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01.
arXiv (CS.CL) 2026-06-11

A PubMed-Scale Dataset of Structured Biomedical Abstracts

作者:
Chia-Hsuan Chang↗Haerin Song↗Brian Ondov↗Hua Xu↗

Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis. However, a vast portion of abstracts indexed in PubMed remain unstructured, presenting a significant bottleneck for downstream text-processing workflows and applications. To resolve this limitation, we introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database, encompassing over 23.2 million research-article records. The corpus is divided into two distinct subsets: a collection of 5.9 million author-structured abstracts parsed from official XML files, and an automatically labeled collection of 17.2 million originally unstructured abstracts structured via a verbatim-extraction Large Language Model pipeline. Every record is harmonized under a unified five-section schema and mapped to its original PubMed identifier, publication type, and publication date. This dataset can be utilized to train sentence-classification models, benchmark text-segmentation architectures, and perform large-scale, section-specific information extraction at an unprecedented PubMed-wide scale.

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03.
arXiv (CS.CL) 2026-06-19

Token-Operations-Oriented Inference Optimization Techniques for Large Models

作者:
Shiguo Lian↗Kai Wang↗Zhaoxiang Liu↗Wen Liu↗Minjie Hua↗Yutong Liu↗Jiangze Yan↗Xin Wang↗Cong Wang↗Yilin Zhang↗Yi Shen↗Jieyun Huang↗…

Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large model services. Centered on token-oriented inference optimization technology, this paper proposes for the first time a four-layer technical architecture consisting of Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion. It systematically reviews the key technologies and current industry status across these four levels and analyzes the application value of related technologies in real-world business scenarios. This paper provides a practical technical path for reducing token production costs, improving token service efficiency, ensuring the stability of token supply, and driving the transition of large model services from being merely callable to being operable.

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04.
arXiv (CS.LG) 2026-06-19

Influence-Guided Concolic Testing of Transformer Robustness

作者:
Chih-Duo Hong↗Chih-Cheng Yang↗Yu Wang↗Fang Yu↗

arXiv:2509.23806v2 Announce Type: replace-cross Abstract: Concolic testing for neural networks alternates concrete execution with constraint solving to search for inputs that flip model decisions. We present a concolic tester for Transformer classifiers that uses SHAP estimates to rank pending path predicates by their impact on the current prediction. To support self-attention with multiple heads in execution backed by SMT solving, we implement attention semantics in pure Python that are compatible with the solver and make the softmax boundary explicit by concretizing exponentiation arguments. We evaluate our method on CIFAR-10 across three compact Transformer classifiers, ResNet18, and VGG16 under a one-pixel budget and a 900s horizon. Across the 500 model–input pairs in this matched comparison, our method achieves 60% success, compared with 15% for a differential evolution baseline that treats the model as a black box. In the primary two-layer Transformer branch-ordering study, SHAP-based predicate prioritization raises success from 56% to 60% and reduces median attack time by 51%. These results show that influence-guided path exploration can make concolic testing a practical way to find adversarial examples in Transformer models.

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05.
arXiv (quant-ph) 2026-06-24

Quantum Metric Bound State of Light

作者:
Jinchao Zhao↗Rongning Liu↗Xue-Yang Song↗K. T. Law↗

arXiv:2606.22479v2 Announce Type: replace-cross Abstract: The spatial confinement of defect-induced bound states is conventionally governed by the effective mass in dispersive bands. More recently, Compact Localized States (CLSs) arising from exact destructive interference have been utilized to achieve confinement in flat bands. However, CLSs rely on pristine lattice symmetries and fine-tuned defect profiles. The introduction of a generic local impurity inevitably breaks these strict phase-matching conditions, resulting in extensive bound states whose fundamental length scale has remained an open question. Here, we establish a third regime of confinement: the quantum metric bound state. We provide a rigorous mathematical proof demonstrating that in the absence of kinetic energy and CLS protection, the exponential decay length of these states is lower-bounded by the quantum metric of the unperturbed flat band. We demonstrate the tightness of this geometric limit by constructing a family of highly tunable flat-band generators, and we verify its universality across diverse realistic architectures. Ultimately, this classification establishes the independently measurable quantum metric as a predictive design principle for engineering confined modes in synthetic wave platforms.

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

Single Photon Cross-Phase Shifts Can Be Enhanced by Localization in both Frequency and Time

作者:
Xinyu Jiao↗Vida-Michelle Nixon↗Kyle Thompson↗Aephraim Steinberg↗

arXiv:2606.11516v1 Announce Type: new Abstract: Single-photon optical nonlinearities face a fundamental trade-off: maximum nonlinearity requires both spectral resonance (narrow bandwidth) and high peak intensity (short duration), constraints that are incompatible due to the time-energy uncertainty relation. We demonstrate experimentally that this limitation does not need to exist in cases involving post-selection. We measure a cross-phase shift (XPS) produced by a resonant photon from a narrow-band source that is first transmitted through a cold atomic cloud and then localized in time through detection. The peak size of this XPS is greatly enhanced compared to that of Gaussian single-photon-level pulses without post-selection, benefiting from the narrow bandwidth of the resonant prepared state and the high intensity of the post-selected state simultaneously. We measure enhancements in the peak XPS of 6$\pm$1 at an optical depth (OD) of 2.4$\pm$0.1, and our results are in qualitative agreement across a range of optical depths with the recently developed weak value theory of atomic excitation [Thompson et al., APL Quantum 2, 036108 (2025)] for such post-selected photons. This work uncovers new consequences of having simultaneous knowledge of frequency and time, raising new foundational questions about how a particle behaves, and interacts with other systems, when its preparation and post-selection are non-commuting.

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07.
arXiv (CS.AI) 2026-06-16

Decision-Weighted Flow Matching for Contextual Stochastic Optimization

作者:
Jize Xie↗Haomiao Wu↗Qiang Chen↗Xiu Su↗Yi Chen↗

arXiv:2606.16790v1 Announce Type: cross Abstract: Conditional generative models are increasingly used as scenario generators for stochastic optimization, but standard training objectives emphasize uniform distributional fit rather than the downstream decisions induced by generated scenarios. This creates an objective mismatch: errors in statistically common regions may have little effect on decision regret, whereas errors in decision-sensitive regions can substantially change the optimal action. We propose Decision-Weighted Flow Matching (DW-FM), a regret-aligned training framework that preserves the simplicity of standard flow matching while reweighting its velocity-regression objective using decision-sensitive endpoint information. Theoretically, we connect downstream regret to pathwise velocity mismatch through a loss-induced decision discrepancy and an adjoint transport argument, yielding an ideal regret-aligned surrogate and practical endpoint-weighted objectives with regret guarantees. Empirically, we demonstrate the effectiveness of DW-FM on three CVaR-based contextual stochastic optimization benchmarks spanning synthetic portfolio, semi-real financial, and traffic-CVaR tasks, where DW-FM improves downstream regret over standard baselines.

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

ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark

作者:
Michael Shalyt↗Rotem Elimelech↗Ido Kaminer↗

Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization with genuine reasoning. To address this gap, we present ASyMOB, a high-resolution dataset of 35,368 validated symbolic math problems spanning integration, limits, differential equations, series, and hypergeometrics. Unlike prior benchmarks, ASyMOB systematically perturbs each seed problem using symbolic, numeric, and equivalence-preserving transformations, enabling a fine-grained assessment of generalization. Our evaluation reveals three key findings: (1) most models' performance collapses under minor perturbations, while top systems exhibit an apparent regime shift in robustness; (2) integrated code tools stabilize performance, particularly for weaker models; and (3) we identify examples where Computer Algebra Systems (CAS) fail while LLMs succeed, as well as problems solved only via a hybrid LLM-CAS approach, highlighting a promising integration frontier. ASyMOB serves as a principled diagnostic tool for measuring and accelerating progress toward building verifiable, trustworthy AI for scientific discovery.

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09.
arXiv (CS.CL) 2026-06-16

Modeling Sarcastic Speech: Semantic and Prosodic Cues in a Speech Synthesis Framework

作者:
Zhu Li↗Yuqing Zhang↗Xiyuan Gao↗Shekhar Nayak↗Matt Coler↗

Sarcasm is a pragmatic phenomenon in which speakers convey meanings that diverge from literal content, relying on an interaction between semantics and prosodic expression. However, how these cues jointly contribute to the recognition of sarcasm remains poorly understood. We propose a computational framework that models sarcasm as the integration of semantic interpretation and prosodic realization. Semantic cues are derived from an LLaMA 3 model fine-tuned to capture discourse-level markers of sarcastic intent, while prosodic cues are extracted through semantically aligned utterances drawn from a database of sarcastic speech, providing prosodic exemplars of sarcastic delivery. Using a speech synthesis testbed, perceptual evaluations show that semantic and prosodic cues enhance perceived sarcasm, with the combined system achieving the best downstream F1 while maintaining high subjective sarcasm ratings. These findings highlight the complementary roles of semantics and prosody in pragmatic interpretation and illustrate how modeling can shed light on the mechanisms underlying sarcastic communication.

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10.
arXiv (CS.CV) 2026-06-12

Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings

作者:
Laura Paul↗Holger Rauhut↗Martin Burger↗Samira Kabri↗Tim Roith↗

Recent advances in imaging technologies, deep learning and numerical performance have enabled non-invasive detailed analysis of artworks, supporting their documentation and conservation. In particular, automated detection of craquelure in digitized paintings is crucial for assessing degradation and guiding restoration, yet remains challenging due to the possibly complex scenery and the visual similarity between cracks and crack-like artistic features such as brush strokes or hair. We propose a hybrid approach that models crack detection as an inverse problem, decomposing an observed image into a crack-free painting and a crack component. A deep generative model is employed as powerful prior for the underlying artwork, while crack structures are captured using a Mumford–Shah-type variational functional together with a crack prior. Joint optimization yields a pixel-level map of crack localizations in the painting.

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

Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis

作者:
Yucheng Xing↗Ling Huang↗Pei Liu↗Jingying Ma↗Jiaqing Xu↗Kai He↗Mengling Feng↗

arXiv:2606.19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across clinical centers. This limitation stems from their reliance on pixel-derived representations that are highly susceptible to domain-specific artifacts caused by staining protocols and scanner hardware. We hypothesize that high-level pathology semantics, such as tumor grade and micro-environmental architecture, provide a domain-invariant semantic representation that mirrors the robust diagnostic logic of human pathologists. Therefore, we propose a Semantic-Anchored Evidential Fusion Survival (SAEFS) framework, where SAEFS derives semantic anchors from WSIs via Visual Question Answering (VQA), employs a dual-stream WSI evidence extraction architecture, uses Dirichlet-based Subjective Logic to model uncertainty, and fuses semantic and visual evidence through a cautious conjunction rule to avoid overconfident fusion from correlated sources. Trained exclusively on one source domain and evaluated zero-shot across four unseen domains, SAEFS consistently outperforms state-of-the-art models both in prediction accuracy and reliability, improving the average C-index by 10.2%. Quantitative analyses further show that VQA-derived semantic features exhibit significantly lower cross-center divergence than pixel-derived features, highlighting their robustness for cross-center clinical applications.

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12.
arXiv (CS.LG) 2026-06-11

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

作者:
Zhuoyi Peng↗Hanlin Gu↗Lixin Fan↗Yi Yang↗

arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read topology and fail on cold nodes; LLMs read text and fail on text-ambiguous nodes. Existing LLM-GNN methods all follow the same recipe: designate one model as the golden teacher and use its outputs (e.g., features or pseudo-labels) to supervise the other. We argue this golden-teacher assumption breaks under sparse supervision: neither model is golden, and treating either as such transfers its blind spots into the student. We therefore ask: can we avoid designating either model as the golden teacher, and still perform effective graph learning? We answer with LLM-GNN Co-Teaching, a bidirectional co-teaching framework in which neither model is fixed as teacher. The GNN and LLM exchange their most confident pseudo-labels under an architecture-specific small-loss criterion, and both update every round. Supervision is then mined from the trajectory: whenever a node moves from cross-model contradiction at round t to cross-model agreement at round t+1, the LLM's two answers on the same input form a preference pair (old contradicting self < new peer-endorsed self) for DPO training. We call this Round-based Pseudo-Label Preference Optimization (RPL-PO). On six benchmarks, LLM-GNN Co-Teaching consistently outperforms GNN-as-Judge and all prior methods, with absolute 3-shot gains of 7.86% on Cora and 7.73% on ogbn-arxiv; improvements carry over to 5-shot and to zero-shot cross-dataset transfer. Error-structure analysis further shows that abandoning the golden-teacher assumption substantially improves the LLM's graph learning capability on challenging samples.

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13.
arXiv (CS.LG) 2026-06-17

Constrained Diffusion Models with Primal-Dual Inference

作者:
Samar Hadou↗Yigit Berkay Uslu↗Alejandro Ribeiro↗

arXiv:2606.17192v1 Announce Type: new Abstract: This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimization problems with average constraints. We formalize constrained sampling in the Lagrangian dual domain, where the optimal distribution takes the form of a Gibbs distribution indexed by the optimal dual variable. Rather than estimating this dual multiplier before sampling and freezing it throughout generation, PDI jointly infers the optimal primal distribution and its parametrizing dual variable. Each reverse diffusion step denoises using the score field associated with the current multiplier and then updates the multiplier through dual ascent using the estimated constraint violation of the denoised samples. To enable this conditional score field, we train a single dual-conditioned score network over the family of Gibbs distributions induced by the dual variables encountered during inference. We prove that the time average of the dual variables generated along the inference trajectory converges to a neighborhood of the dual optimum and bound the effect of residual dual mismatch on the terminal distribution through schedule-dependent stability factors. We evaluate PDI on constrained sampling from a mixture of Gaussians, wireless resource allocation, and portfolio management.

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14.
arXiv (CS.AI) 2026-06-19

Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification

作者:
Sinclair Gurny↗Ryan Quinn↗

arXiv:2606.19568v1 Announce Type: cross Abstract: Acoustic gunshot detection is a problem with applications across civilian public safety, military operations, and wildlife conservation, yet the field lacks a rigorous exploration of feature extraction techniques with a focus on generalization to realistic data. The mixed effectiveness of commercial gunshot detection and classification systems indicates an open problem that is not adequately addressed by the current literature. In this paper, we present a systematic investigation of common feature extraction techniques using a dataset of 23,000 gunshot recordings across 85 firearms and 21 calibers. We benchmark three feature extraction techniques with 12 total unique parameter sets using ResNet-18. Our results demonstrate that using the correct feature extraction technique can improve top-1 accuracy by up to 20%, and utilizing the correct parameters for a given feature extraction technique can improve that value by up to 4.7%.

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15.
arXiv (CS.CL) 2026-06-18

EARS: Explanatory Abstention for Reliable Sub-Agent Modeling in Large-scale Multi-Agent Systems

作者:
Shuang Xie↗Yunan Lu↗Han Li↗Lingyun Wang↗

In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweight, domain-specialized sub-agents. While this architecture improves modularity, scalability, and cost efficiency, its reliability depends not only on accurate routing but also on sub-agents' ability to calibrate their responses to capability constraints. In particular, sub-agents built on smaller fine-tuned models often struggle with such calibration, leading them to over-answer ambiguous, underspecified, misrouted, or unsupported requests and produce hallucinated outputs instead of actionable feedback. To address this challenge, we present EARS (Explanatory Abstention for Reliable Sub-Agent Modeling), a production-oriented framework that reframes sub-agent abstention as an inter-agent communication protocol: a sub-agent does not merely abstain, but exposes an actionable failure state to the coordinator. EARS curates human-agent interaction data using an ensemble of calibrated LLM-as-a-Judge models, producing structured abstention labels and rationales under a taxonomy of sub-agent failure modes. These data are used to fine-tune sub-agents to detect failure conditions and return rationales for coordinator-level clarification, rerouting, or fallback. We evaluate EARS in a large-scale production e-commerce assistant supporting enterprise business intelligence workflows. EARS improves the overall response pass rate from 68.5% to 78.9%, demonstrating that sub-agent-side explanatory abstention improves MAS reliability.

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16.
arXiv (CS.CV) 2026-06-16

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

作者:
Jieyuan Liu↗Jianyang Gu↗Shijie Chen↗Jefferson Chen↗Zhen Wang↗

Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base. In pure-text long-context LLMs, retrieved-context use follows the U-shaped "lost-in-the-middle" effect of Liu et al. (2024): information at the start and end of context is used, the middle is lost. Whether this transfers to deployed multimodal KB-VQA is open. To close this gap, we design the first controlled probe of reader-side position dependence in multimodal KB-VQA: a gold-position protocol in which only the gold passage's prompt slot varies within question. We run it on three open-source 7B/8B VLM readers and two KB-VQA benchmarks at k up to 20. The shape flips from U to primacy: gold-at-first beats gold-at-last by 16 to 26 points on every reader-by-benchmark cell, an effect we call "Lost at the End". Three targeted ablations narrow the cause: a text-only control shows the multimodal setting amplifies an already-present text-mode primacy 2.2 to 4.5 times, and image-position and distractor-shuffle ablations together pin the locus to prompt slot 0 of the instruction-tuned reader. On a frozen reader, three retrieval-side fixes (MMR, oracle reranking, rank-based reordering) all leave the gap intact (no separable improvement). Our findings indicate that recall@k is the wrong metric for deployed KB-VQA and that closing the gap requires reader-side intervention; we release our protocol as a controlled instrument for evaluating such interventions.

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17.
arXiv (quant-ph) 2026-06-16

Grid-state deformation in a no-jump non-Hermitian bosonic dimer

作者:
B. M. Rodriguez-Lara↗H. Ghaemi-Dizicheh↗S. Dehdashti↗A. Hanke↗A. Touhami↗J. N\"otzel↗

arXiv:2606.17036v1 Announce Type: new Abstract: We study the no-jump evolution of ideal grid states in a lossy bosonic dimer with differential decay. The effective non-Hermitian quadratic dynamics induces a complex symplectic flow in phase space that deforms both the primitive lattice vectors and the origin seed. The average decay rate controls common attenuation, while coherent hopping and differential decay control the reduced dimer deformation. The reduced sector contains elliptic, parabolic, and hyperbolic regimes with imaginary spectra, an exceptional point, and real spectra, producing oscillatory, linear, and exponential lattice deformations. Although projected lattice areas can change, the deformation comes from a determinant-one complex symplectic flow on the full four-dimensional phase space. For a Gaussian regularization of the origin seed, we derive the associated complex width matrix and identify the positivity conditions that preserve Gaussian form. For an initial two-mode qunaught product state, the lossless limit recovers the standard beam-splitter generation of a square GKP$+$ Bell pair, while the no-jump dynamics produces its non-Hermitian deformation with a postselection cost set by the no-jump probability.

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18.
arXiv (quant-ph) 2026-06-12

Observation of Non-Gaussian Magnon Dynamics in a Two-Dimensional Long-Range XY Model

作者:
S. -A. Guo↗J. -Y. Tan↗J. Ye↗Y. Jiang↗L. Zhang↗Y. -X. Chen↗H. -J. Chen↗H. -Y. Hu↗W. -X. Guo↗B. -X. Qi↗L. He↗Z. -C. Zhou↗…

arXiv:2606.13499v1 Announce Type: new Abstract: Non-Gaussian evolution of high-order spin correlations characterizes important properties of quantum many-body systems. In practice, decoherence, statistical fluctuation and miscalibration of experimental parameters all hinder the witness of non-Gaussian dynamics. Here we demonstrate the crossover between Gaussian and non-Gaussian dynamics on a two-dimensional XY model with long-range and spatially structured interaction using a trapped ion quantum simulator. We prepare different initial densities of magnon excitations and verify the dynamics of single-spin observables for the engineered Hamiltonian. Then we compare the high-order spin correlations with the mean-field solution and the Holstein-Primakoff approximation, and demonstrate the non-Gaussian behavior in a way independent of the calibration errors. Our work provides a verifiable path from classically simulatable dynamics to regimes where quantum advantage may emerge.

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19.
medRxiv (Medicine) 2026-06-18

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity

作者:
Shah↗Green↗Wainmann↗Karrs↗

DNA methylation-based classification offers a rapid diagnostic complement to conventional molecular workflows in acute leukemia. Existing classifiers are trained on array-derived reference cohorts whose construction favors specimens with adequate tumor content, leaving clinically relevant low-purity specimens underrepresented and classifier robustness in this regime uncharacterized. On held-out low-purity specimens, existing classifiers were concordant with expert pathology in only 7 of 10 (MARLIN) and 5 of 10 (ALMA) cases, motivating a classifier built to maintain accuracy at low tumor purity. We developed MOSAIC (Methylation-Oriented Site Analysis and Information Classifier), a neural network classifier built to maintain accuracy across the full range of tumor purities encountered in clinical practice. MOSAIC is a neural network trained on publicly available array-based methylation data augmented with native methylation calls from Oxford Nanopore sequencing. MOSAIC was evaluated on low-purity specimens held out entirely from training. On these held-out low-blast leukemia specimens, all below 25% blasts and including a case at 1.4%, MOSAIC was concordant with expert pathology in every case, recovering the correct subtype where diluted disease signal would otherwise be mistaken for normal or unrelated tissue. Gradient-based saliency analysis showed that the network relies on a partially distinct set of discriminative CpG probes when classifying low-blast specimens. MOSAIC demonstrates that augmenting training with clinically representative clinical specimens yields methylation-based leukemia classification that maintains effectiveness under the variable tumor purity of real clinical cohorts.

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20.
medRxiv (Medicine) 2026-06-17

Performance of five risk stratification tools for paediatric pneumonia against WHO scores using data from the PediCAP trial in sub-Saharan Africa

作者:
Nalwanga↗Clements↗Musiime↗Mulenga↗Mujuru↗H. A↗Sidat↗Buck↗W. C↗Madhi↗Bielicki↗J. A↗…

Background Risk stratification tools for childhood pneumonia have been proposed to improve identification of children at highest risk of death, particularly in low-resource settings. However, their added value over the WHO Integrated Management of Childhood Illness (IMCI) criteria and danger signs remains uncertain. Methods We conducted a secondary analysis of a multi-country randomised controlled trial of children without HIV hospitalised with pneumonia in Mozambique, South Africa, Uganda, Zambia, and Zimbabwe. We evaluated the performance of five published risk scores alongside WHO IMCI severity classification and danger signs. Discrimination for (1) in-hospital mortality, (2) 28-day mortality, and (3) 28-day readmission or death was assessed using area under the receiver operating characteristic curve (AUC). Comparative performance and clinical utility were examined. Results Of the 1010 participants, 18 (1.8%) died in hospital, 22 (2.2%) died in hospital or in the 7 days post-discharge, and 63 (6.2%) died or were readmitted by day 28. Univariate case-fatality rates were highest for variables associated with malnutrition, convulsions, and hypoxaemia. All risk scores demonstrated moderate discrimination for in-hospital and in-hospital+7-day mortality (AUC range approximately 0.75-0.84), with no meaningful differences between models, and performed similarly to the WHO danger signs and IMCI severity classification. In contrast, all approaches performed poorly in predicting 28-day readmission or death (AUC approximately 0.54-0.58). No risk score consistently outperformed simple clinical criteria. Conclusions In this multi-country dataset, we found no evidence that published paediatric pneumonia risk scores meaningfully outperform WHO IMCI-based clinical assessment for predicting mortality. The relatively small number of mortality events limits precision, and modest differences cannot be excluded. These findings suggest that, in low-resource settings, strengthening implementation of existing WHO clinical criteria may be more effective than adopting more complex prediction tools.

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21.
arXiv (CS.CL) 2026-06-24

One Year Later...The Harms Persist, But So Do We!

作者:
Annika Marie Schoene↗Cansu Canca↗Gautham Vijay Kumar↗Anson Antony↗

General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions. This study evaluates six proprietary LLMs across 16 DSM-5 conditions using four adversarial attack variants, introducing an eight-dimension harm taxonomy and a multi-dimensional evaluation framework. Results show that safeguards hold reliably only for suicide and self-harm, while conditions such as eating disorders, substance use disorder, and major depressive disorder exhibit failure rates of up to 100%. We argue that ethical design and deployment of these LLMs demand clearly defined harm categories across clinical conditions and implementation of safeguards accordingly. Until such safeguards are in place, these models pose significant risks to vulnerable populations, making their growing integration into educational settings a particularly concerning.

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22.
arXiv (CS.CL) 2026-06-16

Understanding the Behaviors of Environment-aware Information Retrieval

作者:
Ruifeng Yuan↗Chaohao Yuan↗David Dai↗Yu Rong↗Hong Cheng↗Hou Pong Chan↗Chenghao Xiao↗

Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers require fundamentally different query formulation strategies for optimal performance. In this work, we present the first systematic analysis of how LLMs can learn to adapt their query formulation strategies for different retrievers via reinforcement learning (RL). Our empirical study reveals that RL effectively teaches an LLM to tailor its queries to specific retriever characteristics. We discover that different retrievers exhibit surprisingly distinct optimal query styles (e.g., descriptive vs. question-like), suggesting strategies learned for one retriever ineffective for another. We further show that performance can be enhanced by incorporating retriever-specific human guidance and by scaling model size. To facilitate learning over multi-retrieval-step trajectories, we introduce a branching-based rollout technique that improves training stability. Our work provides the first empirical evidence and actionable insights for building truly retriever-aware RAG systems. Code and resources are available at https://github.com/LCO-Embedding/Envs-aware-Information-Retrieval.

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23.
arXiv (CS.AI) 2026-06-18

Agentra: A Supervisable Multi-Agent Framework for Enterprise Intrusion Response

作者:
Raj Patel↗Shaswata Mitra↗Michele Guida↗Stefano Iannucci↗Sudip Mittal↗Shahram Rahimi↗

arXiv:2606.18325v1 Announce Type: cross Abstract: Enterprise intrusion response still depends on static playbooks and analyst-driven triage, creating delay between alert generation and containment. We present Agentra, a supervisable multi-agent Intrusion Response System (IRS) framework that converts alerts from IDS, EDR, and XDR platforms into structured incident response plans grounded in MITRE ATT&CK, MITRE D3FEND, and NIST CSF 2.0. Agentra decomposes response reasoning across role-scoped agents, validates proposed plans through a bounded Planner–Validator review loop, screens retrieved threat intelligence through a Moderator security gateway, gates actions through an Action Catalog and risk score, and records decisions in an append-only audit log. We evaluate Agentra against a static OASIS CACAO v2.0 cyber-playbook baseline on a 120-event corpus drawn from ThreatHunter-Playbook, Splunk BOTSv3, and DARPA OpTC. The strongest configuration improves FP-aware IRS F1 from 0.61 to 0.84 and restores the projected harmful-action rate to the static baseline level of 0.0% after Planner-only configurations introduce unsafe overreaction. These results indicate that multi-agent response planning can improve ontology-grounded IRS coverage while preserving analyst approval and auditability.

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24.
arXiv (quant-ph) 2026-06-12

Toward Entanglement Bootstrap for Conformal Field Theory in Any Dimension

作者:
Rolando Ramirez Camasca↗Xiang Li↗Ting-Chun Lin↗John McGreevy↗

arXiv:2606.12540v1 Announce Type: cross Abstract: Given a quantum critical wavefunction in any dimension, we propose a reconstructed Hamiltonian, analogous to the ones previously found for 1+1d CFT and for 2+1d bosonic liquid topologically-ordered states. We test numerically that, for known regularized approximate CFT groundstates (on the icosahedron and the fuzzy sphere), (1) they are close to the groundstate of their reconstructed Hamiltonian, and (2) the spectrum of their reconstructed Hamiltonian on the unit sphere has CFT properties (integer spacing of descendants) and matches known low-lying energies. We show that this provides an automated method to improve the finite-size effects in a fixed Hilbert space.

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25.
arXiv (CS.CV) 2026-06-24

VSANet: View-aware Sparse Attention Network for Light Field Image Denoising

作者:
Gargi Panda↗Soumitra Kundu↗Saumik Bhattacharya↗Aurobinda Routray↗

Light field (LF) image denoising is challenging due to the high-dimensional structure of LF data. While noise is independent across sub-aperture images, scene content exhibits strong cross-view correlations. We introduce VSANet, a view-aware sparse attention network for LF denoising. Specifically, we propose a view-aware sparse attention (VSA) block that represents the 4D LF feature map as a unified spatial-angular token space and performs cross-view aggregation via locality-sensitive hashing-based sparse attention. This enables global feature interactions with linear complexity, effectively exploiting LF correlations across views and spatial locations. In addition, we design a feature refinement (FR) block to emphasize informative features in spatial, angular, and epipolar subspaces. The VSA and FR blocks are integrated within a sequential attention refinement module, forming the core of VSANet. Experiments demonstrate VSANet outperforms stateof-the-art LF denoising methods.

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