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01.

medRxiv (Medicine) 2026-06-15 DOI: HASH:255b6c09d205b63b32360e63a6715003

Epileptogenicity alters intrahippocampal ripple propagation

作者:

Chen ↗Ye ↗Staba ↗R. J ↗wang ↗Weiss ↗S. A ↗

Objective: Tracing the propagation of high-frequency oscillations (HFOs) aids in localizing epileptogenic regions and improving surgical outcomes. We examined how hippocampal epileptogenicity influences the propagation properties of the HFOs it generates. Methods: We analyzed non-REM sleep stereo-EEG from 49 patients (68 hemispheres) with verified hippocampal contacts. Hippocampi were stratified by excitability: 28 seizure onset zone (SOZ), 22 more-irritative non-SOZ (>6 interictal epileptiform discharges [IED]/min), and 18 less-irritative non-SOZ (

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02.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2606.25117

Feasibility-driven QAOA with penalty scheduling

作者:

Francesco Ferrari ↗Matteo Vandelli ↗Daniele Dragoni ↗

arXiv:2606.25117v1 Announce Type: new Abstract: Most available quantum algorithms address constrained optimization problems by treating constraints as soft penalty terms within a QUBO formulation. This approach requires careful adjustment of the penalty coefficients, which scales poorly with the number of constraints and lacks a proper strategy to balance feasibility and solution quality. In this work, we introduce two extensions of standard linear-ramp QAOA (lr-QAOA) tailored to problems with multiple heterogeneous constraints. We first construct $\Lambda$-lr-QAOA, in which each penalty term is assigned its own linear-ramp schedule, promoting penalty weights from external hyperparameters to internal variational parameters of QAOA, similarly to the objective and mixer parameters. By optimizing all schedules jointly in a single run, this approach eliminates nested penalty tuning and scales more efficiently to multiple constraints. The optimization is guided by a feasibility-driven loss function that pushes the quantum state towards high-quality feasible solutions. As a further refinement, we introduce piecewise-ramp QAOA, in which the linear ramps are replaced by two-segment piecewise schedules, enhancing the expressiveness of the Ansatz at the cost of a small parameter overhead independent of the circuit depth. We benchmark both methods on Earth-observation satellite mission planning tasks formulated as budget-constrained Maximum Weight Independent Set problems. Numerical results show that piecewise-ramp QAOA consistently outperforms lr-QAOA and $\Lambda$-lr-QAOA across circuit depths and system sizes. Furthermore, both $\Lambda$-lr-QAOA and piecewise-ramp QAOA exhibit a high feasibility rate, which is crucial in industrial applications. Our analysis highlights an intrinsic feasibility-optimality trade-off, which we address by introducing a filtered variant of the loss providing a single hyperparameter to tune this balance.

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03.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.13867

Muon$^p$: Muon with Fractional Spectral Powers

作者:

Yihe Dong ↗Will Sawin ↗

arXiv:2606.13867v1 Announce Type: new Abstract: Muon is an increasingly widely used optimizer that replaces a gradient $G=USV^\top$ with its polar factor $UV^\top$, thereby flattening the singular spectrum. However, full flattening discards singular-value information that may matter for adaptation. We introduce Muon$^p$, a Muon-style optimizer that instead uses fractional spectral-power updates $US^pV^\top$ for rational $p\in(0,1)$, interpolating between Muon and gradient descent. To make it practical, we prove that fractional spectral powers cannot be computed by any fixed univariate polynomial iteration, and furthermore derive low-degree odd bivariate recurrences that approximate $US^pV^\top$ using only matrix multiplications, preserving Muon's matrix-multiplication-only structure and compute complexity. We show that Muon$^p$ maximizes the linear improvement in loss under the Schatten $q$-norm for $q=1+\frac{1}{p}$. Empirically, Muon$^p$ is especially effective for finetuning: on billion-scale models, Muon$^p$ improves validation perplexity and downstream task performance. We further analyze when Muon$^p$ is less suitable, through the lens of spectral geometry. Our results reveal important insights on when preserving the singular spectrum can bring significant gains, and introduce a principled way to achieve them.

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04.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.13892

Crypto x AI, AI x Crypto: A Survey

作者:

arXiv:2606.13892v1 Announce Type: cross Abstract: The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.

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05.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2510.06647

Q-Learning with Fine-Grained Gap-Dependent Regret

作者:

Haochen Zhang ↗Zhong Zheng ↗Lingzhou Xue ↗

arXiv:2510.06647v2 Announce Type: replace-cross Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minimax worst-case regret, but their gap-dependent bounds remain coarse and fail to fully capture the structure of suboptimality gaps. We address this limitation by establishing fine-grained gap-dependent regret bounds for both UCB-based and non-UCB-based algorithms. In the UCB-based setting, we develop a novel analytical framework that explicitly separates the analysis of optimal and suboptimal state-action pairs, yielding the first fine-grained regret upper bound for UCB-Hoeffding (Jin et al., 2018). To highlight the generality of this framework, we introduce ULCB-Hoeffding, a new UCB-based algorithm inspired by AMB (Xu et al.,2021) but with a simplified structure, which enjoys fine-grained regret guarantees and empirically outperforms AMB. In the non-UCB-based setting, we revisit the only known algorithm AMB, and identify two key issues in its algorithm design and analysis: improper truncation in the $Q$-updates and violation of the martingale difference condition in its concentration argument. We propose a refined version of AMB that addresses these issues, establishing the first rigorous fine-grained gap-dependent regret for a non-UCB-based method, with experiments demonstrating improved performance over AMB.

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06.

arXiv (quant-ph) 2026-06-17 DOI: arXiv:2606.18140

Singular Vector Finite Element Basis Functions for Tetrahedra in Complex Electromagnetic Geometries

作者:

Samuel T. Elkin ↗Ghazi Khan ↗Ebrahim Forati ↗Brandon W. Langley ↗Dogan Timucin ↗Reza Molavi ↗Thomas E. Roth ↗

arXiv:2606.18140v1 Announce Type: cross Abstract: Electromagnetic finite element method (FEM) implementations using traditional basis functions struggle to accurately represent field behavior near singular features such as conducting wedges. To combat this, specialized singular basis functions have been introduced to directly model the singular fields in these regions, leading to substantially improved performance. While these efforts have been pursued extensively in 2D, few functions have been developed for 3D elements. In this work, we develop basis functions for this in tetrahedra. Unlike prior functions, these basis functions are additive, meaning they are included alongside the standard vector basis functions to achieve more robust performance. Further, these functions are designed to be adaptable to tetrahedra touching several unique singular features by using combinations of basis functions singular with respect to each node and edge in the element, making them applicable to highly complex geometries. Higher-order interpolatory versions of the basis functions for modeling singular behavior with greater accuracy are also provided. These basis functions lead to substantial improvements in accuracy relative to the standard basis functions, and allow otherwise expensive simulations to be performed at far lower costs. As an application example, we perform simulations to extract critical quantities for designing superconducting qubits that significantly depend on the behavior of singular fields. In Ansys HFSS, this took 21.27 hours and a peak memory usage of 6.23 TB with 800 processors available, while using our singular basis functions achieved comparable results in 196 seconds while using 27.24 GB of memory and only 16 processors. Due to these benefits, our singular basis functions could be applied to enable design optimization of electromagnetic geometries with dominantly singular behavior, such as superconducting qubits.

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07.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.15679

Stochastic trace estimation with tensor train random vectors

作者:

Zvonimir Bujanovi\'c ↗Daniel Kressner ↗Hrvoje Oli\'c ↗

arXiv:2606.15679v1 Announce Type: cross Abstract: Stochastic trace estimation is a standard tool for approximating the trace of a large-scale matrix available only through matrix-vector products. However, in tensor-structured settings, unstructured Gaussian or Rademacher test vectors may be prohibitively expensive to store and compute with, while cheaper rank-one tensor-product vectors can require sample complexities that grow exponentially with the tensor order. This work studies Gaussian random tensor train vectors as a structured alternative for stochastic trace estimation. We show that, with a suitable choice of the tensor train rank, random tensor train vectors recover dimension-independent guarantees for the Girard–Hutchinson estimator. In particular, a median-of-means variant with tensor train rank $r \geq d-1$ achieves the same dependence on the accuracy $\varepsilon$ and failure probability $\delta$ as the classical estimator based on unstructured Gaussian vectors. We further prove an oblivious subspace injection result for sketches formed from independent Gaussian random tensor train vectors: tensor train rank $r\geq d-1$ and $\mathcal{O}(\varepsilon^{-2}(k+\log(1/\delta)))$ samples suffice for a $k$-dimensional target subspace. Finally, we investigate the use of such sketches within the Nystr\"{o}m++ framework. We show that the resulting estimator can achieve the desired $\mathcal{O}(\varepsilon^{-1})$ sample complexity under an additional spectral-tail condition. These results provide clarififcation on both the potential and the limitations of random tensor train vectors in stochastic trace estimation.

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08.

PLOS Computational Biology 2026-06-22 DOI: HASH:4dcdcd6182e8854d4b1a7af088809c4c

Cell-type resolved transcriptional network analysis of <i>in vivo</i> cellular senescence following injury

作者:

Alda Sabalic ↗

by Alda Sabalic, Victoria Moiseeva, Andres Cisneros, Oleg Deryagin, Eusebio Perdiguero, Pura Muñoz-Cánoves, Jordi Garcia-Ojalvo Identifying the genetic correlates of complex phenotypes is a challenging task. Methods coming from the field of complex networks can help finding such molecular patterns, by revealing statistical associations among groups of genes that correlate with the phenotype. Here we study cellular senescence, a complex cell state whose molecular underpinnings are still under active investigation. We analyze cell type–resolved RNA sequencing data obtained from injured muscle tissue in mice, with a network-based approach that merges eigenvector centrality feature selection and community detection. Our analysis identifies genetic markers that had not been associated with senescence so far, which are validated with existing single-cell RNA sequencing data in a different type of tissue. The identified key genes belong to transcriptional pathways associated with established hallmarks of senescence, and thus can be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.

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09.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.11274

Multi-agent rendezvous in fluid flows via reinforcement learning

作者:

Bocheng Li ↗Jingran Qiu ↗Lihao Zhao ↗

arXiv:2606.11274v1 Announce Type: cross Abstract: Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location. However, achieving this in fluid environments presents a challenge, as it remains unclear how agents can exploit underlying fluid kinematics to facilitate convergence. In this study, we adopt a multi-agent reinforcement learning (MARL) approach to develop physics-informed rendezvous strategies in vortical flows. Compared to a naive strategy, where agents navigate toward their counterparts, MARL strategies significantly improve the rendezvous rate. MARL strategies also show transferability across varying vortex intensities, vortex scales, and swarm sizes. By breaking the symmetry of the state-action map, MARL strategy leverages a non-intuitive mechanism that prevents agents from becoming trapped in separate vortices, thereby enhancing rendezvous success. Additionally, a heuristic strategy is extracted from the learned strategy and also outperforms the naive strategy. Furthermore, a theoretical analysis demonstrates that fluid deformation impedes the rendezvous process. Large finite-time Lyapunov exponents identify where fluid effects separate adjacent agents, suggesting that targets should be planned in weak-deformation regions. Our findings reveal the important role that agent-fluid interactions play in multi-agent tasks and highlight the MARL capability to explore swarm intelligence in complex flow environments.

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10.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16532

Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection

作者:

Zhuodong Liu ↗Hugen Lv ↗Xiangyu Li ↗Chunhong Yuan ↗

arXiv:2606.16532v1 Announce Type: cross Abstract: Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage. Existing methods address this but incur architectural complexity or training instability. This paper proposes a dual-granularity orthogonal disentanglement framework enforcing feature independence at two levels: sample-level cosine orthogonality captures directional decorrelation, while batch-level cross-covariance regularization eliminates linear correlations across embedding dimensions. A curriculum disentanglement schedule progressively strengthens the orthogonality constraint without auxiliary networks or adversarial dynamics. Experiments on ASVspoof 2019 LA, ASVspoof 2021 DF, and In-the-Wild datasets demonstrate that the proposed method achieves 1.35%, 7.88%, and 21.58% equal error rates (EER), respectively, surpassing gradient reversal disentanglement by 2.60% absolute on cross-dataset transfer.

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11.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.14763

Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation

作者:

Lorenzo Ortolani ↗Gabriel Voss ↗Gabriele Beltrami ↗Francesco Dorati ↗Tommaso Felice Banfi ↗

arXiv:2606.14763v1 Announce Type: cross Abstract: Real-time autonomous navigation in dynamic, unknown environments remains a fundamental challenge for mobile robotics. We propose a map-free framework that tightly integrates reactive rolling-horizon planning with nonlinear Model Predictive Control (MPC). At each control cycle, a LiDAR-based Gaussian occupancy representation is constructed and used to generate collision-free trajectories via A* search, which are then tracked by a CasADi/IPOPT MPC formulation incorporating a smooth sigmoid obstacle barrier. To improve robustness to parameter sensitivity, we adopt an offline Bayesian optimization scheme based on Tree-structured Parzen Estimators (TPE), which identifies near-optimal controller parameters with respect to a composite navigation objective. In addition, a Gaussian Process surrogate is used to analyze parameter sensitivity and provide insight into the optimization landscape. The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo, followed by deployment on the physical robot. Experimental results show that parameters tuned in simulation transfer effectively to hardware, maintaining comparable performance without additional tuning. The full system achieves up to a 90.0\% navigation success rate when deployed, along with a 38.9\% average improvement in the evaluation metrics across simulated environments.

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12.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2601.21542

Bi-Anchor Interpolation Solver for Accelerating Generative Modeling

作者:

Hongxu Chen ↗Hongxiang Li ↗Zhen Wang ↗Long Chen ↗

arXiv:2601.21542v3 Announce Type: replace-cross Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis. However, their reliance on iterative Ordinary Differential Equation (ODE) solving creates a significant latency bottleneck. Existing solutions face a dichotomy: training-free solvers suffer from significant performance degradation at low Neural Function Evaluations (NFEs), while training-based one- or few-steps generation methods incur prohibitive training costs and lack plug-and-play versatility. To bridge this gap, we propose the Bi-Anchor Interpolation Solver (BA-solver). BA-solver retains the versatility of standard training-free solvers while achieving significant acceleration by introducing a lightweight SideNet (1-2% backbone size) alongside the frozen backbone. Specifically, our method is founded on two synergistic components: 1) Bidirectional Temporal Perception, where the SideNet learns to approximate both future and historical velocities without retraining the heavy backbone; and 2) Bi-Anchor Velocity Integration, which utilizes the SideNet with two anchor velocities to efficiently approximate intermediate velocities for batched high-order integration. By utilizing the backbone to establish high-precision ``anchors'' and the SideNet to densify the trajectory, BA-solver enables large interval sizes with minimized error. Empirical results on ImageNet-256^2 demonstrate that BA-solver achieves generation quality comparable to 100+ NFEs Euler solver in just 10 NFEs and maintains high fidelity in as few as 5 NFEs, incurring negligible training costs. Furthermore, BA-solver ensures seamless integration with existing generative pipelines, facilitating downstream tasks such as image editing.

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13.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.23797

From Task-Guided Conversational Graphs to Goal-Oriented Dialogue Runtimes

作者:

Mariano Garralda-Barrio ↗

Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives. This conceptual systems paper focuses on the high-complexity end of that design space, where goals can be suspended, resumed, revised, and invalidated by actions in other goals. We introduce the Goal-Oriented Dialogue Runtime (GODR), a framework-neutral design pattern that treats goals, task frames, lifecycle state, invalidation rules, and resumption contracts as first-class runtime objects while delegating bounded execution to graph runtimes, agents, tools, or application programming interfaces (APIs). GODR is not proposed as a replacement for workflow graphs in simple guided processes; it is intended for complex, multi-domain, interruptible conversations where objective continuity cannot be recovered reliably from agent identity, chat history, or execution-graph position alone. The paper formalizes the problem, proposes runtime objects and architecture-selection criteria, and frames evaluation as an agenda for future empirical validation rather than as a measured performance claim.

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14.

medRxiv (Medicine) 2026-06-17 DOI: HASH:ae3169d3e881fc5dae138b195cc0fc70

Waning protection of long-acting RSV monoclonal antibodies in infants: a Bayesian analysis of clesrovimab and nirsevimab trial data

作者:

Gong ↗Flasche ↗Hodgson ↗

Clesrovimab and nirsevimab are long-acting monoclonal antibodies used to prevent respiratory syncytial virus (RSV) disease in infants, but waning protection in the first year of life is incompletely characterised. We applied a published Bayesian inference framework to clesrovimab and pooled nirsevimab trial data to estimate time-varying efficacy against medically attended RSV lower respiratory tract infection (LRTI) and RSV-associated hospitalisation, accounting for differences in placebo-arm event timing between trials. Estimated clesrovimab efficacy declined from 60.7% (95% CrI: 46.3-72.6) shortly after dosing to 38.3% (8.6-52.9) at six months against medically attended RSV LRTI, and from 87.1% (71.2-96.2) to 49.6% (10.4-70.7) against RSV-associated hospitalisation. For nirsevimab, corresponding estimates declined from 86.9% (75.4-95.0) to 53.8% (27.4-69.7) against LRTI, and from 77.5% (52.6-91.8) to 49.7% (15.7-68.3) against hospitalisation. After accounting for differences in RSV exposure timing and LRTI endpoint definitions between trials, we found no evidence of a difference in efficacy or waning between clesrovimab and nirsevimab.

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15.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2605.20726

Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference

作者:

Ziang Song ↗Ying Jin ↗Emmanuel J. Cand\`es ↗

arXiv:2605.20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The quality of such methods is often measured by the false discovery proportion (FDP), defined as the fraction of incorrect selections. Existing approaches typically control the expected value of the FDP, using methods such as the Benjamini-Hochberg procedure. This approach fails to provide high-probability bounds on the realized false discovery proportion and invalidates statistical guarantees if the rejection threshold is selected after inspecting the data. This paper establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds, enabling arbitrary post hoc selection of the threshold. Simultaneous validity is achieved by constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution. Furthermore, our framework allows practitioners to modulate the envelope's shape, thereby producing tight bounds in rejection regions of primary interest. We use this flexible approach to derive simultaneous FDP upper bounds for both outlier detection and conformal selection. We demonstrate through synthetic and real-data experiments that the resulting bounds are both valid and substantially less conservative than those derived from existing approaches.

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16.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2605.25516

Strategic Non-Shareability of Quantum Correlations

作者:

Fumin Wang ↗

arXiv:2605.25516v2 Announce Type: replace Abstract: Correlations distributed by a mediator can be useful for coordination but vulnerable to inheritance by a colluder. We formalize the obstruction to such inheritance as a source-certified resource theory of strategic non-shareability. The free objects are symmetrically extendible sources, the free operations are shareability-preserving maps, and the trace distance to the free set is a faithful convex monotone. For Werner and isotropic sources in arbitrary local dimension, the resource has the exact form $D_m=c(d)(p-p_m^{*})_{+}$, with $p_m^{*}$ the Johnson–Viola shareability threshold. For qubit Werner sources, tomographically complete Pauli measurements yield the exact one-colluder capacity\[ C^tomo_1(p)=\frac{1}{12}\Bigl[(3p-1)-\sqrt{(3p+1)(1-p)}\,\Bigr]_{+}.\] We prove that this anti-collusion resource is independent of Bellnonlocality: the Bell and shareability orderings cross, so some Bell-nonlocal states are strictly less collusion-resistant than Bell-local ones. Finally, we give an aligned Pauli coordination game whose observed behaviour has a local hidden-variable model for every visibility, making device-independent certification empty, while source-certified quantum anti-collusion is positive exactly above the extendibility threshold. These results identify symmetric non-extendibility, rather than Bell nonlocality, as the boundary of source-certified collusion resistance.

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17.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.19579

FlowFake: Liquid Networks for Audio Deepfake Detection

作者:

Shivaay Dhondiyal ↗Divyansh Sharma ↗Dinesh Kumar Vishwakarma ↗

arXiv:2606.19579v1 Announce Type: cross Abstract: Audio deepfakes generated by neural text-to-speech and voice-cloning systems threaten speaker verification and public discourse at scale. The core challenge is cross-dataset generalization: detectors trained on one synthesis pipeline collapse on unseen forgeries. We argue that this failure is primarily because of structural synthetic speech artifacts which are multi-timescale trajectory anomalies. Though every existing detector aggregates a fixed-window frame statistics, this misaligns the architecture with the signal. We propose FlowFake, a Liquid Time-Constant (LTC) architecture whose hidden state evolves via a learned ODE, with per-neuron adaptive time constants simultaneously resolving spectral (10ms) and prosodic (2s) cues. At only 34K parameters FlowFake achieves formal BIBO stability and O(dt^4) integration error. On a four-dataset cross domain benchmark (ASVspoof2019-LA, FakeOrReal, InTheWild, MLAAD), FlowFake reaches 75.29% on ASVspoof2019 trained only on FakeOrReal and 79.97% trained only on MLAAD. It outperforms RawGAT-ST and Whisper-DF on every evaluated pair and matching SSL Wav2vec2 (300x larger) at 0.01% of its parameter count. The source code is available on : https://github.com/GhostRider2023/FlowFake

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18.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2602.04588

Entanglement improves coordination in distributed systems

作者:

Francisco Ferreira da Silva ↗Stephanie Wehner ↗

arXiv:2602.04588v2 Announce Type: replace Abstract: Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement offers fundamentally stronger correlations than classically achievable without communication. Crucially, these correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-work optimization problem in a distributed system comprising two servers. The system must process both a continuously available, preemptible baseline task and incoming customer requests arriving in pairs. System performance is characterized by the trade-off between baseline task throughput and customer waiting time. We present a rigorous analytical model demonstrating that when the baseline task throughput function is strictly convex, rewarding longer uninterrupted processing periods, entanglement-assisted routing strategies achieve Pareto-superior performance compared to optimal communication-free classical strategies. We prove this advantage through queueing-theoretic analysis, non-local game formulation, and computational certification of classical bounds. Our results identify distributed scheduling and coordination as a novel application domain for near-term entanglement-based quantum networks.

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19.

medRxiv (Medicine) 2026-06-11 DOI: HASH:c545826f89d1cadb8c1b16b444b7d0fa

Decoding the Genetic Architecture of Autistic Traits in the Aging Population

作者:

Tian ↗Rao ↗Sui ↗Gao ↗Meng ↗Han ↗Wang ↗

Autism research has mostly focused on diagnostic frameworks in childhood. However, autistic traits including social skills, communication, attention switching, attention to detail, and imagination may also vary in many undiagnosed individuals beyond childhood, and the genetic architecture of autistic traits in undiagnosed aging adults remains poorly understood. Here, we performed an exome-wide association study of autistic traits in adults aged >=40 from the UK Biobank (n = 161,269) and independently validated key findings in the SPARK cohort (n = 142,357). We identified exome-wide significance at 17q21.31, represented by a lead variant associated with social skills (rs199533, beta = 0.081, P = 2.04e-11). In addition, we identified an independent signal for communication (rs12632110, beta = 0.042, P = 3.07e-12) and two independent signals for attention switching (rs690733, beta = 0.046, P = 4.26e-12; rs2164272, beta = -0.047, P = 1.73e-12). Gene-based analyses further implicated loss-of-function variation in ZSCAN2 (beta = 1.00, P = 2.44e-6), which was associated with communication differences. Enrichment analyses revealed preferential expression of implicated genes in the cerebral cortex, while phenotypic and neuroimaging analyses linked those variants to cortical brain structure and regional volume. Taken together, these findings delineate the genetic architecture of autistic traits in the aging population and link genetic variation to downstream molecular and neuroanatomical mechanisms.

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20.

medRxiv (Medicine) 2026-06-22 DOI: HASH:f9d6691f8aac92f15c53341c5a819564

The direct economic impact of surgical non-response in orthopaedic hip, knee, and spine surgery for osteoarthritis: a cost-utility analysis

作者:

Rampersaud ↗Y. R ↗Perruccio ↗A. V ↗Collett ↗Sundararajan ↗Du ↗J. T ↗Montoya ↗Power ↗J. D ↗Canizares ↗…

Background Annually, nearly 2 million hip, knee, and spinal inpatient surgeries are performed in Canada and the US for osteoarthritis (OA), costing over $37 billion in hospital expenditures. However, 15-30% of patients experience limited or no improvement, resulting in poor value for money. This study evaluated the one-year cost-utility of joint and spine procedures for OA by comparing non-responders to responders, considering various responder definitions. Methods Individual micro-costing data were collected for 1,175 elective hip, knee, and spine patients enrolled in the Longitudinal Evaluation in the Arthritis Program - Osteoarthritis (LEAP-OA) between 2014 and 2018. Quality-adjusted life years (QALYs) were derived using the SF-6D utility index. One-year incremental cost-utility ratios (ICURs) were calculated from the hospital perspective. Results Responder rates varied by definition, ranging from 78%-94% for hip replacements, 64%-90% for knee replacements, 60%-64% for spine fusions, and 50%-68% for spine decompressions. Corresponding ICURs were: $45,956-$51,773/QALY for responders versus $108,593-$485,762/QALY for non-responders for hip replacements; $54,831-$71,151/QALY for responders versus $200,486-$1,203,596/QALY for non-responders for knee replacements; $65,980-$74,422/QALY for responders versus $262,039-$729,686/QALY for non-responders for spine fusions; and $29,947-$42,168/QALY for responders versus $63,195-$662,586/QALY for non-responders for spine decompressions. Conclusions While surgical response rates were highly dependent on the responder definition, ICURs for non-responders were significantly higher than those for responders across all definitions. Beyond the negative impact on patients, there is a compelling economic argument for investment in improved pre-operative identification of patients at risk of surgical non-response. Such efforts could enable more personalized, value-based care pathways and reduce the provision of low-value surgical interventions.

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21.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.14517

From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails

作者:

Yuguang Zhou ↗Xunguang Wang ↗Pingchuan Ma ↗Zhantong Xue ↗Zhaoyu Wang ↗Shuai Wang ↗

arXiv:2606.14517v1 Announce Type: cross Abstract: LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents. However, we reveal that the very reasoning and task-following capabilities enabling this protection introduce a novel vulnerability: attackers can inject crafted data to trap the guardrail in extended reasoning loops, effectuating a systematic denial-of-service (DoS) attack. To systematically expose this threat, we design a beam-search optimization framework that crafts natural-language payloads to maximize guardrail reasoning length, utilizing an LLM proposer guided by a strategy bank. Based on the observation of guardrail's schema-following nature, we also provide another attack framework driven by mechanism-aware structural mutations with less computational load. The attack efficacy is systematically evaluated in two parts. First, in standalone evaluations, the attack generalizes across diverse guardrail architectures, safety templates, and agent benchmarks. Payloads optimized on a single open-source surrogate successfully transfer to eight leading model backbones (e.g., Claude, GPT, Gemini, DeepSeek, and Qwen), achieving a 13–63$\times$ token amplification. Second, in end-to-end real-world agent deployments (web, desktop, code, and multi-agent systems), the attack reveals up to a 148$\times$ latency amplification. We show that a single poisoned document can saturate shared guardrail infrastructures, effectively starving co-located agents and paralyzing the entire system. By uncovering this availability flaw, our work underscores the urgent need to develop cost-bounded, reasoning-robust guardrails.

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22.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2606.25996

Autodata: An agentic data scientist to create high quality synthetic data

作者:

Ilia Kulikov ↗Chenxi Whitehouse ↗Tianhao Wu ↗Yixin Nie ↗Swarnadeep Saha ↗Eryk Helenowski ↗Weizhe Yuan ↗Olga Golovneva ↗Jack Lanchantin ↗Yoram Bachrach ↗Jakob Foerster ↗Xian Li ↗…

We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data. We describe the overall formulation, and a specific practical implementation, Agentic Self-Instruct. We conduct experiments on computer science research tasks, legal reasoning tasks and reasoning with mathematical objects, where we obtain improved results compared to classical synthetic dataset creation methods. Further, meta-optimizing the data scientist agent itself delivers an even larger performance uplift. Agentic data creation provides a way to convert increased inference compute into higher quality model training. Overall, we believe this direction has the potential to change the way we build AI data.

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23.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2606.19878

On the Oracle Complexity of Interpolation-Based Gradient Descent

作者:

Dongmin Lee ↗William Lu ↗Anuran Makur ↗

arXiv:2606.19878v1 Announce Type: new Abstract: Recent work on first-order optimizers for empirical risk minimization (ERM) has suggested that smoothness of ERM loss functions in the training data, rather than in the optimization parameters, can be leveraged to improve the oracle complexity of gradient descent (GD) methods. In this paper, we propose an inexact gradient method, piecewise polynomial interpolation-based gradient descent (PPI-GD), which approximates the full gradient in each iteration by querying the first-order oracle at equidistant points in the data domain to construct polynomial interpolants of the resulting gradient samples over appropriately sized patches of the data domain. We analyze the oracle complexity of PPI-GD for strongly convex and non-convex loss functions when the data space dimension is bounded by a polylogarithmic function of the number of training samples, and find it to outperform several GD variants in key regimes when the loss function is sufficiently smooth. Furthermore, our analysis extends several techniques from the error analysis of bicubic spline interpolants to the setting of $d$-variate tensor product polynomial interpolants which may be of independent interest in interpolation analysis.

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24.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.13507

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data

作者:

Qixu Chen ↗Satoshi Nakamura ↗

Large-scale mined corpora provide abundant training data for end-to-end speech-to-speech translation (S2ST) but may contain noise, misalignment, and semantic errors. Filtering noisy data is crucial to maintain robust speech translation performance. We study how to train an audio-language model to make keep/drop decisions on paired speech directly from audio. To obtain reliable supervision without manual labels, we adopt a scalable two-stage Rank-to-Distill strategy. A lightweight ranker generates keep/drop pseudo-labels from noisy speech pairs, then trains an audio large language model to predict keep/drop directly from raw paired speech. The resulting model jointly captures acoustic fidelity and cross-lingual semantic consistency for the selection of speech-conditioned data. Experiments on CVSS-C and SpeechMatrix show consistent improvements over unfiltered training, yielding up to +1.4 ASR-BLEU for end-to-end S2ST.

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25.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14764

Avoiding Exponential Blow-Up in Distributive Lattice Submodular Minimization

作者:

Ishant Shanu ↗

Submodular function minimization has gained a lot of interest in recent years. They are highly applicable in the area of Computer Vision and Machine Learning. Often such applications require to work with submodular functions defined on distributive lattice. Current best way of dealing with it is using a transformation which extrapolates the submodular function for the respective boolean lattice. It makes optimization system too inefficient due to enlargement of the working space. Quantitatively, the expanded space has additional exponential (in set size) number of elements. We propose a generic framework for dealing with distributive lattice which only works within distributive lattice. Our framework allows one to use already established submodular function minimization algorithms for boolean lattice. In our experiment, we show the huge improvement in terms of running time over tranditional methods for handling distributive lattice.

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