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01.

medRxiv (Medicine) 2026-06-15 DOI: HASH:8fb88c5b5b1a208ef8a72b4887b291b7

GLLaucoMed: A Secure LLM-Powered Agentic Workflow for Automated Medication Extraction from Free-Text Glaucoma Clinical Notes

作者:

Solages↗Scherer↗Samico↗G. A↗Gutkind↗N. E↗Kang↗Medeiros↗F. A↗Swaminathan↗S. S↗

Purpose: To evaluate the efficacy of large language models (LLMs) in extracting medication-related information from glaucoma clinical notes in the electronic health record (EHR). Design: Cross-sectional. Subjects: 1,250 subjects in the Bascom Palmer Ophthalmic Repository. Methods: Extracted clinical notes from glaucoma-related encounters between 2014 and 2024 were labeled by two glaucoma specialists with a third serving as an adjudicator. Graders were asked to label current topical medications (CTM), proposed changes to topical medications ({Delta}TM), current oral medications (COM), and proposed changes to oral medications ({Delta}OM) in a structured fashion. The dataset was split into development (10%), validation (10%), and test (80%) sets stratified by clinician. Development and validation sets were used to engineer and refine prompts, and the held-out test set was used for model assessment. Five LLMs (Claude Opus 4.6, DeepSeek-V3.2, GPT 5.2, Grok 4.1, and Qwen3.6-35B-A3B) were accessed via Microsoft Azure AI Foundry within a HIPAA-compliant environment. Inter-grader agreement was assessed with Gwet AC1. LLM performance was initially assessed in a binary fashion with F1 scores, and the degree of text match among positive cases was evaluated using exact match accuracy and Jaccard Index (JI). Main Outcome Measures: F1 score, exact match accuracy, JI. Results: Gwet AC1 for intergrader agreement was 0.799, 0.888, 0.985, and 0.988 for CTM, {Delta}TM, COM, and {Delta}OM, respectively. F1 scores for CTM were 0.985, 0.971, 0.978, 0.968, and 0.970 for Claude, Deepseek, GPT, Grok, and Qwen, respectively; for {Delta}TM: 0.905, 0.826, 0.897, 0.842, 0.855, respectively; for COM: 0.923, 0.887, 0.899, 0.906, 0.894, respectively; for {Delta}OM: 0.958, 0.815, 0.937, 0.835, 0.940, respectively. Among positive cases, range of exact match accuracies for CTM (N=1354) was 0.730- 0.882 and range of JIs was 0.809-0.918. For {Delta}TM (N=404), exact match accuracy range was 0.619-0.780 and JI range was 0.668-0.827. For COM (N=47), exact match accuracy range was 0.766-0.872 and JI range was 0.765-0.870. For {Delta}OM (N=25), exact match accuracy range was 0.583-0.920 and JI range was 0.583-0.922. Conclusions: The GLLaucoMed pipeline demonstrated high performance in extracting and standardizing medication data from unstructured clinical notes, including both current medications and proposed changes. Claude and GPT exhibited the strongest performance.

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02.

Nature Medicine 2026-06-12 DOI: HASH:fde650b09ce972374e2d68e7e4b14d0c

Efficacy and target engagement of dopamine agonist pramipexole for anhedonic depression: a randomized placebo-controlled trial

作者:

Filip Ventorp↗

Anhedonia is a core and disabling symptom of mood disorders with limited treatment options. We evaluated the efficacy and safety of the dopamine agonist pramipexole in patients with mood disorders characterized by clinically significant anhedonia. In this single-center, randomized, double-blind, placebo-controlled trial, adults with major depressive disorder, dysthymia or bipolar depression and elevated Snaith−Hamilton Pleasure Scale (SHAPS) scores were assigned (1:1) to flexible dose, once-daily oral pramipexole as add-on treatment or placebo for 9 weeks. The primary outcome was change in SHAPS score from baseline to week 9. Analyses were conducted in the modified intention-to-treat population. Eighty-five participants were randomized, and 82 were included in the analysis. The primary outcome was met: pramipexole was associated with a greater reduction in SHAPS scores compared to placebo (mean difference: −4.04, 95% confidence interval: −6.89 to −1.18, P = 0.006, Hedges’ g = 0.62). Exploratory analyses indicated that pramipexole was associated with increased light physical activity and relative preservation of reward-related ventral striatal activation. Improvements in anhedonia were sustained during a 6-month open-label extension. Pramipexole was generally well tolerated compared to placebo. Pramipexole significantly improved anhedonia and showed a favorable safety profile, supporting its potential as an augmentation strategy in mood disorders. ClinicalTrials.gov identifiers: NCT05355337 and NCT05825235 . Pramipexole, in patients with major depressive disorder, dysthymia or bipolar depression, reduced Snaith−Hamilton Pleasure Scale scores significantly compared to placebo.

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03.

arXiv (math.PR) 2026-06-17 DOI: arXiv:2508.15032

Limit theorems for random Dirichlet series with summation over primes, with an application to Rademacher random multiplicative functions

作者:

Congzao Dong↗Alexander Iksanov↗

arXiv:2508.15032v2 Announce Type: replace Abstract: It is shown that two conjectures put forward in the recent article Iksanov and Kostohryz (2025) are true. Namely, we prove a functional central limit theorem (FCLT) and a law of the iterated logarithm (LIL) for a random Dirichlet series $\sum_p \frac{\eta_p}{p^{1/2+s}}$ as $s\to 0+$, where $\eta_1$, $\eta_2,\ldots$ are independent identically distributed random variables with zero mean and finite variance, and $\sum_p$ denotes the summation over the prime numbers. As a consequence, an FCLT and an LIL are obtained for $\log \sum_{n\geq 1} \frac{f(n)}{n^{1/2+s}}$ as $s\to 0+$, where $f$ is a Rademacher random multiplicative function.

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04.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.23825

From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection

作者:

Yuhan Rui↗Shihan Qiao↗Yibin Lou↗Mingxi Yu↗Yutong Wan↗Yanqiao Chen↗Dongsheng Hou↗Zhen Cao↗Athena Zhuoming Zhong↗Qi Hao↗

arXiv:2606.23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details. Recovering these fragile cues within the spatial domain is notoriously difficult, as it often requires computationally expensive architectural upscaling that inadvertently amplifies background noise. To bridge this gap, we propose a paradigm shift from spatial to spectral feature processing, introducing a holistic solution with the following novelty: (1) A versatile Frequency-Guided Feature Representation framework that generalizes across diverse detector architectures (both CNN and Transformer-based), offering a robust alternative to spatial-only feature extraction; (2) The unified Decompose–Enhance–Reconstruct (DER) operator, instantiated via three lightweight, plug-and-play modules – Wavelet-Difference Gate (WDG), Log-Gabor Enhancer (LGE), and Frequency-Driven Head (FDHead) – to systematically inject frequency-aware modulation into the backbone, neck, and head. This mechanism decouples feature modeling from resolution reduction, capturing discriminative high-frequency components to enable accurate localization with significantly reduced parameter redundancy; (3) Extensive validation on multi-domain benchmarks (VisDrone2019, UAVDT, TinyPerson, DOTAv1) demonstrating consistent gains. Notably, our proposed DERNet series outperforms YOLOv11 models under the same scale while requiring only 1/6 of the parameters, backed by rigorous spectral diagnostics and error decomposition analysis.

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05.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2603.11249

Differentiable Thermodynamic Phase-Equilibria for Machine Learning

作者:

Karim K. Ben Hicham↗Moreno Ascani↗Jan G. Rittig↗Alexander Mitsos↗

arXiv:2603.11249v3 Announce Type: replace Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method combines discrete enumeration of feasible phase states with masked softmax aggregation in the backward pass, with the propagation of the true equilibrium state in the forward pass, using a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural \gls{gE}-models. We show that this approach bears analogy to statistical thermodynamics, and we evaluate it on binary liquid-liquid equilibrium data where it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data.

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06.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11609

Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection

作者:

Meysam Sabbaghan↗Arman Zareian Jahromi↗Doina Caragea↗

Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong performance on this task, single-pass prompting can be brittle when multiple interpretations are plausible. Existing aggregation strategies, such as majority voting or self-consistency, improve robustness by combining labels, but they discard the intermediate reasoning needed to resolve conflicting interpretations. We introduce a multi-agent reasoning framework with adaptive worker allocation for stance detection that shifts aggregation from label-level voting to reasoning-level synthesis. The framework employs a Manager-Worker architecture in which a Manager adaptively allocates a variable number of Worker agents based on input complexity. Each Worker analyzes the input from a distinct perspective and produces a reasoning-only explanation without emitting a stance label; the Manager then synthesizes these explanations to produce the final prediction. We evaluate the proposed framework on SemEval-2016, P-Stance, and COVID-19 Stance using Llama, Mistral, and Gemini. Results show that the framework yields the largest gains on implicit and context-dependent stance cases, achieving 86.07 Macro-F1 on COVID-19 and 82.90 on SemEval-2016, while remaining competitive on more explicit stance datasets such as P-Stance. These findings suggest that adaptive reasoning-level aggregation is most beneficial when stance cannot be reliably inferred from surface cues alone.

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07.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2511.20709

DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents

作者:

Rupam Patir↗Keyan Guo↗Suvadra Barua↗Abhijeet Pathak↗Dinesh Gudimetla↗Jiawei Guo↗Hongxin Hu↗Haipeng Cai↗

arXiv:2511.20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge. We present DualGauge, the first fully automated framework for jointly evaluating correctness and security of specification-only code generation, supported by DualGauge-Bench, a language-agnostic benchmark of 307 coding tasks each paired with functional and security tests derived from the same specification. Evaluating 10 representative LLMs across Python, C++, and JavaScript, we find that functional correctness substantially overestimates reliable code generation: even the strongest model remains below 15% joint security-functionality success in every language. Common model-side factors–scale, extended thinking, quantization, instruction tuning, and code specialization–do not reliably improve joint performance, suggesting secure-and-correct code generation does not simply emerge from stronger coding capability. Evaluation of 3 leading agentic coding systems (Codex, OpenHands, and Claude Code) shows that iterative scaffolding provides no advantage over direct (LLM-based) generation on specification-only tasks. A qualitative audit reveals failures concentrate at the output contract boundary and in guards that exist but are insufficient–patterns that only joint benchmarking reliably exposes.

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08.

arXiv (CS.CV) 2026-06-15 DOI: arXiv:2606.14504

Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks

作者:

Qinlin He↗Zeming Zhuang↗Yongji Wu↗Lan Zhang↗Xiaoyong↗Yuan↗

Physical adversarial attacks on vision systems are typically studied through scene manipulation, such as adversarial patches or projections, where the adversary controls what the camera observes. Camera-side attacks using stickers or auxiliary optics have also been explored, but they treat attacks as image-space perturbations from designed patterns. This misses how physical imperfections interact with scene-dependent lighting and optics. We identify a threat: passive lens-side damage that is persistent yet trigger-conditioned, producing optical artifacts that bias geometric inference under particular visual conditions. We instantiate this threat through Scratch-induced Lens Adversarial Streak Hijacking SLASH, a physical-world attack caused by small scratches on a camera lens or protective cover. Scratches interact with bright light sources and specular reflections to create structured streak artifacts that distort depth cues. Since the perturbation is fixed in the optical path but triggered by the scene, it is both persistent and selective. We formulate the attack in optical space, model the scratch pattern as a trigger-conditioned optical channel, and optimize one fixed configuration across diverse viewing conditions. We evaluate SLASH on monocular depth estimation and monocular 3D object detection in digital and real-world settings. Under the fixed-scratch constraint, directional depth shifts reach up to 32% relative error for monocular depth estimation, with consistent effects on monocular 3D object detection. Physical experiments confirm transfer to real camera recordings, inducing depth shifts above the model's natural prediction baseline. These findings reveal an attack surface where benign-looking hardware imperfections act as latent, scene-triggered adversarial mechanisms, challenging assumptions about physical robustness and motivating defenses for secure vision systems.

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09.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2606.20108

EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors

作者:

Pengwei Wang↗Jos\'e Morano↗Qian Wan↗Hrvoje Bogunovi\'c↗

arXiv:2606.20108v1 Announce Type: cross Abstract: Image quality control is vital for a wide range of downstream applications. Deep learning-based image quality assessment methods typically train classifiers on dataset-specific quality labels, inheriting two limitations: (1) generalization is tied to the labeling criteria of the training set and (2) these methods cannot provide spatial feedback on where the quality is degraded, lacking explainability. In this work, we propose EFIQA, a framework that requires no quality-related supervision and produces spatial quality maps by design. Rather than learning ``what is degradation" from human-annotated labels, EFIQA learns ``what should be there" by leveraging anatomical priors. For fundus photography, we instantiate this as a two-stage approach, by first training an unsupervised anomaly detector via masked anatomical inpainting to identify regions of missing vasculature, and then distilling this prior knowledge into a shallow adapter mapping features of a frozen foundation model to precise quality maps. External-dataset evaluation demonstrates that this label-free approach with minimal adaptation achieves better performance and explainability compared with supervised methods across benchmarks with different quality criteria, highlighting its potential for real-world applications.

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10.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2606.14698

Resolving the Edge of a Quantum Pyramid

作者:

Alvan Arulandu↗

arXiv:2606.14698v1 Announce Type: new Abstract: Standing on the shoulders of giants, we resolve the quantum pyramids conjecture, confirming the globally information-optimal measurement for an ensemble of equiangular equiprobable pure states, as conjectured by Englert and \v{R}eháček (arXiv:0905.0510). We do so by proving the remaining entropy inequalities of Holevo and Utkin (arXiv:2506.06700), which certify optimality for obtuse and flat pyramids. For obtuse pyramids, our key contribution is a rigorous proof that local minimizers of the corresponding entropy inequality cannot have three distinct coordinate values. We show that eliminating this family can be reduced to a neat algebraic reciprocal inequality relating branches of the Lambert $W$ function, which may be of independent interest. For flat pyramids, we prove a tight $\ell^p$ inequality for zero-sum vectors that was recently conjectured, proved analytically in dimension $d=3$, and computationally verified for $d\leq 200$ by Holevo and Utkin (arXiv:2603.24017). We prove this bound for all $d\geq 2$ via a technique in symmetric inequalities known as the equal variables method.

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11.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16981

Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning

作者:

Augusto Peres↗Iker Perez↗Pedro Valdeira↗Guilherme Jardim↗Ana Sofia Gomes↗Hugo Ferreira↗Pedro Bizarro↗

arXiv:2606.16981v1 Announce Type: cross Abstract: Streaming data systems increasingly underpin Machine Learning workflows that maintain large numbers of continuously updated aggregations. In production settings, each incoming event typically triggers read-modify-write operations to persistent storage, making high-frequency state updates a dominant source of latency, contention, and operational cost. In this work, we decouple inference from state persistence in streaming Machine Learning pipelines via probabilistic thinning: every event is scored, but durable state updates are selectively triggered by informative events. Unlike approaches that shed input or state, we show that persistence-path control is achievable without a high-frequency in-memory control plane or cross-worker coordination, relying exclusively on approximate statistics retrieved from disk-backed key-value stores. We model the resulting stochastic processes, derive bounds on filtering rates, and prove that common time-based aggregations remain unbiased under variance-aware formulations, preventing systemic error accumulation. We evaluate the approach in a controlled setting that isolates per-event costs, demonstrating substantial reductions in storage Input/Output and serialization overhead. Across experiments, up to 90% of events are excluded from the persistence path while preserving and in some cases improving downstream utility.

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12.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2503.02636

A Deep Generative Model for Resting-State EEG Synthesis and Transferable Representation Learning

作者:

Yeganeh Farahzadi↗Morteza Ansarinia↗Zoltan Kekecs↗

arXiv:2503.02636v5 Announce Type: replace-cross Abstract: Resting-state EEG provides a non-invasive view of spontaneous brain activity, but extracting meaningful patterns is often limited by scarce high-quality data and reliance on manually engineered features. Generative adversarial networks (GANs) can synthesize neural signals and learn transferable representations directly from raw data, a dual capability that remains underexplored in EEG research. Here, we introduce REST-GAN, a GAN-based framework for resting-state EEG that combines adversarial training with an auxiliary self-supervised reconstruction objective to support signal synthesis and unsupervised feature extraction. Although trained only on raw time-domain signals, without explicit frequency-domain or sensor-topographic supervision, the generated time series reproduced key temporal, spectral, and connectivity properties of real EEG. In band-power feature space, generated samples showed high precision and recall across eyes-open and eyes-closed conditions (EO: 0.91/0.67; EC: 0.87/0.65), while group-average spectral coherence matrices showed low mean absolute differences from real data across frequency bands (~0.01-0.03). The representations learned by the model's critic transferred to independent resting-state demographic classification tasks, outperforming models trained directly on raw EEG and showing competitive performance relative to a recent EEG foundation model, while requiring substantially less training data and computational resources. These findings highlight a computationally efficient, architecture-driven strategy in which generative models serve not only as EEG signal generators, but also as unsupervised feature extractors. This approach may support more data-efficient EEG analysis while reducing reliance on manual feature engineering. The implementation code for REST-GAN is available at: https://github.com/Yeganehfrh/REST-GAN.

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13.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2605.10907

Engineering Robustness into Personal Agents with the AI Workflow Store

作者:

Roxana Geambasu↗Mariana Raykova↗Pierre Tholoniat↗Trishita Tiwari↗Lillian Tsai↗Wen Zhang↗

arXiv:2605.10907v3 Announce Type: replace-cross Abstract: The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined software engineering (SE) processes – iterative design, rigorous testing, adversarial evaluation, staged deployment, and more – that have delivered the (relatively) reliable and secure systems we use today. By focusing on rapid, real-time synthesis, are AI agents effectively delivering users improvised prototypes rather than systems fit for high-stakes scenarios in which users may unwittingly apply them? This paper argues for the need to integrate rigorous SE processes into the agentic loop to produce production-grade, hardened, and deterministically-constrained agent *workflows* that substantially outperform the potentially brittle and vulnerable results of on-the-fly synthesis. Doing so may require extra compute and time, and if so, we must amortize the cost of rigor through reuse across a broad user community. We envision an *AI Workflow Store* that consists of hardened and reusable workflows that agents can invoke with far greater reliability and security than improvised tool chains. We outline the research challenges of this vision, which stem from a broader flexibility-robustness tension that we argue requires moving beyond the ``on-the-fly'' paradigm to navigate effectively.

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14.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2601.21626

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning

作者:

Jinhao Zhang↗Yunquan Zhang↗Zicheng yan↗Boyang Zhang↗Jun Sun↗Daning Cheng↗

arXiv:2601.21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joint framework reshapes the loss landscape by reducing the largest Hessian eigenvalue and reducing its max eigenvalue, thereby significantly enhancing robustness to quantization noise. HeRo-Q requires no architectural modifications, incurs negligible computational overhead, and integrates seamlessly into existing PTQ pipelines. Experiments on Llama and Qwen models show that HeRo Q consistently outperforms state of the art methods including GPTQ, AWQ, and SpinQuant not only achieving superior performance under standard W4A8 settings, but also excelling in the highly challenging W3A16 ultra low bit regime, where it boosts GSM8K accuracy on Llama3 8B to 70.15\% and effectively avoids the logical collapse commonly seen in aggressive quantization.

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15.

medRxiv (Medicine) 2026-06-15 DOI: HASH:43959628b96f9aefe23ac94ed0b82689

Beyond the Apnea-Hypopnea Index: Physiological and Demographic Predictors of Excessive Daytime Sleepiness in Obstructive Sleep Apnea

作者:

Tan↗Parekh↗Wickramaratne↗S. D↗

Excessive daytime sleepiness (EDS) is a common but inconsistently predicted symptom of obstructive sleep apnea (OSA). OSA is typically diagnosed with polysomnography (PSG), and the current standard for severity assessment is the apnea-hypopnea index (AHI). AHI has many limitations, including its inability to explain physiological mechanisms or reflect variability in patient symptoms, such as EDS. This retrospective study aims to find physiological and demographic parameters that better predict EDS in patients with OSA and to evaluate whether these parameters outperform AHI using PSG data from the Mount Sinai Integrative Sleep Center. Clinical variables used to predict EDS included arousal index (AI), average oxygen desaturation during sleep, average heart rate during sleep, and AHI, along with demographic variables including age, sex, and BMI. Hypothesis tests, logistic regression models, and decision tree classifier models were performed on the data to discriminate sleepy from nonsleepy patients as determined by an Epworth Sleepiness Scale (ESS) score [≥] 10. AI and oxygen desaturation were found to be the most predictive physiological variables, and sex and BMI were found to be the most predictive demographic variables. The final decision tree model with these four variables outperformed the AHI in predicting EDS. These findings suggest that daytime sleepiness in OSA can be better explained by measures of apnea burden, oxygenation impairment, and patient demographics than by AHI alone, although these remain only modestly predictive. Future studies should focus on investigating more comprehensive physiological markers, multi-night sleep data, and more objective assessments of sleepiness.

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16.

Nature (Science) 2026-06-15 DOI: HASH:a4b2ed6b9fe20e720e33c5c637584ee4

Daily briefing: Iron-Age human bones were made into tools before interment

作者:

Flora Graham↗

Newly uncovered bones hint at how Iron Age Britons treated their dead. Plus, AI models have failed to beat human mathematicians at research-level problems and the everyday items that make great scientific tools. Newly uncovered bones hint at how Iron Age Britons treated their dead. Plus, AI models have failed to beat human mathematicians at research-level problems and the everyday items that make great scientific tools.

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17.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12921

LoRA-Muon: Spectral Steepest Descent on the Low-Rank Manifold

作者:

Franz Louis Cesista↗Katherine Crowson↗C\'edric Simal↗Stella Biderman↗

arXiv:2606.12921v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) significantly reduces compute and memory costs for finetuning Deep Learning models but is often harder to tune than dense training: when using factor-wise optimizers such as AdamW, it is sensitive to initialization choices, its optimal learning rates transfer poorly across ranks, and it often fails to beat dense baselines. We derive LoRA-Muon by applying the Muon optimizer's spectral steepest-descent rule to the low-rank setting. Along with our split weight-decay rule, our main claim is that LoRA-Muon is a good low-rank proxy for full-rank Muon and Shampoo-family optimizers. Its optimal learning rates transfer across rank, width, depth, and factor-rescaling. In our compute-matched TinyShakespeare study, a rank-$2$ proxy recovers the dense best tested learning rate, and a rank-$32$ LoRA-Muon run attains lower mean validation loss than the dense baseline in the seed-averaged sweep. We further show that the Spectron optimizer depends on arbitrary factor scaling, so it would likely be a poor fit when finetuning starts from badly imbalanced factors, and that LoRA-RITE's simplified QR-coordinate core implements the same spectral update. LoRA-Muon computes that update without QR-decomposition and avoids storing second moments, making it more accelerator-friendly and memory-efficient.

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18.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16950

Latent space mapping of interpretable structural coordinates from stochastic single-molecule signals

作者:

Matteo Cartiglia↗Sandro Kuppel↗Wouter Botermans Wannes Peeters↗Natan Biesmans↗Liam Vandekerckhove↗Eric Beamish↗Koen Ongena↗Wouter Renckens↗Pol Van Dorpe↗Sanjin Marion↗

arXiv:2606.16950v1 Announce Type: cross Abstract: Nanopores are versatile single-molecular sensors, but their utility is fundamentally constrained by stochastic translocation dynamics warping any encoded information. We resolve it by shifting from time-domain analysis to a learned latent-space mapping via a contrastive encoder trained exclusively on simulated signals from a physics-informed model. This encoder maps solid-state nanopore signals of engineered DNA barcodes into an interpretable molecular coordinate system. The learned representation is responsive to structural barcode parameters while remaining invariant to acquisition conditions and translocation conformation, allowing data pooling across devices. Molecule identification requires a single pass through the encoder, reducing computational cost by three orders of magnitude relative to alignment-based methods. We experimentally validate through mixture quantification, rare-variant detection, consensus barcode reconstruction, and real-time signal acquisition. This shift from temporal analysis to mapping structural coordinates into a latent space changes the paradigm behind analyzing stochastic sensor signals by linking classification to interpretable encoded molecular information.

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19.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.14278

Does the Judge Prefer English? Evaluating Language-Switching Invariance in LLM-as-a-Judge

作者:

Shaojie Yin↗

Large language models (LLMs) are now widely used as automatic judges for open-ended instruction-following evaluation. This practice is convenient, scalable, and often more semantically aware than reference-based metrics, but it also introduces a new reliability question: does a judge evaluate the quality of an answer, or does it also react to the language in which the comparison is presented? We propose Judge-LS, a lightweight meta-evaluation protocol that transforms LLMBar response-pair items into English, Chinese, and Chinese-English language-switched variants. A reliable judge should preserve its preference under label-preserving language transformations and should not prefer a language when two answers are translation-equivalent. We evaluate four API-accessible judges on the full 419-item LLMBar benchmark, producing 13,408 successful pairwise judgments. Across models, Chinese and language-switched presentations induce 10.7–14.4% preference flips relative to English, and all judges achieve their highest accuracy in English. However, translation-equivalent tie probes do not reveal a systematic English preference: most probes are judged as ties, and non-tie decisions more often favor Chinese. We add confidence intervals, paired significance tests, and an automatic transformation audit with a sensitivity analysis that excludes mechanically flagged high-risk variants. The experiment requires no model training, uses only API calls, and is feasible on modest local hardware.

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20.

medRxiv (Medicine) 2026-06-12 DOI: HASH:b8d85219779eb06d30e21224cfd45430

Cancer care disruption during the COVID-19 pandemic in Ontario, Canada: A sequential mixed-methods study

作者:

Timilshina↗Jacobson↗Birze↗Wodchis↗W. P↗Kuluski↗Strumpf↗Ammi↗

Introduction The COVID-19 pandemic profoundly disrupted healthcare delivery worldwide, with cancer care among the most affected services. Prior studies documented delays in referrals, reduced specialist access, and increased provider burden. However, the extent to which these experiences were reflected at the system level remains unclear. Objective To document cancer care experiences and examine whether these experiences were reflected in population-level health system indicators across Ontario, Canada. Methods We used an exploratory sequential mixed-methods design. Qualitative data were collected through focus groups and semi-structured interviews with 32 participants, including patients with cancer (n=8), caregivers (n=5), healthcare providers (n=14), and decision-makers (n=5) across two hospital settings in Ontario, Canada. Emergent themes informed the development of quantitative indicators. We then conducted a retrospective population-based analysis of linked administrative health databases for cancer patients in Ontario (n=87,786) to assess the prevalence of identified themes. Results Four themes emerged: (I) delays in diagnosis and screening; (II) disrupted access to primary care; (III) barriers to specialist and mental health services; and (IV) fragmented care for patients with multimorbidity. Quantitative findings corroborated major themes. Screening rates declined for cervical (64.8% to 57.5%) and breast cancer (64.5% to 57.2%). While in-person primary care shifted almost entirely to virtual modalities (8.5% to 95.4%), overall visit volumes remained stable. Specialist care showed uneven patterns, with increased oncology visits but declines in cardiology and mental health services. Patients with multiple comorbidities experienced the largest reductions in non-oncology specialist care. Conclusion The pandemic disrupted key components of cancer care, particularly screening, access to certain specialist services, and care for patients with complex needs. Integrating qualitative and quantitative evidence highlights areas of system vulnerability and underscores the need for coordinated, resilient cancer care capable of maintaining essential services during future crises.

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21.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.13931

DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation

作者:

Li Zhang↗Yuzhen Shi↗Yiran Hu↗Jingwen Zhang↗Wenbo Lv↗Yubo Ma↗Wei Wang↗Rongyao Shi↗Yuanyang Qiu↗Xinran Xu↗Yuemeng Qi↗Linlin Miao↗…

Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most.

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22.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.23743

Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation

作者:

Yitong Li↗Junsong Chen↗Haopeng Li↗Haozhe Liu↗Jincheng Yu↗Ligeng Zhu↗Ping Luo↗Song Han↗Enze Xie↗

arXiv:2606.23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost. Although many acceleration methods have been proposed, a central challenge is that the most effective acceleration strategy is highly instance-specific: a recipe that works well for one combination of model, hardware, and inference configuration often does not transfer to another. Different models vary in architecture, numerical sensitivity, and attention concentration patterns. Inference settings differ in spatial and temporal resolution and video duration, while hardware platforms differ in memory hierarchy, supported numerical formats, and kernel throughput. These factors create a large tuning space, making manual performance engineering costly. We present Sol Video Inference Engine, an agentic, native, training-free acceleration framework for video diffusion models. It organizes five broadly applicable techniques, cache, sparse attention, token pruning, quantization, and kernel fusion, into an agentic acceleration stack for instance-specific optimization. For a concrete deployment target defined by a model, hardware platform, and serving configuration, parallel skill agents optimize the implementation of each technique, an agent integrator composes them into a global acceleration stack, and a human validator provides feedback on generation quality. We instantiate this workflow on three video models with different sizes and architectures: 64B Cosmos3-Super, 22B LTX-2.3, and 2B SANA-Video. With little human effort, the full stack achieves more than 2x end-to-end acceleration while maintaining near-lossless VBench quality, demonstrating the effectiveness of the agent framework for video diffusion acceleration.

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23.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2606.13127

Fully Distributed Multi-View 3D Tracking in Real-Time

作者:

Byron Hernandez↗Fangyu Li↗Aotian Wu↗Paul J. Shin↗Kaustubh Purandare↗Henry Medeiros↗

Multi-camera tracking with overlapping fields of view typically relies on centralized fusion, which creates computational bottlenecks that prevent deployment at scale. We present MV3DT, a fully distributed framework for real-time multi-view 3D tracking that achieves accurate identity propagation and occlusion recovery through peer-to-peer coordination, eliminating the need for central aggregation. Each camera node executes a lightweight modular pipeline comprising monocular 3D perception, distributed multi-view association, and collaborative fusion via lightweight messaging. MV3DT achieves 94.3% IDF1 and 93.3% MOTA on WILDTRACK, competitive with state-of-the-art centralized methods, while demonstrating superior scalability by sustaining 30 FPS on 100 cameras with less than 10 ms inter-camera latency and only 2.2% communication overhead. MV3DT operates in a zero-shot regime given camera calibrations, requiring no scene-specific learning and making it directly deployable in new environments. These results establish MV3DT as a practical solution for real-time multi-view tracking in large-scale overlapping camera networks.

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24.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2603.11417

Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations

作者:

Fatemeh Naeinian↗Ali Hamza↗Haoran Zhu↗Anna Choromanska↗

End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined. When training and evaluation data are geographically mixed, models may implicitly rely on city-specific cues, masking failure modes that would occur under real-world domain shifts when generalizing to new locations. In this work, we formulate zero-shot cross-city transfer as a controlled representation-level stress test for end-to-end autonomous driving and ask how visual pretraining affects transfer behavior under geographic domain shift. We conduct a comprehensive study by integrating self-supervised backbones I-JEPA, DINOv2, and MAE into planning frameworks. We evaluate performance under strict geographic splits on nuScenes in the open-loop setting and on NAVSIM in the closed-loop evaluation protocol. Our experiments reveal a substantial generalization gap when transferring models across cities with different road topologies, traffic conventions, and visual environments. In open-loop evaluation, a supervised backbone exhibits severe degradation when transferring between cities, yet some domain-specific self-supervised methods can substantially reduce both displacement and collision degradation. In closed-loop evaluation, self-supervised pretraining improves average out-of-distribution PDMS in several single-city training settings. Our results provide empirical evidence that representation learning influences the robustness of cross-city planning and motivate zero-shot geographic transfer as an important stress test for evaluating end-to-end autonomous driving systems.

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25.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.13735

VHDLSuite: Unified Pipeline for LLM VHDL Generation with Data Synthesis and Evaluation

作者:

Yijun Shen↗Minghao Shao↗Yichen Zhao↗Zhuoyan Yu↗Boyuan Chen↗Yik-Cheung Tam↗Muhammad Shafique↗

arXiv:2606.13735v1 Announce Type: cross Abstract: Large Language Models (LLM) have shown impressive capabilities in Register Transfer Level (RTL) code generation, particularly for Verilog. However, evaluating their performance with other Hardware Description Languages (HDL), especially VHDL, remains limited although its distinct language characteristics, such as stricter semantic rules, introduce evaluation considerations that differ from Verilog. This lack of coverage restricts fully understanding of how well current models generalize across hardware design languages with differing structures and semantics. To address this gap, we introduce VHDLSuite, a benchmark-centered infrastructure for scalable VHDL generation evaluation, integrating automated benchmark synthesis, executable validation, and multi-model diagnostic analysis. First, we propose a data pipeline that automatically converts Verilog designs and their accompanying testbenches into executable VHDL benchmark instances, followed by VUnit/GHDL-based validation to ensure each released task is compilable, runnable, and consistently checkable in the VHDL environment. Second, we introduce VHDLBench, a benchmark with over 200 VHDL problems with complete and validated testbenches across a wide range of complexity levels. Third, we extensively evaluate cutting-edge LLMs and uncover key challenges specific on LLM-aided VHDL generation. Our findings provide important insights and support future work in multi-language hardware design automation.Our data pipeline, benchmark, and evaluation framework will be open-sourced.

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