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01.
arXiv (CS.AI) 2026-06-24

Impatient Bandits: Optimizing for the Long-Term Without Delay

arXiv:2501.07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a bandit problem with delayed rewards. There is an apparent trade-off in choosing the learning signal: waiting for the full reward to become available might take several weeks, slowing the rate of learning, whereas using short-term proxy rewards reflects the actual long-term goal only imperfectly. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Rewards as well as shorter-term surrogate outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that quickly learns to identify content aligned with long-term success using this new predictive model. We prove a regret bound for our algorithm that depends on the Value of Progressive Feedback, an information-theoretic metric that captures the quality of short-term leading indicators that are observed prior to the long-term reward. We apply our approach to a podcast recommendation problem, where we seek to recommend shows that users engage with repeatedly over two months. We empirically validate that our approach significantly outperforms methods that optimize for short-term proxies or rely solely on delayed rewards, as demonstrated by an A/B test in a recommendation system that serves hundreds of millions of users.

02.
arXiv (CS.CV) 2026-06-12

SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images

Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly understood. A correct image-level decision can still hide important failures: a model may correctly flag an artifact while relying on the wrong visual cue, selecting the wrong region, or describing a defect that the image does not support. To evaluate these behaviors directly, we introduce SalArt-VQA, a diagnostic benchmark for fine-grained SALient ARTifact understanding in AI-generated images. SalArt-VQA contains 950 images and 3,681 human-authored multiple-choice questions spanning artifact images, matched real reference images, and paired generated reference images. Four aligned question types evaluate presence detection, semantic localization, spatial grounding, and evidence-grounded defect identification, while the reference splits test calibration and abstention when the annotated defect is absent. Across 20 VLMs, SalArt-VQA reveals failures that image-level detection accuracy hides: the strongest model reaches 99.37% detection recall on artifact images but answers all four artifact-side questions correctly on only 53.26% of images. Comparing artifact images with artifact-free references reveals a sensitivity-calibration tradeoff: sensitive models often make unsupported artifact claims, while conservative models avoid false alarms largely by missing real artifacts. These results show that high artifact detection accuracy alone does not imply grounded artifact understanding. SalArt-VQA exposes these hidden failure modes and provides a fine-grained evaluation of whether VLM artifact claims are supported by local visual evidence.

03.
arXiv (CS.CV) 2026-06-25

Towards a Dynamic and Fixed-budget Memory Bank for Efficient Streaming Video Understanding

Currently, streaming video understanding is still a daunting task for existing multimodal large language models (MLLMs). Its difficulties not only lie in handling the ever-increasing video frames, but also in the unpredictability of future video content and input instructions. In this paper, we study this task from the perspective of constructing a dynamic but fixed-budget memory bank, and propose a novel and training-free approach termed CausalMem. CausalMem is dedicated to constructing a dynamic visual memory update mechanism, thereby maximizing the amount of information in streaming video within a limited memory space, much like the human brain. In practice, CausalMem estimates the redundancy of visual tokens and updates the memory bank via an online semantic basis, which models the principal semantics of the observed video stream. To validate CausalMem, we apply it to two representative MLLMs, namely LLaVA-OneVision and Qwen2.5-VL respectively, and conduct extensive experiments on both streaming and offline video understanding benchmarks. The experimental results not only show the great advantages than existing methods under both streaming and offline settings, e.g., $+3.2\%$ and $+3.0\%$ average accuracy gains respectively, but also witness the superior semantic preservation for streaming videos, e.g., using 12$k$ token budgets to memorize hour-long streaming videos, which achieves more than 20$\times$ visual token compression ratio and only occupies about 82 MB storage. Our code is given in \href{https://github.com/hktk07/CausalMem}{CausalMem}.

04.
medRxiv (Medicine) 2026-06-24

Study protocol and statistical analysis plan for a randomized controlled trial evaluating the safety and feasibility of the recombinant human platelet-derived growth factor B (rhPDGF-BB)-enhanced collagen plug for complex perianal fistula healing

Background A drug-repurposing-specific phenome-wide association study (PheWAS) demonstrated that patients with a single nucleotide variant that decreases expression of platelet-derived growth factor receptor beta (PDGFR{beta}) have a higher prevalence of fistulas, suggesting that PDGFR{beta} signaling is important for tissue repair. Recombinant human platelet derived growth factor B (rhPDGF) is an FDA-approved protein-based therapeutic that signals through PDGFR{beta} to heal and regenerate cutaneous skin wounds, periodontal tissue, and orthopedic bone with a strong safety profile. We hypothesize that rhPDGF will benefit other conditions identified by PheWAS with a similar physiological mechanism as the existing indications, such as complex perianal fistulas that are ineligible for a fistulotomy. Methods and analysis This prospective, blinded, single-site study aims to enroll 12 participants, randomized at a ratio of 2:1, comparing implantation of rhPDGF-enhanced collagen to routine care procedures, and stratified by fistula etiology, idiopathic versus Crohns disease (CD)-related. The primary outcome of this study will evaluate the technical performance of the rhPDGF-enhanced collagen implant for treatment of complex perianal fistulas as measured by the proportion of participants with successful implantation of the intervention without any intervention-related serious adverse events. The secondary outcomes will assess the preliminary safety and efficacy of the intervention based on all intervention-related adverse events, total fistulas healed, rate of fistula recurrence, and change in patient-reported symptoms. Complex perianal fistulas, idiopathic or CD-related, remain a major clinical challenge in need of new multimodal treatments aimed at tissue repair and regeneration. Pharmaceutical rhPDGF stimulation of PDGFR{beta} signaling promotes healing of skin, bone, and soft tissue. PheWAS revealed fistulas as a novel indication for repurposing rhPDGF. This protocol aims to evaluate the technical performance, preliminary safety and efficacy, and feasibility of rhPDGF-enhanced collagen for healing and remission of complex perianal fistulas. Ethics and dissemination This trial was approved by the Vanderbilt University Medical Center institutional review board (IRB#240585). Results will be submitted for publication in a peer-reviewed journal.

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

Information Leakage Detection through Approximate Bayes-optimal Prediction

arXiv:2401.14283v4 Announce Type: replace-cross Abstract: In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem. IL involves unintentionally exposing sensitive information to unauthorized parties via observable system information. Conventional statistical approaches rely on estimating mutual information (MI) between observable and secret information for detecting ILs, face challenges of the curse of dimensionality, convergence, computational complexity, and MI misestimation. Though effective, emerging supervised machine learning based approaches to detect ILs are limited to binary system sensitive information and lack a comprehensive framework. To address these limitations, we establish a theoretical framework using statistical learning theory and information theory to quantify and detect IL accurately. Using automated machine learning, we demonstrate that MI can be accurately estimated by approximating the typically unknown Bayes predictor's log-loss and accuracy. Based on this, we show how MI can effectively be estimated to detect ILs. Our method performs superior to state-of-the-art baselines in an empirical study considering synthetic and real-world OpenSSL TLS server datasets.

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

Effective Faraday interaction between light and Helium-3 nuclear spins in a multi-pass cell

arXiv:2606.20328v1 Announce Type: new Abstract: Helium-3 nuclear spins form an exceptionally stable quantum system with extremely long coherence time, offering exciting opportunities for quantum technologies. In particular, nuclear spin-squeezed states promise enhanced precision for sensing tasks and tests of new physics. A central challenge for all these applications is the realization of a controllable light-nuclear spin interface. Here we experimentally demonstrate such an interface by exploiting metastability-exchange collisions in a low-pressure helium-3 gas cell at room temperature. A radio-frequency discharge produces a small population of metastable atoms that both enables efficient optical pumping and mediates an effective Faraday interaction between the collective nuclear spin and an optical probe. We quantitatively characterize the strength of this interaction as a function of the nuclear polarization, applied magnetic field, and probe-beam parameters. Moreover, we show that using a multi-pass cell enhances this interaction by effectively increasing the optical depth. Extrapolating to a tenfold increase of the probe power used in the present experiment, we project a measurement-induced squeezing rate of 0.52 s$^{-1}$. Our results provide a practical pathway for optical access to helium-3 nuclear spins and open prospects for generating long-lived, macroscopic nuclear spin-squeezed states for quantum metrology.

07.
arXiv (math.PR) 2026-06-16

Plateau Gaps of Poisson Correctors Encode Metastable Reaction Rates

arXiv:2606.14789v1 Announce Type: cross Abstract: Metastable reaction rates are commonly inferred from transition-state fluxes, mean first-passage times, or fitted kinetic models. We show that they are directly encoded in the plateau gap of an occupation-time Poisson corrector. For a centered basin-occupation observable, the Poisson corrector develops metastable plateaus in the reactant and product basins, and their separation determines the forward and backward transition rates. This construction requires only the generator, stationary measure, and metastable partition, and therefore does not rely on a predefined transition-state surface. In overdamped and underdamped double-well dynamics, the plateau-gap rate recovers the Kramers, Grote-Hynes, and Pollak-Grabert-Hänggi hierarchy. The same corrector-martingale decomposition yields a reactive-noise density, revealing where stochastic forcing contributes to transitions in configuration or phase space. Thus, reaction rates and their fluctuation sources emerge from a single corrector field.

08.
arXiv (CS.CL) 2026-06-24

Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants

Conversational product search assistants offer a more expressive, natural, and interactive alternative to traditional keyword-based product search. With limited screen space, showing only a few items increases the need for precise preference elicitation, which can prolong conversations, leading to user frustration and session abandonment. Conversely, rushing to recommend items without a clear understanding of preferences risks poor matches and a degraded user experience. We present Dialogue to Discovery (D2D), an attribute-oriented preference elicitation framework that dynamically exploits the structure of product attributes to efficiently steer conversations toward the user's desired item. D2D adaptively prioritizes the most informative queries and strategically times product recommendations, reducing premature or off-target suggestions that harm engagement. To evaluate D2D, we curate three datasets from the Amazon Reviews corpus. In simulated conversations modelled using a multi-factor utilitarian patience framework, D2D achieves a 22.2-29.9% improvement in target-finding accuracy, 6.6-16.1% reduction in abandonment, and 27.5% shorter average conversations over the state-of-the-art baselines. A complementary user study further confirms significant gains in both user satisfaction and perceived efficiency.

10.
medRxiv (Medicine) 2026-06-15

VarEx: A Large Language Model Pipeline for Automated Extraction of Exposures, Outcomes, and Covariates from Epidemiologic Studies

Objective: Observational studies are essential for investigating risk factors for Alzheimer's disease and related dementias (ADRD), but inconsistent reporting and selection of covariates can contribute to residual confounding, omitted-variable bias, and reduced reproducibility. We developed and evaluated VAREX (Variable Extraction), a large language model (LLM)-based information extraction framework designed to automatically identify exposures, outcomes, and covariates from epidemiologic studies and populate structured evidence repositories. Materials and Methods: VAREX combines retrieval-augmented generation, biomedical language-model embeddings, semantic chunking, cross-encoder reranking, and prompt-engineered LLM workflows to extract epidemiologic variables from full-text biomedical articles. The framework was evaluated using a reference-standard corpus of observational studies examining blood pressure variability (BPV) and Alzheimer's disease-related dementias (ADRD), together with external validation datasets involving other exposure-outcome relationships. Extracted variables were compared with independently curated human reference standards using semantic matching and one-to-one assignment procedures. Covariates were additionally classified into ten epidemiologically relevant semantic categories. Results: In the primary BPV[->]ADRD corpus (10 studies), VAREX achieved a precision of 0.91, recall of 0.84, and F1-score of 0.87 for variable extraction. Covariate classification accuracy was 0.90, yielding a strict extraction-and-classification F1-score of 0.78. External validation datasets demonstrated comparable performance across diverse epidemiologic domains, with extraction F1-scores ranging from 0.73 to 0.85. Category-level performance was strongest for health behaviors (F1=0.96), sociodemographic variables (F1=0.90), and medication exposures (F1=0.89). Compared with published estimates of manual systematic-review effort, VAREX reduced processing time from approximately 61 minutes to 9 minutes per article, representing an 85.7% reduction in review time. Discussion: These findings demonstrate that LLM-based information extraction can accurately identify and classify epidemiologic variables across heterogeneous observational-study designs. Automated extraction enables scalable construction of structured repositories of exposures, outcomes, and covariates while substantially reducing the labor required for evidence synthesis and systematic reviews. Conclusion: VAREX provides an effective framework for automated extraction and classification of epidemiologic variables from the biomedical literature. By supporting large-scale evidence synthesis and structured knowledge resource development, VAREX may facilitate more rigorous observational research, improved confounder identification, and enhanced reproducibility in epidemiology.

11.
arXiv (math.PR) 2026-06-16

The optimal sub-Gaussian normalisation for randomised monotone functions

arXiv:2312.01265v5 Announce Type: replace Abstract: Let $\mathcal{M}$ denote the class of randomised monotone functions on $\mathbb{R}$ with values in $[0,1]$, and let $U_{\mathcal{M}}\colon \mathbb{R}_+\to \mathbb{R}_+$ be the minimal function for which $$ \mathbb{P}\left\{ \sqrt{\eta_f}\, \sup_{t\in\mathbb{R}} \left| f_Z(t) - \Exf{f_Z(t)} \right| \ge \varepsilon\sqrt{U_{\mathcal{M}}(\eta_f)} \right\} \le 2\e^{-2\varepsilon^2} $$ holds for every member $f_Z$ of $\mathcal{M}$ with finite effective sample size $\eta_f$ and every positive $\varepsilon$. We prove that for every $x> 1$, $$ \left| \sqrt{U_{\mathcal{M}}(x)} - \sqrt{\log_4 x} \right| \le 2 \min\!\left\{ 1,\, \frac{2 \ln(\e + \ln x)}{\sqrt{\ln x}} \right\}\,. $$ The optimal adjustment $\sqrt{U_{\mathcal{M}}(x)}$ matches $\frac{1}{\sqrt{2\ln 2}}\sqrt{\ln x}$ for all $x>1$, with residuals bounded as above.

12.
arXiv (CS.AI) 2026-06-25

PVF:Understanding AI Vulnerability Against SDCs

arXiv:2405.01741v4 Announce Type: replace-cross Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies. Unfortunately, the escalating complexity and heterogeneity of AI hardware systems make them increasingly susceptible to hardware faults, e.g., silent data corruptions (SDC), that can potentially corrupt model parameters. When this occurs during AI inference/servicing, it can potentially lead to incorrect or degraded model output for users, ultimately affecting the quality and reliability of AI services. In light of the escalating threat, it is crucial to address key questions: How vulnerable are AI models to parameter corruptions, and how do different components (such as modules, layers) of the models exhibit varying vulnerabilities to parameter corruptions? To systematically address this question, we propose a novel quantitative metric, Parameter Vulnerability Factor (PVF), inspired by architectural vulnerability factor (AVF) in computer architecture community, aiming to standardize the quantification of AI model vulnerability against parameter corruptions. We define a model parameter's PVF as the probability that a corruption in that particular model parameter will result in an incorrect output. In this paper, we present several use cases on applying PVF to three types of tasks/models during inference – recommendation (DLRM), vision classification (CNN), and text classification (BERT), while presenting an in-depth vulnerability analysis on DLRM. PVF has been a critical metric used for making key error management design decisions in productionizing Meta's in-house AI chip - MTIA.

13.
arXiv (quant-ph) 2026-06-19

Charge-Conjugation Violation and Population Asymmetry in Bipartite Fermionic Lattices

arXiv:2606.06138v2 Announce Type: replace-cross Abstract: Charge conjugation violation (CCV) is a central concept in particle physics and appears also for quasiparticles in quantum many-body systems, which typically relies on an embedded external symmetry breaking to the underlying system. An open question is how an intrinsic CCV mechanism could emerge and what its macroscopic consequences would be. We establish sublattice kinks in bipartite fermionic lattices as a concrete setup showing intrinsic CCV. The intrinsic CCV of the sublattice kink is based on the graph-topological nature of the underlying Hamiltonian, with no explicit symmetry breaking taking place. It leads to a population asymmetry of different configurations and imprints a hidden leaf-like structure in the eigenenergy spectrum. The population asymmetry also leads to an imbalanced sublattice-kink production triggered by the vacuum-instability in the quench dynamics. Our work demonstrates the graph topology as the microscopic origin of intrinsic CCV, with the population asymmetry as the macroscopic consequence, of which the proposed setup is highly amenable to experimental implementation via cold-atom quantum simulators.

14.
arXiv (CS.CL) 2026-06-24

Less is More: Quality-Aware Training Data Selection for Scientific Summarization

Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with the source article vary. At the same time, publicly available scientific summarization datasets remain limited in scale and structure for modern long-context models. In this work, we address both challenges by a) constructing and releasing one of the largest biomedical and life science datasets for long-document summarization, containing 1.88 million PMC articles, and b) analyzing the reference quality of author-written abstracts with source-grounded and model-based metrics. We show that author-written abstracts vary in their alignment with the full article and that these quality signals can guide training-data selection. Training on selected high-quality subsets outperforms random sampling at matched training sizes and can match or exceed larger random subsets on factuality-oriented metrics. Our findings suggest that reference quality is an important factor in scientific summarization and that quality-aware data selection can improve training efficiency.

15.
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%.

16.
medRxiv (Medicine) 2026-06-15

Socioeconomic inequalities in smoking prevalence and intensity in Germany: A repeated cross-sectional analysis from 1998 to 2024

Background: Smoking inequalities by socioeconomic status have widened consistently in Germany, but sex-specific trends after 2013 and inequalities in daily cigarette consumption among smokers (intensity) are unknown. We analyzed trends in absolute and relative socioeconomic inequalities in smoking prevalence and intensity among German adults across three decades. Methods: We used 14 waves (1998-2024) of population-representative cross-sectional data from the German Socio-Economic Panel to estimate sex-specific trends in smoking prevalence and intensity in adults aged 25-64. Inequalities were quantified across strata of education, occupation, and equivalized household income using the absolute and relative concentration index with 95% bootstrap confidence intervals. Results: Overall smoking prevalence declined from 35.05% (CI: [33.90%, 36.20%] in 1998 to 22.19% (CI: [21.15%, 23.24%]) in 2024, and mean intensity from 17.49 (CI: [17.09,17.90]) to 13.33 (CI: [12.88, 13.79]) cigarettes/day. Over this period sex-differences in both outcomes narrowed almost completely. Absolute and relative inequalities in smoking prevalence widened across all SES dimensions, particularly for education and occupation. By 2024, inequalities were larger among women than men driven by a stagnating or rising smoking prevalence among low-SES women at least until 2018 alongside continued declines in higher-SES women and for men. Inequalities in smoking intensity, particularly related to income, were generally smaller than those in prevalence. Conclusion: Socioeconomic smoking inequalities in Germany widened from 1998 to 2024 primarily driven by reductions among higher-SES groups and increases in low-SES women. However, recent reductions in low-SES women may indicate a new phase in the smoking epidemic. Health equity considerations should be integrated into a targeted German tobacco control strategy.

17.
arXiv (CS.CV) 2026-06-17

CIAN: Multi-Stage Framework for Event-Enriched Image Captioning via Retrieval-Augmented Generation

Event-enriched image captioning describes not only visible content but also the broader context of events, including timing, location, and participants, capabilities missing in most pixel-bound models. We propose the Contextual Image-Article Narrator (CIAN), a multi-stage framework that enriches captions with external narratives. CIAN retrieves relevant articles using SigLIP, summarizes them to guide a Narrative Generation stage with a LoRA-fine-tuned Qwen model, and applies N-Gram-based Refinement for fluency and coherence. On the OpenEvents-V1 benchmark, CIAN achieves high retrieval performance (mAP 0.979) and improves caption quality, increasing CIDEr from 0.030 to 0.094. These results highlight the effectiveness of retrieval-augmented reasoning combined with linguistic refinement for generating context-aware, human-like captions.

18.
arXiv (CS.LG) 2026-06-11

Minimal surfaces, Knots, and Neural Networks

arXiv:2605.26234v2 Announce Type: replace-cross Abstract: A recent conjecture by Joel Fine posits a relationship between the coefficients of the HOMFLY polynomial of a knot $K$ in the 3-sphere $S^3$, and the signed count of minimal surfaces in hyperbolic 4-space $\mathrm{H}^4$ meeting the sphere at infinity at $K$, with prescribed genus and self-intersection number. In this paper, we develop a novel machine learning framework based on Physics-Informed Neural Networks (PINNs) to solve the minimal surface equation in hyperbolic space. We utilise this framework to test Fine's Conjecture by constructing near-minimal surfaces bounding various families of knots in $S^3$. Furthermore, we develop an algorithmic method to find self-intersections and compute their sign. For every knot analysed, the computationally discovered minimal surfaces and their self-intersection numbers perfectly align with the predictions of Fine's Conjecture, providing empirical evidence for it.

19.
PLOS Medicine 2026-06-18

Association between initial benzodiazepine prescribing patterns and time to benzodiazepine discontinuation: A population-based retrospective cohort study

by Nikki Bozinoff, Tanya S. Hauck, Robert A. Kleinman, Matthew E. Sloan, Beth A. Sproule, Simone N. Vigod, Jennifer Wyman, Priscila Pequeno, Tara Gomes Background Long-term benzodiazepine use has been associated with increased risk of morbidity and mortality. Preventing long-term use through safer prescribing practices has received little attention to date. We sought to better understand associations between initial prescription characteristics and duration of benzodiazepine use. Methods and findings This was a retrospective population-based cohort study of 1,820,808 adults in Ontario with incident benzodiazepine prescriptions between January 1, 2013 and December 31, 2020, with follow-up to December 31, 2021. The primary exposure was duration of the index prescription (≤7 days—referent group, 8–14 days, 15–30 days, or >30 days). Secondary exposures were: (a) duration of action of index benzodiazepine(s) prescription (short-acting, long-acting or both); (b) number of benzodiazepine dispensed on index (1 or 2+); and (c) mean daily dose of the index prescription in Diazepam Milligram Equivalents (DMEs). The primary outcome was time to benzodiazepine discontinuation in days. Multivariable models were adjusted for age, sex, anxiety, insomnia, and substance use disorders as well as other important comorbidities and socio-demographic characteristics. The median age at index was 53 years (Interquartile Range (IQR) 38–67), and 62.6% were women. The median time to discontinuation in women was 16 days (IQR: 6–29) while the median time to discontinuation in men was 19 days (IQR: 6–29). Lorazepam was the most commonly prescribed benzodiazepine on index (63.9%), followed by clonazepam (17.3%) and diazepam (5.8%). In multivariable Cox Proportional Hazards Models, longer index prescriptions were associated with a lower likelihood of benzodiazepine discontinuation (adjusted Hazard Ratio (aHR) 0.54 (95% Confidence Interval (CI) [0.54,0.54]) for 8–14 days; aHR 0.26 (95% CI [0.25,0.26] for 15–30 days and aHR 0.14 (95% CI [0.14,0.14]) for >30 days, compared to ≤7 days, respectively). Being prescribed two or more benzodiazepines versus 1 was also associated with a reduced likelihood of discontinuation (aHR 0.59 (95% CI [0.57,0.61])), as was being prescribed long-acting benzodiazepines (aHR 0.80 (95% CI [0.80,0.80])) or a combination of short and long acting benzodiazepine (aHR 0.84 (95% CI [0.80,0.88])) versus short-acting benzodiazepines alone. Mean daily doses of >5 to ≤10 DME and >10 to ≤20 DME were associated with an increased likelihood of discontinuation (aHR 1.03 (95% CI [1.03,1.03]); aHR: 1.03 (95% CI [1.03,1.04])), whereas doses >20 DME were associated with a reduced likelihood of discontinuation (aHR 0.98 (95% CI [0.97,0.98])) compared with ≤5 DME. Findings may be subject to bias from unmeasured confounding. Conclusion This large population-based cohort study found that prescribing shorter courses of benzodiazepines, use of a single benzodiazepine, use of a short-acting agent, were associated with reduced likelihood of long-term benzodiazepine use. Findings suggest that simple changes to prescribing practices could reduce prolonged benzodiazepine use and the morbidity and mortality associated with long-term use of these medications.

20.
arXiv (CS.AI) 2026-06-24

Infinitesimal Causality

arXiv:2606.24621v1 Announce Type: cross Abstract: This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics. IDC captures the infinitesimal layer in which interventions act as tangent deformations of copy/discard structure. Two distinct Frobenius structures interact: (1) the categorical Frobenius algebra on classical variables encoding copying, comparing, and discarding; and (2) the geometric Frobenius integrability condition, namely involutive closure of the intervention distribution, distinct from the algebraic Frobenius structure. Categorical causal sufficiency is defined as the compatibility of these two notions. A key observation is that, for structural causal models, infinitesimal causality is most naturally formulated in the slice of deterministic mechanisms over exogenous variables, with visible stochastic kernels obtained only after pushforward. Interventions are tangent vectors that deform the Frobenius copy/discard operations; their Lie brackets measure whether this deformation preserves classical information-flow structure. Pearl's do-calculus is used as a guiding example of intervention identities: ignoring irrelevant interventions corresponds to counit invariance, action/observation exchange to coproduct compatibility with pushforward, and independence to involutive bracket closure of the visible intervention distribution.

21.
arXiv (CS.CL) 2026-06-16

EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management

Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive: a good model should not only recognize a user's emotion, but also improve the user's emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2x2 taxonomy covering Support, Defense, Repair, and Charm, which together capture different forms of support, boundary maintenance, trust repair, and rapport building. In each scenario, an LLM simulator plays the user, updates an emotion-relation state after each turn, and maps the final state to an anchor-based score. This design makes EIBench both an evaluation benchmark and a training environment: the final state gives the outcome reward, while the per-turn state updates provide dense feedback for RL. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator's per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9%). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management.

22.
arXiv (quant-ph) 2026-06-16

Forbidden transitions in superconducting artificial atoms

arXiv:2606.06069v2 Announce Type: replace Abstract: Artificial atoms built from Josephson junctions have become a powerful tool to explore the limits of quantum optics due to their strong coupling to electromagnetic fields and their sensitivity to changes at the single-photon level. This sensitivity to quantum fluctuations complements their metrological and computational use, which are based on the precise oscillating frequency of the underlying supercurrents. We present here a theory for Josephson junctions immersed in electromagnetic fields where focus is shifted from temporal correlations and towards spatial ones. Unlike the commonly used circuit and black-box descriptions, our work is based on a microscopic model that enables systematically accounting for the effect of the spatial and vectorial profile of an electromagnetic field over a junction. As an example of the interactions that emerge in such a setup, we investigate the possibility of driving a junction via a quadrupole transition, using typical experimental parameters in existing devices. With the transition being dependent on the gradient of the electric field – rather than its intensity – the junction can be excited in a region where the electric field vanishes.

24.
arXiv (CS.AI) 2026-06-17

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system

arXiv:2606.18108v1 Announce Type: cross Abstract: We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical database. ALeRCE is a community broker for the Zwicky Transient Facility and the Vera C. Rubin Observatory. The system enables users to query the database in natural language (NL) and generates executable SQL queries. To develop and evaluate the system, we constructed a dataset of 110 NL/SQL pairs. We propose a step-by-step generation framework comprising four modules: schema linking, query classification, prompt decomposition, and self-correction. The performance of thirteen LLMs is evaluated using in-context learning and prompt engineering techniques. Text-to-SQL performance is assessed using the perfect-match (PM) rate for row identifiers (e.g., object identifiers) and column identifiers (i.e., column names). The proposed step-by-step framework consistently outperforms a direct-inference baseline, while the self-correction module consistently reduces execution errors. For Claude Opus 4.6, PM performance on row (column) identifiers is high for simple queries, reaching 0.97 (0.94), and decreases with query complexity to 0.44 (0.72) for medium queries and 0.59 (0.49) for hard queries. Among the thirteen evaluated models, the best-performing LLMs for the text-to-SQL task are Claude Opus 4.6, Gemini 2.5 Pro, Gemini 3 Flash, and GPT-5.2-Codex.

25.
arXiv (quant-ph) 2026-06-17

Coherent Dark State Formation of a Lead-Vacancy Spin Qubit in Diamond

arXiv:2605.27841v2 Announce Type: replace Abstract: A lead-vacancy (PbV) center in diamond exhibits coherent emission above the liquid helium temperature, making it highly attractive for quantum network applications. Here, we report the magneto-optical and spin properties of PbV centers in diamond. We record a spin lifetime of 12 ms at 7.5 K under large off-axis magnetic field. Furthermore, we observe formation of the coherent dark state by coherent population trapping and estimate a spin dephasing time of 177 ns at 6.5 K. This work demonstrates the outstanding thermal robustness of the PbV spin compared to other group-IV centers above 4 K.