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01.
arXiv (CS.LG) 2026-06-16

High-Dimensional Random Projection for Activation Steering in Language Models

作者:
Minh-Hieu Pham↗Bach Do↗Laziz Abdullaev↗Tan Minh Nguyen↗Khoat Than↗

arXiv:2606.15092v1 Announce Type: new Abstract: Activation steering has emerged as a key methodology for controlling the behavior of large language models (LLMs). Existing difference-in-means based methods, however, are fundamentally limited: they capture only mean differences between class activations and fail to recover discriminative signals that naturally exist in the nonlinear feature subspace under the superposition hypothesis. Motivated by that, we propose High-Dimensional Random-projection for Activation Steering (HiDRA), a training-free approach that integrates seamlessly with existing activation steering methods. By performing activation addition in the projected high-dimensional space, HiDRA can provably capture a better discriminative structure beyond the reach of linear methods. Experiments across diverse LLM families and benchmarks demonstrate that HiDRA consistently outperforms baseline counterparts, achieving stronger behavioral control without significant computational overhead.

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02.
arXiv (quant-ph) 2026-06-11

Super-Heisenberg Non-Equilibrium Quantum Sensing with Waveguide-Coupled Emitters

作者:
Mohammad B. Arjmandi↗

arXiv:2606.11975v1 Announce Type: new Abstract: We explore an array of quantum emitters as non-equilibrium probes, coupled to a one-dimensional photonic waveguide, aiming to estimate its properties such as wave number which encodes the waveguide frequency and dispersive characteristics. By considering transient dynamics following initial excitation, we show that the quantum Fisher information (QFI) can be significantly enhanced through careful emitter positioning. For two-emitter probes, optimal spacing stabilizes populations and coherences in the single-excitation subspace, suppressing super radiant decay and extending both the magnitude and longevity of QFI. Randomized emitter configurations also reveal that vanishing waveguide-mediated cross decay maximizes both achievable sensitivity and the temporal duration over which information about the parameter remains accessible. Extending to multipartite probes, we demonstrate that the maximum QFI and its temporal integral scale with system size, exceeding the Heisenberg limit for all positioning strategies. Our results highlight the potential of waveguide-coupled emitter arrays as versatile quantum sensors, where collective radiative dynamics can be harnessed to achieve tunable, long-lived, and enhanced precision.

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03.
arXiv (CS.LG) 2026-06-16

The Machine Learning Approach to Moment Closure Relations for Plasma: A Review

作者:
Samuel Burles↗Enrico Camporeale↗

arXiv:2511.22486v3 Announce Type: replace-cross Abstract: The requirement for large-scale global simulations of plasma is an ongoing challenge in both space and laboratory plasma physics. Any simulation based on a fluid model inherently requires a closure relation for the high order plasma moments. This review compiles and analyses the recent surge of machine learning approaches developing improved plasma closure models capable of capturing kinetic phenomena within plasma fluid models. We survey two methodological families: neural-network surrogates (from multilayer perceptrons to Fourier neural operators, the latter recently reproducing both linear and non-linear Landau damping online within a fluid solver) and equation-discovery methods such as sparse regression; and organise the studies by whether they are tested offline against reference data or online within a time-evolving solver. We outline the challenges associated with machine-learning closures, including off-diagonal pressure-tensor accuracy, generalisation beyond the training distribution, and stable integration into large-scale simulations, and the directions future research might take to address them.

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04.
arXiv (CS.AI) 2026-06-15

StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance

作者:
Guanming Liu↗Yuqi Ren↗Hansu Gu↗Peng Zhang↗Weihang Wang↗Jiahao Liu↗Ning Gu↗Tun Lu↗

arXiv:2606.14571v1 Announce Type: new Abstract: A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two-step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow-up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow-up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow-up behavior, even when evidence is stored or feedback is incorporated locally. StreamMemBench is publicly available at https://github.com/landian60/StreamMemBench.

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05.
arXiv (CS.LG) 2026-06-16

Learning Topological Representations for Molecular Dynamics

作者:
Dominik Geng↗Florian Graf↗Martin Uray↗Roland Kwitt↗

arXiv:2606.14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings. Designing descriptors that are both expressive and broadly applicable, however, remains challenging. We study persistent homology (PH) as a general-purpose representation for MD and introduce the masked Flood complex, a protein-tailored modification of a recently introduced simplicial complex construction that emphasizes inter-residue structure at low computational cost. Vectorized persistence diagrams then provide information-rich, geometry-aware summaries of protein conformations, which we evaluate on protein class prediction, frame-level observable regression, and Markov state model (MSM) estimation from learned low-dimensional coordinates in a single shared representation space. Results on the mdCATH dataset show that PH-based descriptors are competitive across tasks, with masked Flood PH yielding the most consistent overall performance. Further, when using topologically-informed MSMs as a drop-in replacement within the recent MarS-FM framework for generative modeling of protein conformations, we obtain consistently better ensemble statistics than MSMs based on physical observables. Finally, we explore the transferability of the generative model to qualitatively different, fast folding, proteins.

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

DRAG-Compatible Leakage Suppression in Landau–Zener Control via Isoprobability Twins

作者:
Ivo S. Mihov↗Nikolay V. Vitanov↗

arXiv:2506.19572v4 Announce Type: replace Abstract: Analytically solvable models – particularly the Landau-Majorana-Stückelberg-Zener (LMSZ) and Allen-Eberly-Hioe (AEH) models – underpin many quantum-gate implementations and population-transfer protocols. However, their canonical pulse shapes are incompatible with modern leakage-suppression techniques and some systems. Most notably, the constant Rabi envelope of the LMSZ pulse prevents many leakage-suppression approaches, which require smoothness. We address both limitations by developing the concept of isoprobability twin models: distinct pairs of Rabi frequency $\Omega(t)$ and detuning $\Delta(t)$ that yield identical post-pulse transition probabilities based on the Delos-Thorson transformation. In this work, we formalise the method by experimentally demonstrating the equivalence of multiple LMSZ and AEH twin models on IBM's ibm_kyiv processor. Finally, we show a staggering leakage reduction by more than 3 orders of magnitude using a custom DRAG implementation of a cosine LMSZ isoprobability model.

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07.
arXiv (CS.AI) 2026-06-18

AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice

作者:
Danielle R. Thomas↗Marie Cynthia Abijuru Kamikazi↗Clara Brandt↗Conrad Borchers↗Kenneth R. Koedinger↗

arXiv:2606.18617v1 Announce Type: cross Abstract: There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven system that assesses both open responses during training and authentic real-life tutoring. Unlike platforms that only assess learning through online training or simulations, our system utilizes Generative AI (Gemini-2.5-pro) to analyze transcriptions of authentic tutoring, measuring the transfer of tutor skills to real-life application. Human tutors instructing students remotely in math (N=86) completed six scenario-based lessons, averaging a significant 7.4% learning gain. Using mixed-effects models across 405 session-to-lesson pairs, we found that training performance significantly predicted real-life transcript scores with an effect size of 0.25 SD. Model comparison (AIC/BIC) indicated averaging open response and multiple choice performance during training predicted real-life tutor performance best, although open responses were comparatively more predictive. Exploratory analysis showed that after training, tutors were significantly more likely to encounter pedagogical opportunities to apply their skills (61.1% to 68.9%) and demonstrated higher execution quality within those opportunities (65.5% to 68.1%). Interrupted time series analysis suggested that these tutor improvements were part of a gradual trend over time rather than an immediate intervention effect of training. We illustrate an AI-driven method to link tutor training with real-life assessment. In doing so, we contribute open datasets, AI prompts, and scoring rubrics to support transparency and reproducibility.

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08.
arXiv (CS.LG) 2026-06-18

Adaptive Speech-to-Spike Encoding for Spiking Neural Networks

作者:
Taharim Rahman Anon↗Jakaria Islam Emon↗

arXiv:2606.19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing. Current systems typically rely on fixed spike encoders, forcing downstream Spiking Neural Networks (SNNs) to compensate for non-adaptive input representations. To address this, we present a learnable residual speech-to-spike encoder jointly trained end-to-end with a Recurrent Leaky Integrate-and-Fire (R-LIF) backbone. We validate this approach on the Google Speech Commands v2 (GSC-v2) benchmark, achieving up to 94.97% accuracy. Notably, the learned encoder remains highly parameter-efficient with a compact 35k-parameter variant that reaches 89.8%, matching or exceeding prior baselines that require an order of magnitude more parameters. Our encoder-focused analysis, including linear probing and gradient-residual inspection, indicates that the encoder does not target faithful signal reconstruction but instead learns task-aligned spike representations that enhance class separability. Finally, we benchmark bio-inspired, hardware-friendly credit assignment by comparing Direct Feedback Alignment (DFA) with surrogate-gradient BPTT under identical architectures and training conditions. We find that DFA reaches 91.5% accuracy, quantifying the performance trade-off of bio-inspired learning rules for modern neuromorphic audio.

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09.
medRxiv (Medicine) 2026-06-10

Documented clinical genetic testing among carriers of hereditary breast and ovarian cancer variants: Ancestry and socioeconomic disparities in the All of Us research program

作者:
Yerukala Sathipati↗Scott↗

Importance: Hereditary breast and ovarian cancer (HBOC) variant carriers benefit from risk-reducing interventions, but only if identified. The extent to which carriers are clinically recognized, and whether recognition is equitable across diverse populations, is poorly characterized in a single large U.S. cohort. Objective: To estimate P/LP HBOC carrier prevalence across genetic ancestry groups, quantify documented clinical genetic testing among carriers, and evaluate ancestry and socioeconomic disparities in testing. Design, Setting, and Participants: Cross-sectional analysis of the All of Us Research Program Controlled Tier (Curated Data Repository v8/C2024Q3R9), comprising participants with short-read whole genome sequencing and linked electronic health record (EHR) and survey data. Carriers were ascertained from research genomic data independent of clinical testing. Exposures: Genetically inferred ancestry (African [AFR], Admixed American [AMR], East Asian [EAS], European [EUR], Middle Eastern [MID], South Asian [SAS]); self-reported household income and educational attainment. Main Outcomes and Measures: (1) Carrier prevalence with Wilson 95% CIs; (2) documented clinical genetic testing (procedure codes) among carriers; (3) adjusted odds of documented testing among women, by ancestry, before and after socioeconomic adjustment, using multivariable logistic regression. Results: Among 414,830 participants, P/LP HBOC carrier prevalence was 1.42% (95% CI, 1.38-1.45) overall and similar across ancestry groups (AFR 1.24%, AMR 1.32%, EAS 1.19%, EUR 1.52%, MID 1.68%, SAS 1.33%; overlapping CIs). Among 250,071 women in the testing analysis, documented clinical genetic testing was rare: only 74 of 5,878 carriers overall (1.3%) and 59 of 3,572 European-ancestry carriers (1.7%) had a documented test, with counts below reportable thresholds in all other ancestry groups. African-ancestry women had lower adjusted odds of documented testing than European-ancestry women (Model 1 adjusted odds ratio [aOR], 0.32; 95% CI, 0.27-0.39), an association that attenuated but persisted after adjustment for income and education (Model 2 aOR, 0.48; 95% CI, 0.40-0.58; P < 0.001); Admixed American women also had reduced adjusted odds (aOR, 0.71; 95% CI, 0.61-0.84). Lower income and lower education were independently and dose-dependently associated with lower testing odds (income

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10.
arXiv (quant-ph) 2026-06-16

Excited-State Quantum Chemistry on Qumode-Based Processors via Variational Quantum Deflation

作者:
Marlon F. Jost↗Sijia S. Dong↗

arXiv:2604.13457v3 Announce Type: replace Abstract: Variational quantum algorithms on bosonic quantum processors are an emerging paradigm for quantum chemistry calculations, exploiting the natural alignment between molecular structure and harmonic oscillator-based hardware. We introduce the qumode-based variational quantum deflation framework (QumVQD) for finding both electronic and vibrational excited state energies on qumode-based architectures. We validate the approach through electronic structure calculations on H$_{2}$ and linear H$_{4}$, where we introduce Hamming-weight filtering of the Fock basis to enforce particle number conservation and eliminate spurious eigenstates by reducing the required Hilbert space, which reduces the required number of qumodes in turn. We achieve agreement with full configuration interaction (FCI) using the STO-3G basis set within the chemical accuracy threshold at most points along the potential energy surfaces. Extending to the vibrational structure, we combine QumVQD with an existing Hamiltonian fragmentation approach based on Cartan subalgebra, allowing us to compute the vibrational eigenenergies of CO$_{2}$ and H$_{2}$S to spectroscopic accuracy with per-fragment circuits that scale as $O(N)$ in single-qumode gates and $O(N^2)$ in beam-splitter gates for $N$ qumodes. For the case of CO$_{2}$, we get total gate counts more than an order of magnitude smaller than those reported for qubit-based vibrational algorithms at this system size. These results demonstrate that bosonic quantum devices are a viable platform for excited-state quantum chemistry, particularly for vibrational problems where qubit-based methods incur substantial boson-to-qubit mapping overhead.

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11.
arXiv (CS.LG) 2026-06-15

Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments

作者:
Mykola Vysotskyi↗Runqi Lin↗Grzegorz Biziel↗Michal Zakrzewski↗Sebastian Montagna↗Damian Rynczak↗Shreyansh Padarha↗Kumail Alhamoud↗Zihao Fu↗William Lugoloobi↗Kai Rawal↗Hanna Yershova↗…

arXiv:2606.14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchmarks are typically built on popular applications with relatively simple tasks and focus on a narrow set of capabilities while overlooking broader dimensions, resulting in saturated performance on modern agents and failing to probe their limitations. To this end, we introduce GauntletBench, a web-based benchmark for evaluating agent generalisation in challenging scenarios, focusing on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning), across five less-covered professional applications (Video Editor, Workflow Builder, 3D Modeller, Flight Analyser, and Circuit Designer), each with 20 vision-intensive tasks (100 in total). Our benchmark provides a modular pipeline that comprises an environment compatible with both open- and closed-source agent frameworks, a controlled web-based application, a well-structured task suite, and an automated evaluation engine with diverse metrics. Contrary to widespread expectations, our empirical results reveal that frontier agentic systems remain far from achieving human-level performance. Even the state-of-the-art agent achieves only a 19.1% success rate on our GauntletBench, highlighting the limitations in these overlooked capabilities and generalisation. By comparison, non-expert human annotators achieve over 80% success on our challenging yet feasible tasks, revealing the substantial gap between current agent capabilities and those required for complex real-world scenarios.

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

Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty

作者:
Purav Matlia↗Christian Moya↗Guang Lin↗

arXiv:2605.00330v2 Announce Type: replace Abstract: Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadratic inference complexity and unreliable uncertainty quantification in safety-critical settings. We propose Conformalized Quantum DeepONet Ensembles, a framework that addresses both challenges simultaneously. By leveraging Quantum Orthogonal Neural Networks (QOrthoNNs), we reduce operator inference complexity from O(n^2) to O(n), enabling scalable evaluation over fine discretizations. To provide rigorous uncertainty quantification, we combine ensemble-based epistemic modeling with adaptive conformal prediction, yielding distribution-free coverage guarantees. A key challenge in ensembling is that naive parallelism scales hardware resources linearly with the number of models. We resolve this by using Superposed Parameterized Quantum Circuits (SPQCs), which compress multiple ensemble members into a single circuit and enable simultaneous multi-model execution. Experiments on synthetic partial differential equations and real-world power system dynamics demonstrate that our approach achieves accurate predictions while maintaining calibrated uncertainty under realistic quantum noise. These results establish a practical pathway toward scalable, uncertainty-aware operator learning in quantum machine learning.

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

Runtime Analysis of Cartesian Genetic Programming in Evolving Boolean Functions

作者:
Duc-Cuong Dang↗Roman Kalkreuth↗Andre Opris↗

arXiv:2606.15923v1 Announce Type: cross Abstract: Cartesian Genetic Programming (CGP) is among the practical and popular forms of Genetic Programming as it uses a graph-based representation of programs. This paper presents a first runtime analysis of CGP in evolving Boolean functions using complete training sets. We prove an asymptotic bound $O(n D^5)$ for the expected number of fitness evaluations of CGP to construct a conjunction of $n$ inputs using at most $D \geq n-1$ binary gates, a minimal function set, and even with a strict survival selection. When the non-strict selection is used, the bound is improved to $O(n D^4)$. Our analysis reveals interesting characteristics of CGP induced search, which have been only observed empirically. In particular, enabling the acceptance of equally good solutions, including those with connected gates non-contributing to fitness, can lead to a speedup, and consequently a better asymptotic time bound. In contrast to conjunctions, we also prove a negative result which shows that CGP requires exponential time to evolve an exclusive disjunction. Experiments evolving conjunctions complement our theoretical findings. The use of incomplete training sets is found to further reduce the average number of fitness evaluations while maintaining a good level of generalisation.

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

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

作者:
Chanuka A. S. Hewa Kaluannakkage↗Rajkumar Buyya↗

arXiv:2606.10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations. Operating over lossy wireless links under constraints, these systems cannot rely on retransmissions, so model parameters must be accepted as partial chunks, leading to two key failure modes, which are selection bias, where poor-quality links are systematically under-represented in gossip aggregation, and update staleness, where asynchronous nodes contribute outdated models. We prove that classical gossip aggregation introduces irreducible selection bias proportional to the link-loss rate. We propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which corrects selection bias using Inverse Probability Weighting (IPW) with online channel estimation and mitigates staleness via Age-of-Information (AoI) decay without requiring a global clock. We prove that DFL-AA removes link-quality distortion in expectation and consistently outperforms state-of-the-art baselines across varying loss rates and heterogeneous channel conditions on fixed directed topologies.

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17.
medRxiv (Medicine) 2026-06-11

Assessment of occupational aerosol exposure for laboratory technicians: A quantitative study using {Phi}X174 phage as a substitute virus

作者:
Liu↗Zhang↗

This study aimed to clarify aerosol exposure risks throughout the workflow of a Biosafety Level 2 (BSL-2) polymerase chain reaction (PCR) laboratory, validate the suitability of the {Phi}X174 bacteriophage as an indicator virus, and provide evidence for biosafety control measures. The {Phi}X174 bacteriophage was used to simulate viral samples, and a concentration-bacteriophage plaque standard curve was constructed (R2=0.998). Five operational steps in a simulated PCR laboratory were quantitatively monitored for aerosol concentration using double-layer agar plates, with blank controls used to eliminate interference. Statistical analysis was employed to identify risk differences. Sample homogenization ((5.67 {+/-} 1.23) x 104 plaque-forming units (PFU)/m3) and nucleic acid extraction ((3.45 {+/-} 0.89) x 104 PFU/m3) were identified as high-/very high-risk steps. The viral load in the samples was strongly positively correlated with the aerosol concentration (r = 0.926, P

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18.
arXiv (CS.AI) 2026-06-16

Synthetic Counteradaptation: A Principle of Human-AI Co-evolution

作者:
Ivar Frisch↗Jackie Kay↗Philip Moreira Tomei↗

arXiv:2606.15503v1 Announce Type: new Abstract: In this paper, we introduce the concept of synthetic counteradaptation, a process where human and AI systems co-evolve by adapting to each other's strategies and behaviors. Synthetic counteradaptation occurs when AI systems develop novel strategies or social protocols, prompting humans to extract insights and adapt their own behaviors in response, leading to the emergence of new agent interaction dynamics. To illustrate these dynamics, we analyze examples from various contexts, including the game of Go, mixed-motive social interactions, and geopolitical simulations. By exploring these cases, we demonstrate how synthetic counteradaptation provides a framework for understanding the recursive and co-evolutionary nature of human-AI interactions in multi-agent environments.

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

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut

作者:
Sidharth Brahmandam↗Vayd Ramkumar↗

arXiv:2606.23718v1 Announce Type: new Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a leading variational algorithm for combinatorial optimization on near term quantum devices. As circuit depth increases, the number of optimization parameters grows, making the search landscape increasingly nonlinear and difficult to optimize. Previous studies have shown that optimal QAOA parameters often lie on a low dimensional manifold that can be approximated using Principal Component Analysis (PCA) at shallow circuit depths. However, the effectiveness of PCA decreases at higher depths because the underlying parameter manifold becomes increasingly nonlinear. In this work, we investigate Kernel Principal Component Analysis (KPCA) with a radial basis function kernel as a nonlinear dimensionality reduction technique for QAOA parameter optimization. The model is trained using 200 graphs from each of 3 graph families, namely Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, with graph sizes ranging from 7 to 10 nodes. Performance is evaluated on 30 test graphs containing 12 nodes at circuit depths 1, 2, 4, and 8. Experimental results demonstrate that KPCA consistently outperforms PCA at deeper circuit depths across all graph families. At depth 8, KPCA achieves approximation ratios above 0.86, while PCA declines to approximately 0.81 to 0.83. Both methods reduce the number of quantum circuit evaluations by more than 93 percent relative to unrestricted QAOA optimization. These findings suggest that nonlinear kernel methods more effectively capture the structure of the QAOA parameter manifold and provide a practical approach for scaling variational quantum optimization to deeper circuits.

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20.
arXiv (CS.CL) 2026-06-17

Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing

作者:
Kexin Chen↗Yi Liu↗Haonan Zhang↗Yanhui Li↗Xinyu Deng↗Dongxia Wang↗

As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern. Existing deception monitors either score visible transcripts or derive scalar probe scores from representation vectors, leaving little inspectable evidence about why a response is suspicious. We introduce STATEWITNESS, an activation explainer for deception auditing. A separate decoder reads a target model's hidden states, then answers natural-language queries or emits structured reports about them. We evaluate STATEWITNESS on two target reasoning LLMs across seven deception datasets. STATEWITNESS reaches 0.916 mean AUROC, a relative gain of 11.6% over the best black-box text monitor and 25.0% over the best activation-probe baseline under the same evaluation protocol. When combined with existing monitors, STATEWITNESS reduces missed deceptive examples in simple threshold ensembles. Beyond scalar detection, the decoder returns query-level answers, schema reports, and token- or sentence-level evidence traces for human inspection. We view this interface as a potential building block for broader interpretability and alignment tools.

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21.
medRxiv (Medicine) 2026-06-12

Microbial etiology, antibiotic susceptibility profiles, and multidrug resistance of urinary tract infections at a secondary healthcare facility in Ghana

作者:
Agyapong↗J. K↗Damalie↗Dombawel↗Noah↗Balo↗Acheampong↗Kudzordzi↗P.-C↗Nyarko↗Ofori↗D. K↗…

Background: Rising antibiotic resistance challenges empirical therapies for urinary tract infections (UTIs). This study evaluated the microbial etiology, susceptibility profiles, and multidrug resistance (MDR) patterns of uropathogens among outpatients at the Berekum Holy Family Hospital, Ghana. Methods: This cross-sectional study (February to August 2021) screened 263 symptomatic outpatients. Mid-stream urine samples underwent quantitative culture, biochemical identification, and antimicrobial susceptibility testing via the Kirby-Bauer disc diffusion method following the 2021 CLSI guidelines. Results: Significant bacteriuria prevalence was 22.8% (60/263). UTIs predominated in females (78.3%, 47/60; p = 0.1501) and individuals [&ge;]45 years (33.3%, 20/60). Gram-negative rods accounted for 90.0% of isolates, primarily Escherichia coli (26.7%), Citrobacter spp. (25.0%), and Enterobacter spp. (21.7%); Staphylococcus aureus (10.0%) was the only Gram-positive pathogen. Extreme phenotypic resistance was observed against piperacillin/tazobactam (98.3%), cefotaxime (93.3%), tetracycline (88.3%), and cefoperazone (85.0%). Conversely, highest therapeutic susceptibilities were retained by amikacin (78.3%), levofloxacin (61.7%), and gentamicin (58.3%). Conclusion: The high prevalence of MDR uropathogens against advanced beta-lactamase inhibitor combinations and cephalosporins necessitates an immediate re-evaluation of regional empirical protocols. Amikacin, levofloxacin, and gentamicin remain viable options prior to culture confirmation. These findings establish a crucial phenotypic baseline to guide localized prescribing policies and regional antimicrobial resistance tracking strategies.

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22.
arXiv (quant-ph) 2026-06-24

From Spectral Singularities to Multipartite Entanglement Scaling at Higher-Order Exceptional Points

作者:
Chunlai Yang↗Shuheng Liu↗Xinyao Huang↗Kaiye Shi↗Qiongyi He↗

arXiv:2606.24205v1 Announce Type: new Abstract: Exceptional points (EPs) are non-Hermitian spectral singularities exhibiting fractional-power responses, yet their implications for multipartite entanglement of interacting quantum many-body systems remain largely unexplored. Here we develop a general framework that links higher-order non-Hermitian degeneracies to the scaling behavior of genuine multipartite entanglement in interacting identical-qubit systems. Permutation symmetry of the identical qubits decomposes the exponentially large Hilbert space into independent irreducible-representation sectors, thereby constraining the maximal EP order of $N$ qubits to $N+1$ rather than $2^N$. Near an $n$th-order EP, genuine multipartite entanglement inherits the spectral response and generically exhibits a fractional-power scaling under weak perturbations. Explicit examples show that conventional two-body interactions support third- and fourth-order EPs with the corresponding entanglement responses, whereas higher-order EPs with genuine multipartite-entangled coalesced states require additional independent interaction channels, such as three-body interactions. Our results establish a fundamental connection among non-Hermitian degeneracies, multipartite entanglement, and symmetry, extending higher-order EP physics from spectral singularities to genuine many-body quantum correlations.

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23.
medRxiv (Medicine) 2026-06-22

Integration of lung tissue proteomics and genome-wide association data to identify lung cancer susceptibility proteins and potential drug targets

作者:
Xu↗Shi↗Shu↗X.-O↗Tao↗Dou↗Guo↗Wen↗Yang↗Zhang↗Wu↗Deppen↗…

Background: Proteins directly impact disease development and act as drug targets. Therefore, we integrated genomic and lung tissue proteomics data to identify lung cancer susceptibility proteins, elucidating genetic mechanisms and candidate drug targets. Method: We profiled the proteome and genome in non-neoplastic lung tissue from 200 lung cancer patients. Using this data, we constructed genetic models to predict abundance across the proteome in lung tissue. We applied these models to genome-wide association study (GWAS) data from 55,174 lung cancer cases and 1,294,174 controls to evaluate their associations with the risk of lung cancer, overall and by major histological subtypes. Bayesian colocalization and Mendelian randomization (MR) analyses were used to prioritize putative causal proteins, which were cross-referenced with three main drug-protein databases to identify potential therapeutic targets. Results: We identified 29 proteins associated with lung cancer risk at a false discovery rate < 5%, including 25 for overall lung cancer, two (AQP3 and IL18) specifically for adenocarcinoma, and another two (HMGN2 and HLA-DMB) for squamous cell carcinoma. Of them, genes encoding 17 proteins reside at least 2Mb away from any known GWAS risk loci, including 14 for overall lung cancer (HYI, GPX1, GMPPB, DSP, HDDC2, MTCH2, SUOX, JMJD7, PDIA3, IL16, IQGAP1, SULT1A2, ARHGAP27, and TYMP) and three for subtypes (AQP3, IL18, and HMGN2). Among the 12 proteins located within the known risk loci, EPHX2, CLDN18, PSMD5, and CYP2S1 proteins showed an association independent of the proximal GWAS-identified lead variant. Colocalization and/or MR analysis suggested 11 potential causal proteins. Five of these candidate causal proteins (DSP, CLDN18, IQGAP1, IL18 and TYMP) are targeted by nine drugs already approved by the FDA or in phase III trials. Conclusion: Our study identified novel lung cancer susceptibility proteins and potential drug targets, offering valuable insights into lung cancer biology and future translational utilities.

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24.
arXiv (CS.LG) 2026-06-18

Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems

作者:
Kuilin Qin↗Lianfang Wang↗Xu Sun↗Jiwei Jia↗Yu Wang↗Yong Wang↗Yuping Duan↗

arXiv:2606.18305v1 Announce Type: cross Abstract: Operator learning is an emerging interdisciplinary field that integrates machine learning with scientific computing. By mapping infinite-dimensional function spaces, this approach provides an efficient surrogate modeling framework for high-dimensional partial differential equations (PDEs). Compared to traditional numerical solvers, it achieves a superior trade-off between computational complexity and approximation accuracy, demonstrating significant advantages in many-query tasks such as real-time prediction and parameter sweeps. Given the stringent accuracy requirements of both forward simulation and inverse inference, as well as the precision bottlenecks of existing operator learning methods in handling complex boundaries or long-term evolution, we propose the Starter-Iterator Neural Operator (SINO). Our framework reinterprets the initialization strategies and iterative formats of traditional iterative methods through neural networks, establishing an efficient approach for spectral-spatiotemporal collaborative modeling. Specifically, the frequency-domain initialization module captures globally stable low-frequency features, while the time-domain learning module focuses on optimizing local solution residuals, thereby effectively overcoming the inherent limitations of conventional single-domain modeling approaches. Extensive experiments on typical dynamical systems such as the Navier-Stokes equations and acoustic wave equations, as well as practical applications including super-resolution imaging and weather forecasting, demonstrate that SINO achieves outstanding performance in numerical accuracy, generalization capability, and robustness.

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25.
arXiv (CS.AI) 2026-06-19

Optimal Order of Multi-Agent and General Many-Body Systems

作者:
Jake J. Xia↗

arXiv:2606.20485v1 Announce Type: cross Abstract: This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations. The framework is built on two fundamental agent-level variables: power, which measures agent influence on collective outcomes, and response functions, which determine how agents react to observations. We derive how macroscopic properties, including total power, useful power, entropy, order, fragility, and mobility, emerge from these two variables of heterogeneous agents. To study the trade off between growth and resilience, we introduce a system-level utility function parameterized by a risk-appetite coefficient and derive an optimal degree of order that balances productivity, stability, and adaptability. The analysis suggests that stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility. We further argue that order, entropy, information, and useful energy are task-dependent and system-relative concepts whose meanings depend on the objectives of the system. By measuring and designing agent power distributions and response functions, it may be possible to better understand, predict, and optimize collective behavior and identify the conditions under which collective intelligence and optimal order emerge.

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