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

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

arXiv:2606.15219v1 Announce Type: new Abstract: In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian single-index models? Prior research has shown that any polynomial-time algorithm under the statistical query (SQ) framework requires $\Omega(d^{s^\star/2}\lor d)$ samples, where $s^\star$ is the generative exponent representing the intrinsic difficulty of learning the underlying model. However, it remains unknown whether neural networks can achieve this sample complexity. Inspired by prior techniques such as label transformation and landscape smoothing for learning single-index models, we propose a unified gradient-based algorithm for training a two-layer neural network in polynomial time. Our method is adaptable to a variety of loss and activation functions, covering a broad class of existing approaches. We show that our algorithm learns a feature representation that strongly aligns with the unknown signal $\theta^\star$, with sample complexity $\widetilde{O} (d^{s^\star/2} \lor d)$, matching the SQ lower bound up to a polylogarithmic factor for all generative exponents $s^\star\geq 1$. Furthermore, we extend our approach to the setting where $\theta^\star$ is $k$-sparse for $k = o(\sqrt{d})$ by introducing a novel weight perturbation technique that leverages the sparsity structure. We derive a corresponding SQ lower bound of order $\widetilde{\Omega}(k^{s^\star})$, matched by our method up to a polylogarithmic factor. Our framework, especially the weight perturbation technique, is of independent interest, and suggests potential gradient-based solutions to other problems such as sparse tensor PCA.

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

Q-Learning with Fine-Grained Gap-Dependent Regret

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.

03.
bioRxiv (Bioinfo) 2026-06-22

From hotspot dependence to distributed robustness in resistance-aware lead optimization

Drug resistance remains a recurrent failure mode in targeted anticancer and antiviral therapy, and resistance evidence often enters only after compound selection. ResistAgent is an evidence-constrained framework that converts mutational liabilities into design-time objectives through site- and combo-aware resistance mapping, deterministic mechanism diagnosis and robust counter-design. In EGFR-Erlotinib and HIV-RT-Rilpivirine, the framework separated residue-level liabilities from observed HIV combination liabilities and linked prioritized mutations to anchor loss, pocket rearrangement, electrostatic shifts and contact redistribution. Same-budget paired searches showed that robust objectives changed lower-tail mutant-panel behavior and interaction-dependence profiles while prioritizing robustness over average-affinity behavior. Under predefined liability panels, selected robust-best trajectories shifted support away from mutable hotspot contacts toward more distributed interaction networks. Supplementary physical summaries and ranking-first benchmarks support the scope of this resistance-aware design strategy while preserving clear boundaries for prospective validation.

04.
arXiv (CS.LG) 2026-06-16

LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors

arXiv:2606.15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective. We instead treat these functions through distinct mechanisms: rewards for task specification, constraints for operational limits, energy minimization for gait preference, and exteroceptive perception for adapting energy use to terrain difficulty. We show that these components jointly enable efficient, terrain-adaptive locomotion, and that removing each component exposes a distinct failure mode. Our formulation removes explicit gait priors (including air-time, contact-count, and foot-clearance targets) in favor of emergent behavior. Compared to a conventional complex-reward baseline, our formulation achieves comparable terrain traversal while reducing cost of transport by 56% and operational-limit violations by 96%. The resulting policies transfer zero-shot to a physical Unitree Go2 using LiDAR-based elevation mapping. Project website with videos: https://tinyurl.com/locomposition.

05.
arXiv (CS.LG) 2026-06-19

Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

arXiv:2606.19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incurred at that action. The loss consists of an underlying convex and $\beta$-smooth component and an adversarial perturbation that may be chosen after observing the learner's action. The perturbations are subject to a global budget controlling their cumulative magnitude over time. This framework extends the globally budgeted, post-action perturbation model from underlying linear losses to general convex and $\beta$-smooth losses. For this broader class, we establish expected regret guarantees that explicitly characterize the effect of the perturbation budget. To establish these guarantees, we modify a standard bandit optimization algorithm and develop an analysis that controls the additional regret caused by the perturbations. In the absence of perturbations, our results reduce to regret guarantees for the standard bandit convex optimization setting with $\beta$-smooth losses.

06.
arXiv (math.PR) 2026-06-11

Sample Path Properties of the Fractional Wiener–Weierstrass Bridge II

arXiv:2606.11994v1 Announce Type: new Abstract: Fractional Wiener–Weierstrass bridges are a class of Gaussian processes obtained by replacing trigonometric functions in the construction of classical Weierstrass functions by fractional Brownian bridges. A number of their sample path properties were derived in Schied–Zhang (2024,2026). The analysis in these papers left several open questions, most of which are addressed here. Specifically, we prove that, in the regime in which the Weierstrass mechanism dominates the underlying fractional Brownian bridge, the limiting $b$-adic variation coefficient has an absolutely continuous distribution and is therefore genuinely random. At the critical point between the two roughness regimes, we establish the power-variation formula and the critical $\Phi$-variation limit conjectured in Schied–Zhang (2024). Finally, we derive the Hausdorff dimension for the graphs of the sample paths by proving a conjecture from Schied–Zhang (2026) for the missing high-Hurst case.

07.
Nature (Science) 2026-06-10

Amplified Arctic iceberg traffic reshapes benthic biodiversity

The Arctic is undergoing rapid warming, resulting in retreating sea ice and glaciers1, yet how cryospheric changes propagate into the deep ocean remains poorly understood2. Here we identify a climate-driven mechanism linking accelerating glacier disintegration to an increase in deep-sea hard-bottom habitats far beyond calving fronts. Seafloor observations in Fram Strait show a localized increase in the density and patchiness of dropstones delivered by debris-laden icebergs. At the same time, four decades of shipboard records show that the occurrence of icebergs increased abruptly in the early 2000s. Backtracking links these icebergs to the main outlet glaciers in northeast Greenland and the Russian High Arctic. In northeast Greenland, the timing of glacier destabilization coincides with this rise, whereas sparse satellite coverage in the Russian sector limits temporal attribution despite indications of enhanced glacier activity. A model sensitivity study shows that, apart from intensified calving, a more dynamic sea ice cover enhances downstream transport of glacial ice. Along these pathways, increased iceberg activity could reshape deep-sea habitats through enhanced melt and associated lithogenic input, and elevate navigational hazards as maritime traffic expands in the Arctic. Although modest compared with the iceberg discharges of Pleistocene Heinrich events, this mechanism provides a modern analogue of long-range cryospheric influence on the seafloor in a warming climate. Accelerated Arctic glacier disintegration and a more dynamic sea ice cover are increasing iceberg-delivered dropstones in the deep ocean, reshaping seafloor habitats and extending cryospheric impacts far beyond glaciers.

08.
medRxiv (Medicine) 2026-06-18

Maternal and fetal HLA heterozygosity in preeclampsia: Insights from a large multi-ancestry pregnancy cohort

Preeclampsia (PE) is a leading cause of maternal and neonatal morbidity, with immune dysregulation at the maternal-fetal interface central to its pathogenesis. The highly polymorphic human leukocyte antigen (HLA) region mediates maternal immune tolerance of the semi-allogeneic fetus, yet the contribution of HLA diversity to PE risk remains poorly defined. Whether the HLA heterozygote advantage observed in other immune disorders is relevant to PE has not been systematically evaluated. Using data from the multi-ancestry TOPMed Boston-Colombia Collaborative for Adverse Pregnancy Outcomes (n = 12,790; 4,770 PE, 8,020 controls; 10,808 maternal, 1,982 fetal, including 1,848 pairs), we evaluated associations between heterozygosity across eight classical HLA loci and PE and four sub-phenotypes, adjusting for genetic ancestry. HLA heterozygosity was common across most loci (>80%). No individual maternal HLA locus was associated with overall PE; however, heterozygosity across class I loci showed a protective effect in preterm PE (OR=0.82, 95%CI:0.69-0.97), with a similar pattern for HLA-A heterozygosity (OR=0.78, 95%CI:0.64-0.96). In contrast, fetal heterozygosity at HLA-DQB1 was nominally associated with increased risk of PE (OR=1.36, 95%CI:1.03-1.79) and preterm PE (OR=1.73, 95%CI:1.13-2.73). No individual maternal or fetal HLA alleles were associated with PE. Maternal-fetal mismatch analysis demonstrated locus-specific associations with preterm PE, including increased risk with HLA-DQA1 mismatch and reduced risk with HLA-C mismatch. These findings highlight distinct maternal and fetal immunogenetic contributions to PE risk and underscore the importance of considering HLA diversity-rather than individual alleles alone-in studies of PE etiology.

09.
arXiv (CS.CV) 2026-06-19

OncoReg: Medical Image Registration for Oncological Challenges

In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue by enabling researchers to develop and validate image registration methods through a two-phase framework that ensures patient privacy while fostering the development of more generalisable AI models. Phase one involves working with a publicly available dataset, while phase two focuses on training models on a private dataset within secure hospital networks. OncoReg builds upon the foundation established by the Learn2Reg Challenge by incorporating the registration of interventional cone-beam computed tomography with standard planning fan-beam CT images in radiotherapy. Accurate image registration is crucial in oncology, particularly for dynamic treatment adjustments in image-guided radiotherapy, where precise alignment is necessary to minimise radiation exposure to healthy tissues while effectively targeting tumours. This work details the methodology and data behind the OncoReg Challenge and provides a comprehensive analysis of the competition entries and results. Findings reveal that feature extraction plays a pivotal role in this registration task. A new method emerging from this challenge demonstrated its versatility, while established approaches continue to perform comparably to newer techniques. Both deep learning and classical approaches still play significant roles in image registration, with the combination of methods, particularly in feature extraction, proving most effective.

10.
medRxiv (Medicine) 2026-06-10

Gendered pathways to adolescent mental health: An empirical assessment of a new conceptual framework

Introduction Gender norms and roles are important determinants of physical and mental health in the key period of adolescence. Yet, the gendered pathways to mental health in adolescents are not fully understood. Using a conceptual framework for global adolescent mental health that we developed based on a Delphi process, we empirically investigated the associations between six gender-related constructs and adolescent mental health. Methods We used cross-sectional Gender and Adolescence: Global Evidence (GAGE) data from Ethiopia (2020) to explore the associations between sex, gender norms, psychological competencies, gender attitudes, gender roles, with the latter two also serving as mediators, and psychological distress (GHQ-12), using Structural Equation Modelling (SEM). Results The SEM model contained measurements from 1,584 adolescents, including 843 girls and 741 boys, with a median age of 13 years. Out of 14 pathways tested, we found statistically significant associations between psychological competencies and psychological distress; sex and gender attitudes; and between gender norms and psychological competencies, gender attitudes, and gender roles. Hence, the gender-related constructs were mostly associated with each other, rather than with psychological distress. Conclusion The gender-related constructs are strongly interrelated, thereby attenuating their individual effects on psychological distress. The interplay of gender-related constructs should be considered when developing interventions to promote mental health in adolescents.

11.
arXiv (CS.AI) 2026-06-19

Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow

arXiv:2606.20101v1 Announce Type: cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remaining acoustic content. Despite the remarkable progress of diffusion models, existing training-based editing methods mainly rely on the local inductive biases and cross-attention interaction in convolutional U-Net backbones, which often hinder long-range semantic alignment and precise understanding and localization of instructions. In contrast, diffusion transformers provide stronger global modeling and multimodal fusion, but existing editing architectures usually adopt a simple stack of MMDiT and DiT blocks. Applying joint attention over concatenated audio and text tokens in all blocks results in quadratic complexity with respect to token length. To balance editing performance and efficiency, we propose a hybrid two-stage diffusion transformer architecture for instruction-guided audio editing based on rectified flow matching. It performs joint attention over audio and text tokens to establish coarse semantic alignment at low-resolution stage, then switches to alternating joint-attention and cross-attention blocks to refine editing details at high-resolution stage. This coarse-to-fine strategy enables efficient and accurate instruction-guided audio editing. Experiments show that the proposed framework achieves notable performance gains on challenging editing tasks involving overlapping audio events and complex instructions, while substantially improving editing efficiency with a compact model.

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

ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation

Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unlike prior studies, this paper addresses a more practical scenario in which the predominant category of visual data shifts over time as environments evolve. In this context, we introduce a new notion of continual alignment, which incrementally adapts the alignment module within pre-trained VLMs to preserve high-quality cross-modal representations. Based on this idea, we propose Efficient Continual Alignment (ECA), a novel exemplar-free IL approach for OpenITG. The key challenge is enabling the model to acquire new, task-specific features while minimizing interference with the established alignment without accessing raw data from previous tasks. To address this, ECA employs three core mechanisms: a Mixture of Query (MoQ) module that adapts task-specific query tokens, a Fisher Dynamic Expansion (FeDEx) that dynamically expands model structure based on a Fisher Information Matrix (FIM)-based metric, and an embedding dictionary with Dictionary Replay (DR) to retain past knowledge. To evaluate ECA's performance, we construct four new IL OpenITG benchmarks that better reflect real-world scenarios. Experimental results demonstrate that ECA significantly mitigates catastrophic forgetting and improves IL performance compared to baseline methods. Code and benchmarks are available at https://github.com/Snowball0823/ECA.

13.
arXiv (CS.AI) 2026-06-19

Process-Verified Reinforcement Learning for Theorem Proving via Lean

arXiv:2606.20068v1 Announce Type: new Abstract: While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assistant itself can serve as a symbolic process oracle, supplying both outcome-level and fine-grained tactic-level verified feedback during training. Proof attempts are parsed into tactic sequences, and Lean's elaboration marks both locally sound steps and the earliest failing step, yielding dense, verifier-grounded credit signals rooted in type theory. We incorporate these structured rewards into a GRPO-style reinforcement learning objective with first-error propagation and first-token credit methods that balances outcome- and process-level advantages. Experiments with STP-Lean and DeepSeek-Prover-V1.5 show that tactic-level supervision outperforms outcome-only baselines in most settings, delivering improvements on benchmarks such as MiniF2F and ProofNet. Beyond empirical gains, our study highlights a broader perspective: symbolic proof assistants are not only verifiers at evaluation time, but can also act as process-level reward oracles during training. This opens a path toward reinforcement learning frameworks that combine the scalability of language models with the reliability of symbolic verification for formal reasoning.

14.
arXiv (CS.LG) 2026-06-11

Bergson: An Open Source Library for Data Attribution

arXiv:2606.11660v1 Announce Type: new Abstract: Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applications including debugging undesirable model behavior and training dataset curation. However, significant engineering effort is required to perform it at scale, and many cutting edge techniques lack open-source tooling and support. Bergson is an open source library that aims to enable faster progress in the field by providing a host of techniques that scale to very large language models and pre-training datasets. The library natively supports on-disk gradient stores and multi-node distributed training, and provides quality of life tools for researchers. Finally, we introduce the first open-source implementations of three leading data attribution methods: MAGIC, SOURCE, and TrackStar. The library is available at https://github.com/EleutherAI/bergson .

15.
medRxiv (Medicine) 2026-06-22

Genetic modifiers of psychiatric, motor, and cognitive symptoms in Huntington's disease

The Enroll HD natural history platform provides rich longitudinal phenotypes enabling genome wide analyses across diverse clinical domains. Psychiatric symptoms are a major source of morbidity in Huntington's disease (HD), yet the genetic architecture underlying their onset is poorly understood. We analyzed ~18,000 people with HD (PwHD) to define genetic determinants of ages at psychiatric, motor, and cognitive symptom onset, and HD diagnosis. GWAS meta analysis recapitulated 11 established modifiers of motor onset and identified a novel locus spanning RAB3B/ZFYVE9 associated with age at violent/aggressive behavior onset. Exome wide analyses in Enroll HD participants implicated rare variants in FAN1, PMS1, POLD1, and HTT. Several HD modifiers of motor and cognitive symptom onset (MSH3, FAN1, HTT) also influenced psychiatric symptom onset, whereas PMS1 and POLD1 showed significant association with motor symptom onset. Psychiatric polygenic scores predicted psychiatric symptom onset, revealing a hybrid architecture combining psychiatric liability in general population with HD- or repeat expansion disease (RED) specific pathways.

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

TransitNet: A Compact Attention-Augmented Deep Learning Framework for Low-SNR Transit Blind Searches

arXiv:2606.18932v1 Announce Type: cross Abstract: Motivated by the observational incompleteness of intermediate-to-long-period Earth-size planets, we present TransitNet, a compact attention-augmented deep-learning framework for low-SNR transit blind searches. To enable realistic method development and objective threshold calibration under blind-search conditions, we develop a unified dataset construction, benchmarking, and threshold-selection framework. On recovery benchmarks constructed from unseen Kepler targets, TransitNet attains 95.2 percent accuracy in the challenging SNR range of 6 to 8 and outperforms both TLS and BLS, achieving ROC-AUC and PR-AP values of 0.974 and 0.982, respectively. In an injected Earth-size and sub-Earth-size transit recovery experiment, TransitNet achieves a recovery rate of 93.0 percent, substantially exceeding those of TLS (63.1 percent) and BLS (60.0 percent). In addition to detection, TransitNet provides attention-based estimates of transit windows and midpoints. On an independent evaluation set, 97.4 percent of injected transits are fully covered by the estimated transit window. Applied to real Kepler observations, the model successfully recovers all 34 selected confirmed Kepler planets, with a mean absolute transit midpoint error of 1.24 hours. The model combines a compact footprint of about 1.5 MB with high inference efficiency, yielding speed-ups of about 12 to 25 times relative to CPU-TLS and about 4 to 5 times relative to CPU-BLS. These results demonstrate that TransitNet provides an accurate, scalable, and computationally efficient framework for low-SNR transit blind searches in the tested regime and motivate its extension to longer-period Earth-size planet searches.

17.
arXiv (CS.AI) 2026-06-19

RoboSSM: Scalable In-context Imitation Learning via State-Space Models

arXiv:2509.19658v2 Announce Type: replace-cross Abstract: In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at deployment time, this paradigm supports few-shot adaptation to novel tasks. However, recent ICIL methods rely on Transformers, which have computational limitations and tend to underperform when handling longer prompts than those seen during training. In this work, we introduce RoboSSM, a scalable recipe for in-context imitation learning based on state-space models (SSM). Specifically, RoboSSM replaces Transformers with Longhorn – a state-of-the-art SSM that provides linear-time inference and strong extrapolation capabilities, making it well-suited for long-context prompts. Through diverse experiments on the LIBERO benchmark, we demonstrate the effectiveness of applying SSMs to ICIL, achieving improved generalization to both unseen and long-horizon tasks than Transformer-based ICIL methods by handling longer contexts at test-time. These results show for the first time that SSMs are an efficient and scalable backbone for ICIL. Our code is available at https://github.com/youngjuY/RoboSSM.

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

Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data

arXiv:2606.15950v1 Announce Type: cross Abstract: Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable. Many time-indexed datasets are not exchangeable. They have seasons, recurring regimes, changing frequencies, or other forms of structured dependence. This paper studies a simple way to use that structure. We propose spectral adaptive conformal prediction, a method that forms weighted conformal quantiles using local spectral similarity and then updates the target miscoverage level online. The spectral weights choose calibration residuals that look relevant to the current test point. The adaptive update corrects the long-run miss rate when uncertainty changes over time. We give an approximate coverage result for the fixed spectral weighted quantile and a deterministic long-run calibration result for the adaptive update. Simulations with recurring regimes and slowly changing frequencies, together with three U.S. real-data examples, show that the hybrid method can improve on fixed spectral weighting, while also showing that spectral weighting must be monitored through effective sample size diagnostics.

19.
arXiv (CS.AI) 2026-06-18

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

arXiv:2606.18308v1 Announce Type: cross Abstract: Safe coordination in networked cyber-physical systems forces learning algorithms to simultaneously handle hybrid discrete-continuous actions, hard training-time safety constraints, and physics-governed dynamics. We show that these three features form a directed cycle of biases that defeats any naive composition of off-the-shelf modules, and formalize this as a three-way coupling lemma. We then introduce TRIDENT, the first MARL framework whose three components are co-designed to cancel each leak: a Richardson-Romberg gradient correction reducing Gumbel-Softmax bias from O(tau) to O(tau^2), a Lyapunov-constrained sequential trust-region update enforcing per-iterate feasibility, and a physics-informed residual critic that decomposes value rather than reward. We prove an O~(1/sqrt(K)) convergence rate to a constrained Nash equilibrium and an O(sqrt(K)) cumulative-violation bound. On multi-UAV mobile-edge computing, autonomous intersection management, and a hybrid SMAC variant, TRIDENT cuts training-time violations by 95.5% over MADDPG and 76.3% over MACPO, while improving reward by 13.5% over the strongest unconstrained baseline.

20.
medRxiv (Medicine) 2026-06-10

Estimating COVID-19 Cumulative Incidence from Seroprevalence Surveys accounting for Time-Varying Seroreversion: A Fully Bayesian Methodology

Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning - or seroreversion - biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens.

21.
arXiv (math.PR) 2026-06-16

Malliavin Calculus for the stochastic Cahn-Hilliard equation driven by fractional noise

arXiv:2601.10490v2 Announce Type: replace Abstract: The stochastic partial differential equation analyzed in this work is the Cahn-Hilliard equation perturbed by an additive fractional white noise (fractional in time and white in space). We work in the case of one spatial dimension and apply Malliavin calculus to investigate the existence of a density for the stochastic solution $u$. In particular, we show that $u$ admits continuous paths almost surely and construct a localizing sequence through which we prove that its Malliavin derivative exists locally, and that its law is absolutely continuous with respect to the Lebesgue measure on $\bf R$, establishing thus that a density exists. A key contribution of this work is the analysis of the stochastic integral appearing in the mild formulation: we derive sharp estimates for the expectation of the $p$-th power ($p \geq 2$) of the $L^{\infty}(D)$-norm of this stochastic integral as well as for the integral involving the $L^{\infty}(D)$-norm of the operator associated with the kernel appearing in the integral representation of the fractional noise, all of which are essential for this study.

22.
arXiv (CS.CV) 2026-06-18

SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs

作者:

We propose SpectralDiT, a lightweight modification to flow-matching Diffusion Transformers that adds timestep-conditioned spectral correction to the MLP residual branch. The module decomposes each residual update into low- and high-frequency components on the patch-token grid, then learns a zero-initialized additive gate so the model initially matches the baseline DiT. On CIFAR-10 pixel-space generation, SpectralDiT improves FID from 20.78 to 19.71 at patch size 1 and reduces the radial Fourier spectrum gap. Furthermore, we scale our method to latent diffusion on ImageNet-100. With 0.6% additional theoretical FLOPs and 1.36% additional parameters, SpectralDiT improves latent flow-matching, achieving an 8.7% relative FID reduction under classifier-free guidance (CFG 2.0). All reported results are averaged over five seeds. Ablations and gate visualizations on CIFAR-10 reveal stable block-specific spectral correction patterns.

23.
arXiv (CS.CL) 2026-06-15

An Empirical Study of Automating Agent Evaluation

Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably automate this evaluation process? Our study shows that simply prompting coding assistants is insufficient for this task. Without domain-specific evaluation knowledge, frontier coding assistants achieve only a 30% execution success rate and produce over-engineered evaluations averaging 12+ metrics per agent, indicating that strong coding ability does not automatically translate to reliable agent evaluation. We introduce EvalAgent, an AI assistant that automates the end-to-end agent evaluation pipeline. EvalAgent encodes evaluation domain expertise as evaluation skills (procedural instructions, reusable code and templates, and dynamically retrieved API documentation) that compose into a trace-based pipeline producing complete evaluation artifacts including metrics, executable code, and reports. To systematically assess generated evaluations, we introduce a meta-evaluation framework alongside AgentEvalBench, a benchmark comprising 20 agents, each paired with evaluation requirements and test scenarios. We further propose the Eval@1 metric to measure whether generated evaluation code both executes and yields meaningful results on the first run. Our experiments show that EvalAgent produces focused evaluations, improving Eval@1 from 17.5% to 65%, and achieving 79.5% human expert preference over baseline approaches. Further ablation studies show that evaluation skills are critical for handling complex evaluation: removing them causes Eval@1 to drop significantly from 65% to 30%.

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

TW-LegalBench: Measuring Taiwanese Legal Understanding

Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal reasoning remains underexplored. We present TW-LegalBench that utilizes Taiwanese legal system's rich official corpus open to the public to fill the gap in evaluating LLMs on Taiwanese law, among common-law benchmarks that focus on English sources and civil-law benchmarks focusing on sources of Simplified Chinese. TW-LegalBench comprises three task types: (1) over 16,000 multiple-choice questions (MCQs) across five years of official examinations in 18 professional domains; (2) 117 open-ended essay questions (OEQs) from examinations for legal professionals with official scoring rubrics; and (3) more than 14,000 legal judgment prediction (LJP) instances covering hundreds of crime categories. We evaluate 13 LLMs using accuracy for MCQs, a decomposed LLM-as-Judge framework based on the scoring rubric points for OEQs, and metrics for sentencing accuracy and statute citation for LJP. Our results reveal that top-performing models exceed the passing threshold for qualified lawyers (passing rate: 11%) but fall short of that for judges and prosecutors (passing rate: 1~2%). For LJP, while models demonstrate reasonable verdict type accuracy and sentence prediction capability, they struggle to cite exact legal articles. These findings highlight that reliable legal text generation remains challenging for LLMs, even though their performance on qualification examinations approaches human level.

25.
PLOS Medicine 2026-05-14

First-trimester nonsteroidal anti-inflammatory drugs exposure and risk of major congenital malformations: A retrospective register-based cohort study

by Ariel Avraham Hasidim, Itamar Ben Shitrit, Daphna Idan, Tal Michael, Amalia Levy, Gali Pariente, Eitan Lunenfeld, Sharon Daniel Background Pain and fever are common in early pregnancy, yet their management poses a major clinical dilemma. Although not confirmed, recent studies have raised safety concerns regarding acetaminophen. Evidence on the use of nonsteroidal anti-inflammatory drugs (NSAID) in the first trimester remains inconclusive. This uncertainty has left clinicians with limited evidence to guide treatment decisions. This study evaluated the association between first-trimester NSAID exposure and the risk of major congenital malformations (MCMs) in a large, population-based cohort of pregnancies. Methods and findings We conducted a population-based retrospective cohort study within the Southern Israeli Pregnancy Registry (siPREG) project, including all singleton pregnancies of women aged 15–45 years resulting in live births, stillbirths, or elective terminations for fetal malformations at a Soroka University Medical Center between 1998 and 2018. Pregnancies exposed to established teratogens, multiple gestations, and those with documented genetic or chromosomal anomalies were excluded. First-trimester NSAID exposure was defined by pharmacy dispensations (overall and by specific agents). MCMs were identified from linked clinical, hospitalization, and termination records through the first postnatal year.Propensity scores were estimated using covariates selected via a directed acyclic graph, including maternal age, ethnicity, diabetes, medical indication for NSAID use, exposure to other antipyretics, obesity, smoking, folic-acid use, gravidity, perinatal care, and year of pregnancy. Generalized full matching was used to balance covariates. Adjusted risk ratios were derived using weighted Poisson regression with G-computation, and two-way cluster-robust standard errors, jointly clustering by maternal identifier and matching subclass. Sensitivity analyses included a dose–response assessment across defined-daily-dose (DDD) categories and a tipping-point analysis evaluating the impact of potential misclassification from unrecorded over-the-counter NSAID use.A total of 264,858 singleton pregnancies were included in the final cohort; 20,202 (7.6%) were exposed to NSAID, most commonly ibuprofen (5.1%), diclofenac (1.6%), and naproxen (1.2%). NSAID exposure, in total and as individual agents, was not associated with MCMs overall (8.2% versus 7.0%; matched-adjusted-Relative Risk (aRR) = 0.99 (95% CI [0.90,1.10])) or with organ-system-specific MCMs, including cardiovascular (matched-aRR = 1.05 (95% CI [0.92,1.20]), musculoskeletal (matched-aRR = 1.03 (95% CI [0.77,1.39])), central nervous system (matched-aRR = 0.77 (95% CI [0.53,1.11])), cleft palate (matched-aRR = 0.95 (95% CI [0.47–1.91])), gastrointestinal (matched-aRR = 1.03 (95% CI [0.64–1.63])), and genitourinary (matched-aRR = 0.99 (95% CI [0.72,1.35])) malformations. Dose–response analyses showed no significant association with MCMs across cumulative NSAID exposure: short-term (1–7 DDD, matched-aRR = 1.06 (95% CI [0.97,1.15]), medium-term (8–21 DDD, matched-aRR = 1.10 (95% CI [0.99,1.22]), and long-term (>21 DDD, matched-aRR = 1.24 (95% CI [0.94,1.63])). The main limitation was the potential for minor exposure misclassification due to over-the-counter availability of ibuprofen, although sensitivity analyses simulating such misclassification suggested minimal impact on the risk estimates. Conclusion In this large, population-based cohort, we found no evidence supporting an association between first-trimester exposure to NSAID and MCMs, providing reassuring evidence regarding their fetal safety in early pregnancy.