyu Jiang↗Xinshan Jiao↗Xinping Zhang↗

arXiv:2603.20750v2 Announce Type: replace Abstract: Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network. We study whether graph-local evidence and credibility-weighted communication can generate persistent distortions in perceived academic standing, even when agents repeatedly receive objective performance signals. We introduce a data-constrained multi-agent framework in which LLM agents operate through individualized subjective graphs that determine peer visibility, evidence access, and interaction opportunities. Agents exchange uncertainty-annotated assessments, evaluate message credibility, and maintain explicit Gaussian belief states updated through Bayesian fusion. We evaluate the framework on 12 middle-school classrooms comprising 482 students, using questionnaire-derived social information and six consecutive examinations. On the Social-Observed subset (n=419), collective ranking error increases from 0.066 \pm 0.008 to 0.124 \pm 0.009 across six epochs despite repeated exam-based anchoring. Ablations associate individualized visibility and LLM-based trust gating with more stable long-horizon behavior, while constrained retrieval primarily safeguards against global-information leakage. Compared with evaluated DeGroot configurations, the proposed framework achieves lower final ranking error; those DeGroot configurations exhibit near-zero terminal opinion diversity. These findings establish subjective-graph LLM agents as a mechanism-oriented framework for data-constrained simulated social perception. Code is available at https://anonymous.4open.science/r/Rashomonomon-0126.

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16.
arXiv (quant-ph) 2026-06-24

Efficient Graph State Purification with Factorized Graph-Preserving Operations across Local Clifford Orbits

Authors:
Mingyuan Wang↗Guus Avis↗Kenneth Goodenough↗Stefan Krastanov↗

arXiv:2606.23809v1 Announce Type: new Abstract: Graph states form a broad class of multipartite entangled states underlying measurement-based quantum computation, quantum networks, and stabilizer codes. However, systematic entanglement distillation for arbitrary graph states remains challenging because the circuit design space grows rapidly with the number of parties. We introduce a group of Clifford operations that we call "factorized graph-preserving". It enables us to efficiently enumerate and optimize graph-state purification circuits at finite size for realistic noisy hardware. These operations map products of graph-basis states to products of graph-basis states, so their action can be represented as permutations of graph-basis labels. Moreover, this useful gate set admits a compact factorized description determined by simple graph-theoretic features. This structure also allows, after some initial cached precomputation, drastically lower computational complexity for simulating a gate. We further organize these operations over local-complementation (LC) orbits using minimum-edge representatives (MERs), which let us design purification circuits that apply to all locally equivalent graph states (up to a basis change). Using this framework, we optimize noisy finite-size multipartite distillation circuits for several graph-state families. Numerical results show that the resulting graph-preserving circuits can outperform standard recurrence-based purification protocols under realistic gate and measurement noise. Our results establish LC-orbit structure and factorized graph-preserving operations as practical tools for scalable, topology-aware and hardware-constrained graph-state distillation protocol design. Our work can also be interpreted as a graph-based heuristic for finding transversal gates.

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

Constitutional Value Potentials: reading and steering internal priority margins in language models

Authors:
Tong Che↗Rui Wu↗

arXiv:2606.15420v1 Announce Type: cross Abstract: A constitution tells a language model what to value, but little tells us whether it does. Adherence is judged from outputs, and output evidence is most fragile on value conflicts, where what matters is not which value a model mentions but which one it is willing to sacrifice. We provide evidence that this arbitration can be read from activations in a structured margin readout. We introduce Constitutional Value Potentials (CVP). For each value we learn a scalar potential from the hidden state: an internal pressure to preserve that value, supervised not by the prompt but by an independent judge's verdict on which value the model's own response actually preserved. The signed difference of two potentials is a priority margin. A constitutional clause becomes the claim that a margin stays positive, and a single monitor score flags when it does not. The monitor predicts conflict violations with AUROC up to 0.95, beats a strong hidden-state probe, and generalizes to held-out synthetic conflicts across three Qwen2.5 scales. The signal appears as the answer begins, from the prompt tail and first response token. Read this early, the same signal reveals whether an adversarial priority hack has actually pushed the model toward a violation, rather than only whether the prompt looks adversarial. The same directions also support intervention tests: under selected steering settings, moving along a value direction shifts judged trade-offs in the intended direction. Together, these results suggest that some constitution-relevant priorities are accessible as activation-space margins, rather than only as output behavior.

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18.
arXiv (CS.AI) 2026-06-18

QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI

Authors:
Dongping Liu↗Aoyu Zhang↗Luyao Zhang↗

arXiv:2605.27729v2 Announce Type: cross Abstract: The 2024-2025 Nobel and Turing awards recognised AI and quantum science simultaneously. Yet no deployed system has brought these streams together for the public. This paper presents QSignAI, a production-deployed platform demonstrating a bidirectional AI-quantum relationship in a real-time event participation system. We address three questions: can quantum-randomness generation via a two-source extractor be embedded in an AI-driven social platform with acceptable latency; can an AI bot make quantum phenomena perceptually legible to general audiences; and does the combined system work in practice? A conversational bot routes each participant's first message through a quantum pipeline comprising a Toeplitz two-source extractor over independent single-qubit Hadamard measurements on SV1 and DM1 simulators, plus a 2-qubit Bell state, producing a unique quantum-randomness-seeded identity signature per participant. The first two questions are answered through system architecture and qualitative deployment evidence from live events; the third through successful production deployment. The current deployment uses cloud quantum simulators; physical QPU randomness is the near-term extension. Measurable benchmarks are identified as priority future work.

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19.
arXiv (quant-ph) 2026-06-24

Quantum Entanglement Halves the Oblivious Update Bandwidth

Authors:
Sagar Dubey↗

arXiv:2605.19248v2 Announce Type: replace Abstract: We consider $(n,k)$ MDS-coded distributed storage over $\mathbb{F}_q$ with per-node storage $\alpha$ symbols. For the oblivious update problem, where a single message symbol changes and neither helpers nor the stale node know which, the classical lower bound is $\alpha k \log_2 q$ bits. We prove that when the $k$ contacted helpers share prior quantum entanglement, the update bandwidth is $\lceil \alpha/2 \rceil \cdot k \log_2 q$ bits-equivalent, a factor approaching 2 reduction. For $\alpha = 2$, a $[[k, k-2]]_q$ CSS code achieves bandwidth $k \log_2 q$ with one qudit per helper. For general $\alpha$, a $[[\lceil \alpha/2 \rceil k, \lceil \alpha/2 \rceil k - \alpha]]_q$ CSS code achieves the bound with $\lceil \alpha/2 \rceil$ qudits per helper. The matching converse uses the superdense coding bound: the stale node holds all transmitted qudits and hence the entangled partners, so each helper's channel supports at most $D^2$ distinguishable signals for dimension $D$. The result holds for all $(n,k)$ pairs with sufficiently large prime $q$.

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20.
arXiv (CS.LG) 2026-06-16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

Authors:
Yi Zhao↗Aidan Scannell↗Wenshuai Zhao↗Yuxin Hou↗Tianyu Cui↗Le Chen↗Dieter B\"uchler↗Arno Solin↗Juho Kannala↗Joni Pajarinen↗

arXiv:2502.19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL). This paper expands the pool of usable data for offline-to-online RL by leveraging abundant non-curated data that is reward-free, of mixed quality, and collected across multiple embodiments. Although learning a world model appears promising for utilizing such data, we find that naive fine-tuning fails to accelerate RL training on many tasks. Through careful investigation, we attribute this failure to the distributional shift between offline and online data during fine-tuning. To address this issue and effectively use the offline data, we propose two techniques: i) experience rehearsal and ii) execution guidance. With these modifications, the non-curated offline data substantially improves RL's sample efficiency. Under limited sample budgets, our method achieves nearly twice the aggregate score of learning-from-scratch baselines across 72 visuomotor tasks spanning 6 embodiments. On challenging tasks such as locomotion and robotic manipulation, it outperforms prior methods that utilize offline data by a decent margin.

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21.
arXiv (CS.LG) 2026-06-11

From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features

Authors:
Juliette Murris↗Bernadette Stolz↗Karsten Borgwardt↗

arXiv:2606.11911v1 Announce Type: cross Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical tools developed for comparing them have largely evolved separately from those used for downstream prediction. We introduce STRAND (Survival Topological Representation ANalysis of Diagrams), which treats (collections of) PDs as survival data: each topological feature with persistence value $p = d - b$ is a fully observed time-to-event, and the persistence survival function $S(t) = \mathbb{P}(p > t)$ is the central object for comparing diagrams. From this single representation we derive (i) a non-parametric two-sample test with calibrated Type I error and high power from a small number of diagrams; (ii) interpretable effect sizes; and (iii) a 1-Wasserstein-stable feature vector for downstream machine learning. We validate calibration and power on synthetic manifolds with controlled topology, demonstrate competitive vectorisation across 14 graph and 3D point cloud benchmarks, and apply the method to study functional brain connectivity in fMRI/neuroscience data. To our knowledge, STRAND is the first method to provide hypothesis testing and vectorisation for persistence diagrams from a single coherent and interpretable representation.

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22.
arXiv (CS.CL) 2026-06-11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

Authors:
Jingpei Wu↗Xiao Han↗Weixiang Shen↗Boer Zhang↗Zifeng Ding↗Volker Tresp↗

Visual question answering increasingly requires multi-step reasoning. Recent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Optimization (GRPO) can improve multimodal reasoning, but most approaches rely on sparse outcome-only rewards. As a result, they struggle to tell whether an incorrect answer comes from a small mistake late in the reasoning or from an unhelpful trajectory from the start. A common solution is to train a process reward model (PRM) for step-level supervision, but this typically requires large-scale high-quality chain-of-thought annotations and additional training cost. We propose ProcessThinker, a practical post-training pipeline that provides step-level process rewards without training an explicit PRM. ProcessThinker first rewrites reasoning traces into a step-tagged format for cold-start supervised fine-tuning, then applies GRPO with a standard format reward and our rollout-based process reward. Concretely, for each intermediate step, we sample multiple continuations from that step and use the empirical success rate (final-answer verification) as the step reward. This gives dense credit assignment and encourages reasoning steps that more reliably support a correct conclusion, helping reduce inconsistent or self-contradictory progress across steps – a key issue in logical reasoning. Across four challenging video benchmarks (Video-MMMU, MMVU, VideoMathQA, and LongVideoBench), ProcessThinker consistently improves over the baseline model Qwen3-VL-8B-Instruct

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23.
arXiv (CS.AI) 2026-06-17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

Authors:
Umer Siddique↗Peilang Li↗Yongcan Cao↗

arXiv:2606.18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives. While single-policy MORL methods can learn fair policies for fixed user preferences using welfare functions such as the generalized Gini welfare function (GGF), they fail to provide the diverse set of policies necessary for dynamic or unknown user preferences. To address this limitation, we formalize the fair optimization problem in multi-policy MORL, where the goal is to learn a set of Pareto-optimal policies that ensure fairness across all possible user preferences. Our key technical contributions are threefold: (1) We show that for concave, piecewise-linear welfare functions (e.g., GGF), fair policies remain in the convex coverage set (CCS), which is an approximated Pareto front for linear scalarization. (2) We demonstrate that non-stationary policies, augmented with accrued reward histories, and stochastic policies improve fairness by dynamically adapting to historical inequities. (3) We propose three novel algorithms, which include integrating GGF with multi-policy multi-objective Q-Learning (MOQL), state-augmented multi-policy MOQL for learning non-statoinary policies, and its novel extension for learning stochastic policies. We evaluate our algorithms across various domains and compare our methods against the state-of-the-art MORL baselines. The empirical results show that our methods learn a set of fair policies that accommodate different user preferences.

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

Shift-and-Sum Quantization for Visual Autoregressive Models

Authors:
Jaehyeon Moon↗Bumsub Ham↗

Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains relatively unexplored. We identify two key challenges for applying PTQ to VAR: (i) large reconstruction errors in attention-value products, especially at coarse scales where high attention scores occur more frequently; and (ii) a discrepancy between the sampling frequencies of codebook entries and their predicted probabilities due to limited calibration data. To address these challenges, we propose a PTQ framework tailored for VAR. First, we introduce a shift-and-sum quantization method that reduces reconstruction errors by aggregating quantized results from symmetrically shifted duplicates of value tokens. Second, we present a resampling strategy for calibration data that aligns sampling frequencies of codebook entries with their predicted probabilities. Experiments on class-conditional image generation, inpainting, outpainting, and class-conditional editing show consistent improvements across VAR architectures, establishing a new state of the art in PTQ for VAR.

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25.
arXiv (quant-ph) 2026-06-19

Solving Nonequilibrium Dynamics via Influence Matrix Bootstrap: Floquet-PXP Model

Authors:
Xiao-Yang Yang↗He-Ran Wang↗Zhong Wang↗

arXiv:2606.19430v1 Announce Type: new Abstract: Studies of integrable systems have profoundly deepened the fundamental understanding of quantum many-body physics. While equilibrium properties such as ground states and thermodynamics can often be characterized efficiently, accurately characterizing nonequilibrium integrable dynamics remains a significant challenge. Here, we address this problem in the "Rule 201" quantum cellular automaton, an integrable Trotterization of the PXP Hamiltonian. Using the tensor-network approach of the influence matrix, we develop local conditions called generalized zipper conditions that allow exact solutions of local dynamics. We also introduce a numerical bootstrap method for solving influence matrices with finite but relatively large bond dimensions. This uncovers a rich landscape of nonequilibrium behavior exhibiting initial-state dependence. As an example, we investigate the fate of persistent oscillating dynamics under local non-integrable perturbations, and present analytical results for non-thermal relaxation constrained by conservation laws. We also obtain numerically exact results for entanglement growth across a broad class of initial states. Furthermore, from an information-theoretic perspective, we identify a refined structure of multitime correlations termed the hidden Markov order: the memory encoded in the dynamics separates into finite-length and long-range distributed components, which becomes transparent in an exact split-index matrix-product-state representation of the influence matrix. Our approach enables unified investigations of nonthermalizing and thermalizing regimes of nonequilibrium dynamics within a single analytically tractable model, and can be tested experimentally in state-of-the-art quantum simulators such as Rydberg atom arrays.

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