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01.
arXiv (math.PR) 2026-06-11

Construction of ergodic IDLA forests in $\mathbb{Z}^d$

arXiv:2506.10476v2 Announce Type: replace Abstract: We prove the existence of infinite-volume IDLA forests in $\mathbb{Z}^d$ , with $d \geq 2$, based on a multi-source IDLA protocol. Unlike IDLA aggregates, the laws of the IDLA forests studied here depend on the trajectories of particles, and then do not satisfy the famous Abelian property. Their existence is due to a stabilization result (Theorem 1.1, our main result) that we establish using percolation tools. Although the sources are infinitely many, we also prove that each of them play the same role in the building procedure, which results in an ergodicity property for the IDLA forests (Theorem 1.2).

02.
arXiv (CS.AI) 2026-06-19

Sovereign Execution Brokers: Enforcing Certificate-Bound Authority in Agentic Control Planes

arXiv:2606.20520v1 Announce Type: cross Abstract: Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not reside inside non-deterministic reasoning processes. Existing access-control mechanisms authorize identities, while assurance layers certify proposed actions; neither alone provides a mandatory enforcement point for certified authority at the moment of mutation. This paper introduces the Sovereign Execution Broker (SEB), a runtime enforcement boundary for certificate-bound agentic infrastructure. SEB consumes certificates issued by the Sovereign Assurance Boundary (SAB), verifies that the requested mutation matches the certified execution contract, checks validity windows, policy epochs, revocation epochs, and live-state drift, mints scoped execution identity, invokes infrastructure APIs, and records signed decision and outcome records. By separating proposal, admission, and execution, SEB turns certified authority into a short-lived, revocable, auditable runtime capability, provided that production mutation APIs reject non-broker identities. We present the SEB execution model, certificate and replay-verification predicates, scoped identity semantics, bypass-prevention deployment patterns, failure behavior, and a concrete prototype implementation. We evaluate the prototype on AWS and Kubernetes clusters, measuring latency overheads, revocation propagation, drift detection, and security under fault injection.

03.
arXiv (CS.AI) 2026-06-16

When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting

arXiv:2606.16465v1 Announce Type: new Abstract: AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequential damages, users are left with uncompensated losses, and default human review limits the efficiency gains of automation. We ask when autonomous AI deployment can become economically acceptable despite failure risk. Our answer is to quantify risk at the customer-task-trace episode level and transfer it through insurance. Automation is acceptable when its expected benefit exceeds the premium, control cost, and remaining risk. This requires a defined role with bounded permissions and comparable traces. We introduce trace-economic underwriting, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer. It uses deterministic economic labels rather than an LLM judge. In our trace-to-loss testbed, trace-economic pricing reduces pricing MAE from $17.7K to $569 and removes regressive cross-subsidy. A 300-trace expert audit accepts 295 labels unchanged. On 1,000 real SWE-smith traces, trace-conditioned controls reduce CVaR95 by 72%. Theorem~1 gives a finite-sample scope condition. We release code, labels, and audit sheets.

04.
arXiv (math.PR) 2026-06-25

Hoeffding-Style Concentration Bounds for Exchangeable Random Variables

arXiv:2603.10190v2 Announce Type: replace-cross Abstract: We establish Hoeffding-type concentration inequalities for the lower and upper tails of finite sums of exchangeable random variable sequences. In contrast to the existing literature, our concentration bounds are expressed in terms of the largest and smallest means among the distributions in the support of the de Finetti mixing measure, rather than the population mean. Specifically, the upper-tail bound is centered at the largest such mean, while the lower-tail bound is centered at the smallest. These results bridge the gap between finite-sample and population means of exchangeable random variables, and the means of the underlying distributions in the de Finetti representation.

05.
arXiv (CS.AI) 2026-06-17

Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval

arXiv:2512.04524v4 Announce Type: replace-cross Abstract: Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-Based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.

06.
arXiv (math.PR) 2026-06-15

Trivariate Hypergeometric Series Formulas for Pure Partition Functions of Multiple $3$-SLE$_\kappa$

Authors:

arXiv:2606.14038v1 Announce Type: new Abstract: Pure partition functions of multiple SLE are characterized by null-state partial differential equations, Möbius covariance, and boundary asymptotics. After quotienting by Möbius covariance, the case of three curves is the first genuinely multivariable one: the moduli space has three independent variables, naturally represented by the three unoriented cross-ratios of the three pairs of links. We solve this Möbius-normalized three-variable problem for the two basic link-pattern types of multiple \(3\)-SLE\(_\kappa\), namely the rainbow and neighbor patterns. Writing \(\beta=4/\kappa\), we construct explicit trivariate hypergeometric-series normal forms and identify them with the corresponding pure partition functions for all \(\beta>1/2\) in the rainbow case and all \(\beta\ge2/3\) in the neighbor case. Equivalently, these ranges are \(\kappa\in(0,8)\) and \(\kappa\in(0,6]\), respectively. The proof is analytic. The null-state PDEs and Möbius covariance yield recursion relations for the trivariate coefficient arrays. In the rainbow case, coefficient estimates give convergence and boundary regularity on the closed cube. In the neighbor case, Pfaff systems continue the local power series to a neighborhood of \([0,1)^3\), while side-face equations, regular normal estimates, and corner propagation give continuity on \([0,1]^3\) for \(\beta\ge2/3\). The endpoint \(\beta=2/3\), corresponding to \(\kappa=6\), requires a logarithmic normal term. The two-dimensional boundary degenerations are classical Appell \(F_1\) and Horn \(G_2\) functions. The probabilistic identification uses SLE martingale arguments and Itô calculus, together with positivity and boundary regularity. We also discuss boundary degenerations, including heuristic connections with boundary Green's functions.

07.
arXiv (quant-ph) 2026-06-15

Dealing with locality in QAOA

arXiv:2606.14447v1 Announce Type: new Abstract: Shallow-depth QAOA on sparse, high-diameter MaxCut instances faces a locality bottleneck: at depth \(p\), local observables can depend only on a bounded neighborhood of the circuit interaction graph. We propose a transport-augmented QAOA that keeps the MaxCut cost Hamiltonian unchanged but enriches the mixer with optimized, unweighted shortcut couplings (scheduled \(XX+YY\)) to collapse the effective interaction-graph diameter. Using exact finite-depth support recursions, we relate optimal shortcut placement to bounded-diameter graph augmentation, and show in benchmarks that (unlike ma-QAOA) performance becomes effectively size-invariant once the diameter is reduced. For bipartite families (base diameter 4), reducing the interaction path to \(d=1\) raises the ensemble-averaged approximation ratio from 0.7378 (ma-QAOA) to 0.9767 at \(p=1\) (\(\sigma=0.0251\), nine system sizes); on random trees (base diameter 10), at \(p=2\) it improves from 0.9226 to 0.9997 (\(\sigma=0.0001\)).

08.
arXiv (CS.CL) 2026-06-17

Translating the Untranslatable: An Operationalizable Ontology for Untranslatability

Untranslatability, cases where meaning cannot be directly preserved across languages, is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems improve on standard benchmarks, their limitations increasingly concentrate in such cases, where translation cannot be reduced to one-to-one equivalence. We introduce a structured ontology of untranslatability along with a taxonomy of compensation strategies, which are specific techniques to convey meaning under these untranslatable circumstances. We operationalize this framework into a multilingual dataset of untranslatable sentences paired with strategy-based translations, enabling controlled analysis of translation behavior. Initial human preference studies suggest that translation quality depends on the strategy used, with consistent preferences for outputs that include explanatory context, known as the Annotation compensation strategy. Our framework and dataset provide a foundation for studying and modeling strategy-informed machine translation.

09.
arXiv (CS.AI) 2026-06-24

AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design

arXiv:2606.09831v2 Announce Type: replace-cross Abstract: As classroom cohorts expand, team teaching is increasingly used to integrate the expertise and pedagogical perspectives of multiple teachers. Yet, there is limited empirical understanding of how team teaching unfolds in practice, particularly regarding differences in teachers' contributions across experience levels, student cohorts, and learning task design. Prior research on team teaching has largely relied on retrospective self-reports or small-scale observations, offering limited insight into the micro-level processes through which team teaching is enacted. Teacher talk offers a scalable lens on these processes. While research in individual teaching contexts shows that acoustic features of speech (e.g., voice quality, intonation, and loudness) can shape student learning, evidence from team-teaching settings remains scarce. Moreover, capturing such features through manual observation or transcription is especially challenging in team-teaching classrooms, where multiple teachers speak across extended sessions and spatial locations, limiting scalability without automation. Grounded in spatial pedagogy theory and team-teaching research, this paper presents an AI-based speech processing approach to analyse classroom talk in team-teaching settings. We analysed 36 recorded undergraduate and postgraduate sessions involving 12 teachers. Spatial pedagogy behaviours were coded and acoustic features extracted to examine variation across teachers' experience, student cohorts, and the learning task design. The results reveal systematic differences, most notably in loudness dynamics: high-experience teachers, undergraduate classes and collaborative learning tasks exhibited greater loudness variation, suggesting more frequent modulation of volume to foreground key information and support classroom interaction and engagement.

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

Cross-Model Disagreement as a Label-Free Correctness Signal

arXiv:2603.25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deployment. Existing approaches rely on a model's own uncertainty – such as token entropy or confidence scores – but these signals fail critically on the most dangerous failure mode: confident errors, where a model is wrong but certain. In this work we introduce cross-model disagreement as a correctness indicator – a simple, training-free signal that can be dropped into existing production systems, routing pipelines, and deployment monitoring infrastructure without modification. Given a model's generated answer, cross-model disagreement computes how surprised or uncertain a second verifier model is when reading that answer via a single forward pass. No generation from the verifying model is required, and no correctness labels are needed. We instantiate this principle as Cross-Model Perplexity (CMP), which measures the verifying model's surprise at the generating model's answer tokens, and Cross-Model Entropy (CME), which measures the verifying model's uncertainty at those positions. Both CMP and CME outperform within-model uncertainty baselines across benchmarks spanning reasoning, retrieval, and mathematical problem solving (MMLU, TriviaQA, and GSM8K). On MMLU, CMP achieves a mean AUROC of 0.75 against a within-model entropy baseline of 0.59. These results establish cross-model disagreement as a practical, training-free approach to label-free correctness estimation, with direct applications in deployment monitoring, model routing, selective prediction, data filtering, and scalable oversight of production language model systems.

11.
medRxiv (Medicine) 2026-06-23

Social networks and their association with quality of life among older adults in rural Burkina Faso

Objective: This study aimed to identify the types of social networks present among older adults in a rural, low-income country setting and describe their association with quality of life (QoL). Methods: A population-representative, cross-sectional survey was conducted in 60 villages around Nouna in Burkina Faso from July to August 2021. Data were collected from resident adults aged 40 years and older. Variables captured were sociodemographic status; social network characteristics (using the Practitioner Assessment of Network Typology (PANT)); quality of life (using the EuroHIS-8 tool); presence of non-communicable diseases, mental health conditions, and disability. Additionally, social networks were broadly categorised as aggregated integrated and aggregated less-integrated groups. Social network types and the groups were described separately, and a multivariable linear regression model was used to understand the association between social network types and QoL, adjusted for sociodemographic and morbidity factors. Results: Among the 2390 respondents, median age was 55 yrs (IQR: 47-64 yrs) and 55.8% were female. Locally Integrated (35.4%) or Family Dependent (30.3%) were the most common PANT social network types, followed by a mixed group (having characteristics of two or more social network types) (30.5%). Private Restricted (2.1%), Locally Self-Contained (1.2%), and Wider Community-Focussed (0.4%) types were uncommon. Adults with aggregated integrated network groups (36.1%) and aggregated less-integrated group (36.0%) were near equal, while others were non-aggregable. Although Wider Community-Focused type showed a significantly better QoL ({beta}= 8.69, 95%CI: 4.10 to 13.27), the association between social networks and QoL were subdued when controlled for morbidity factors, and hence no significant associations were observed between other types or the aggregated groups. Conclusion: Although having integrated social networks lead to a better QoL, morbidity has a greater effect on the QoL among older adults in Nouna and hence, investing more on improving the physical and mental health needs appears more beneficial.

12.
arXiv (CS.AI) 2026-06-18

A Technical Taxonomy of LLM Agent Communication Protocols

arXiv:2606.19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks. Nonetheless, the fragmented protocol landscape presents a significant interoperability challenge. This study develops a technical taxonomy to classify and analyze LLM agent communication protocols. Following an established iterative method, we defined the taxonomy's purpose, meta-characteristic, and ending conditions, then performed five iterations, three empirical-to-conceptual and two conceptual-to-empirical, on nine actively maintained open-source protocols with demonstrable adoption. The taxonomy comprises five dimensions: counterparty, payload, interaction state, discovery mechanism, and schema flexibility. Classification reveals recurring architectural patterns: all sampled agent-to-agent protocols combine hybrid payloads with session-state persistence; most protocols support multiple predefined schemas, and two negotiate schemas at runtime, indicating a trend toward schema flexibility; decentralized discovery remains rare. Analysis suggests short-term convergence pressure toward protocols unifying agent-to-agent and agent-to-context (tool and data) communication. Long-term, however, no single protocol is likely to maximize versatility, efficiency, and portability simultaneously. The field will more likely evolve toward a federated, layered protocol stack. The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.}

13.
arXiv (CS.CL) 2026-06-12

EDEN: A Large-Scale Corpus of Clinical Notes for Italian

We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals. The corpus, in its current version, is composed of approximately 4 million clinical notes fully anonymized, covering diverse phases of patient care during the stay in the emergency department. In addition, a subset of about six thousand notes has been manually annotated by clinical experts through a structured Case Report Form (CRF) containing 132 items relevant for two patient situations in emergency departments, dyspnea and loss of consciousness. Items may assume numerical values (e.g., for blood saturation), categorical (e.g., for level of consciousness ), binary (e.g., for presence of traumas), and mixed value types. The annotation process involved multiple clinicians and underwent iterative revision to resolve ambiguities in item formulation, resulting in a richly structured (although high imbalanced) resource. The dataset aims to fill a relevant gap of data able to support both the development and the use of Large Language Models in concrete medical applications. We describe the data collection protocol, the on-site anonymisation pipeline, corpus statistics, and the annotation scheme. Finally, we propose CRF-filling as a novel structured information extraction benchmark, and provide zero-shot baseline resulting from Gemma-27B and MedGemma-27B. To the best of our knowledge, the EDEN dataset is the largest freely available corpus of clinical notes existing for the Italian language.

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

Tac-DINO: Learning Vision-Tactile Features with Patch Alignment

Touch is the primary medium through which humans interact with the environment. Currently, tactile learning mainly focuses on image-level pretraining or alignment. However, tactile signals correspond to local object contact, while research into scale alignment and holographic matching remains limited and proper datasets and benchmarks also lack. To bridge this gap, we first construct a data collection system to acquire a large-scale tactile dataset, with over 20 K tactile contacts from 505 real-world objects. Building on this dataset, we design a Vis-Tac Holographic Matching Benchmark to evaluate vision-tactile local-to-global alignment ability. Then we propose Vision-Tactile Patch Alignment (VTPA) methods for vision-tactile representation learning. Experiments demonstrate that these exceed the performance of methods without alignment and align with whole-object images.

15.
arXiv (CS.AI) 2026-06-17

Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection

arXiv:2606.17109v1 Announce Type: cross Abstract: Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs). However, most existing GNN-based NIDS approaches focus on the relational structure of traffic flows, and treat them as temporally independent, which limits their ability to cope with evolving attack behaviors. Moreover, their reliance on supervised or semi-supervised learning often restricts generalization to unseen attacks. To address these limitations, we propose a novel self-supervised GNN-based framework. To the best of our knowledge, the proposed model is among the first self-supervised GNN-based NIDS models to explicitly leverage real timestamps, which provides faithful temporal dependencies for representation learning. We first construct a series of temporal graphs from network traffic flows according to their timestamps, and then employ an E-GraphSAGE and LSTM based encoder to fully extract temporal information and spatial dependencies of network traffic, without introducing time-costly attention mechanisms. A multi-view graph contrastive learning (GCL) scheme is introduced, where temporal, spatial, and feature contrasts are jointly performed to capture temporal continuity, preserve structural consistency, and improve the generalization and robustness of the learned representations, respectively. In addition, a gradient-norm-based adaptive weighting strategy is designed to optimize the contrastive loss weights. Experimental results on four representative NIDS datasets with real timestamps demonstrate that our method significantly outperforms existing self-supervised approaches and achieves performance comparable to the supervised state-of-the-art GNN method, while maintaining high computational efficiency.

16.
arXiv (CS.LG) 2026-06-25

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

arXiv:2512.07420v3 Announce Type: replace-cross Abstract: Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interaction Graph Network (KIGNet), a graph neural network that integrates kinematic variables into jet classification by constructing four graph representations per jet, each weighted by a distinct variable: angular separation ($\Delta$), relative transverse momentum ($k_T$), momentum fraction ($z$), and invariant mass squared ($m^2$). Three of these ($\Delta$, $k_T$, $z$) are motivated by the Lund jet plane, grounded in perturbative QCD factorization; the fourth ($m^2$) adds complementary mass-scale sensitivity for heavy-flavor identification. Using Gradient-weighted Class Activation Mapping (Grad-CAM), we determine which variables dominate classification. Angular separation and relative transverse momentum account for about 76% of the total Grad-CAM attribution (40.72% and 35.67%), with momentum fraction and invariant mass contributing the remaining 24%. This hierarchy is consistent with the soft-collinear structure of QCD radiation in the training data, showing that the network learns physically interpretable representations rather than spurious correlations. On the JetClass dataset, KIGNet achieves a macro-accuracy of 95.07%, macro-AUC of 96.61%, and macro-AUPR of 81.52%, relative improvements of 2.45%, 3.40%, and 19.11% over the state-of-the-art baseline. On the Aspen Open Jets dataset of real CMS collision data, KIGNet produces substantially more structured latent representations than the baseline, reducing the Davies-Bouldin Index by 52.15% ($0.8395 \rightarrow 0.4017$) and increasing the Dunn Index by 42.33% ($0.0189 \rightarrow 0.0269$), confirming that physics-informed kinematic encoding generalizes beyond idealized simulation to experimental detector conditions.

17.
arXiv (CS.LG) 2026-06-16

Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation

arXiv:2606.14945v1 Announce Type: new Abstract: The autoresearch pattern enables autonomous experimentation by having a large language model (LLM) iteratively modify code to optimize a target metric. Its stateless design, however, reconstructs experimental context from scratch at every iteration, incurring $O(n)$ token cost per iteration and $O(n^{2})$ total. This work reformulates the pattern as a stateful ReAct agent using LangGraph, where typed persistent state carries experimental history across iterations via a tool-calling interface. Two benchmarks are evaluated: hyperparameter tuning (15 iterations, small per-iteration observations) and code performance optimization (40 iterations, large per-iteration observations containing full source code and benchmark results). On hyperparameter tuning, the stateful agent consumes 90\% fewer tokens (2{,}492 vs.\ 24{,}465). On code optimization, the stateful agent consumes 52\% fewer tokens (627K vs.\ 1{,}275K) while achieving comparable optimization quality on both tasks. The token reduction is structural: the stateless agent re-reads the full history at $O(n)$ cost per iteration, while the stateful agent operates within a fixed-size conversation window at $O(1)$ cost. This paper describes the architecture in sufficient detail for practitioners to implement a stateful autoresearch agent for their own workflows.

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

When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models

Failures in multi-turn reasoning models are largely invisible to terminal-score evaluation. A model can lock onto an unsafe stance early in a long dialogue, yet its final-turn refusal rate may appear indistinguishable from a robustly aligned baseline. To expose these hidden temporal dynamics, we propose a trace-level diagnostic - the CoT-Output 2x2 safety matrix. This framework labels every turn along two independent axes (internal reasoning and visible output), yielding four operationally defined failure cells: robust alignment, alignment faking, overt jailbreak, and a distinct failure mode we term context-injection failure (where the CoT maintains safe reasoning, but the visible output produces harm, highlighting a multi-turn manifestation of reasoning unfaithfulness). We evaluate three distilled reasoning targets against a fixed attacker across five oversight conditions, collecting 6750 turn-level observations on the Information-Hazard scenario. Our analysis reveals two reproducible vulnerabilities: an oversight paradox where explicit monitoring cues paradoxically increase alignment-faking rates rather than suppress them, and a context-injection failure where models lock onto unsafe external outputs despite safe internal states. We release the full dataset of multi-turn dialogues and CoT traces to support follow-up trace-diagnostic research.

19.
medRxiv (Medicine) 2026-06-18

Intra-arterial recombinant human TNK tissue-type plasminogen activator (rhTNK-tPA) thrombolysis for acute medium vessel occlusion (MeVO-TNK): Study rationale and design

Background The optimal management of acute ischemic stroke caused by medium vessel occlusion (MeVO) remains uncertain. Recent randomized trials have failed to demonstrate a clear benefit of endovascular therapy in this population, whereas intra-arterial thrombolysis (IAT) has emerged as a biologically plausible alternative. However, prospective evidence supporting IAT in MeVO is lacking, and the optimal dosing strategy for stand-alone IAT remains undefined. Aim To preliminarily evaluate the efficacy and safety of intra-arterial tenecteplase (IA-TNK) plus standard medical therapy (SMT) compared with SMT alone in patients with acute MeVO stroke, and to explore a stepwise IA-TNK dosing strategy. Design The MeVO-TNK trial is a multicenter, prospective, randomized, open-label, blinded-endpoint (PROBE), exploratory phase II study. A total of 60 participants with imaging-confirmed MeVO will be randomized 1:1 to receive either IA-TNK plus SMT or SMT alone. Participants presenting beyond 6 hours from symptom onset must demonstrate salvageable penumbral tissue on advanced imaging. Those assigned to the intervention group will receive up to two intra-arterial boluses of tenecteplase (0.0625 mg/kg per bolus), with the second bolus administered based on angiographic assessment of reperfusion and safety. Outcomes The primary efficacy outcome is final infarct volume measured at 72{+/-}24 hours after randomization. Secondary efficacy outcomes include the proportions of patients achieving modified Rankin Scale (mRS) scores of 0-1, 0-2 and 0-3 at 90 days, a shift analysis of the mRS distribution at 90 days, early neurological deterioration, and National Institutes of Health Stroke Scale score at 7 days or discharge. The primary safety outcome is symptomatic intracranial hemorrhage within 24 hours. Conclusions This trial will provide preliminary evidence on the biological efficacy, reperfusion potential and safety of stand-alone IA-TNK for acute MeVO stroke, helping to address an important evidence gap and inform the design of future confirmatory studies.

20.
arXiv (CS.LG) 2026-06-16

Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment

arXiv:2407.05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data. In such scenarios, the generated pseudo-labels tend to exhibit a bias toward the majority class, and models relying on these pseudo-labels can further amplify this bias. Existing imbalanced SSL algorithms explore pseudo-labeling strategies based on either pseudo-label refinement (PLR) or threshold adjustment (THA), aiming to mitigate the bias through heuristic-driven designs. However, through a careful statistical analysis, we find that existing strategies are suboptimal: most PLR algorithms are either overly empirical or rely on the unrealistic assumption that models remain well-calibrated throughout training, while most THA algorithms depend on flawed metrics for pseudo-label selection. To address these shortcomings, we first derive the theoretically optimal form of pseudo-labels under class imbalance. This foundation leads to our key contribution: SEmi-supervised learning with pseudo-label optimization based on VALidation data (SEVAL), a unified framework that learns both PLR and THA parameters from a class-balanced subset of training data. By jointly optimizing these components, SEVAL adapts to specific task requirements while ensuring per-class pseudo-label reliability. Our experiments demonstrate that SEVAL outperforms state-of-the-art SSL methods, producing more accurate and effective pseudo-labels across various imbalanced SSL scenarios while remaining compatible with diverse SSL algorithms. The code is publicly available (https://github.com/ZerojumpLine/SEVAL).

21.
arXiv (CS.CL) 2026-06-17

E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems

Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features from EventStream, and utilized them as inputs for downstream tasks such as grade prediction and modeling of student behavior. Previous research evaluated models that mainly used statistical-based features derived from EventStream logs, such as the number of operation types or access frequencies. While these features are useful for providing certain insights, they lack temporal information that captures fine-grained differences in learning behaviors among different students. This study proposes E2Vec, a novel feature representation method based on word embeddings. The proposed method regards operation logs and their time intervals for each student as a string sequence of characters and generates a student vector of learning activity features that incorporates time information. We applied fastText to generate an embedding vector for each of 305 students in a dataset from two years of computer science courses. Then, we investigated the effectiveness of E2Vec in an at-risk detection task, demonstrating potential for generalizability and performance.

22.
arXiv (CS.CL) 2026-06-12

MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection

Detecting mental health disorders from Arabic social media text remains challenging due to dialectal variation, informal language, limited high-quality annotated resources, and severe class imbalance. While English mental health natural language processing (NLP) has progressed substantially, Arabic multi-class disorder classification remains insufficiently studied. This study proposes a two-phase framework for Arabic mental health text classification. In phase 1, three Arabic pre-trained language models, AraBERT, CAMeLBERT, and MARBERT, undergo Domain-Adaptive and Task-Adaptive Pretraining (DAPT and TAPT) using a large-scale corpus of unlabeled Arabic mental health tweets. The adapted models are evaluated under a unified protocol to identify the most effective backbone model. In phase 2, the selected model is assessed across four configurations combining single-stage and hierarchical two-stage classification architectures with full fine-tuning and Low-Rank Adaptation (LoRA). To support this study, we constructed a novel annotated Arabic mental health dataset comprising 50,670 tweets across six categories, with strong inter annotator agreement (Krippendorff's Alpha = 0.733, average pairwise agreement = 0.797). Experimental results show that the domain-adapted MARBERT (MentalMARBERT) achieves statistically significant improvements over baseline models in both accuracy and macro-F1. The hierarchical two-stage architecture combined with full fine-tuning achieves the best overall performance, reaching a macro-F1 of 0.861 and an accuracy of 0.877. These findings demonstrate the effectiveness of domain-specific adaptive pretraining and hierarchical classification for Arabic mental health disorder detection.

23.
arXiv (math.PR) 2026-06-17

Optimal Impulse Control for Cyber Risk Management

arXiv:2410.17706v2 Announce Type: replace-cross Abstract: We explore an optimal impulse control problem wherein an electronic device owner strategically calibrates protection levels against cyber attacks. Utilizing epidemiological compartment models, we qualitatively characterize the dynamics of cyber attacks within the network. We determine the optimal protective measures against effective hacking by formulating and solving a stochastic control problem with optimal switching. We demonstrate that the value function for the cluster owner constitutes a viscosity solution to a system of coupled variational inequalities associated with a fully coupled reflected backward stochastic differential equation (BSDE). Furthermore, we devise a comprehensive algorithm alongside a verification procedure to ascertain the optimal timing for network protection across various cyber attack scenarios. Our findings are illustrated through numerical approximations employing deep Galerkin methods for partial differential equations (PDEs). We visualize the optimal protection strategies in the context of two distinct attack scenarios: (1) a constant cyber attack, (2) an exogenous cyber attack strategy modeled with a Poisson process.

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

An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production

Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production chain: brain signals, muscle activations, and articulatory movements. This multimodal acquisition paradigm presents substantial technical challenges, including MRI-induced electromagnetic interference and myogenic artifacts. To mitigate these, we introduce an artifact suppression pipeline tailored to this tri-modal setting. Once fully developed, this framework is poised to offer an unprecedented window into speech neuroscience and insights leading to brain-computer interface advances. The source code and data are available.

25.
Nature Medicine 2026-06-11

Clinical Profile and Genomic Characterization of the 2026 Bundibugyo Virus Index Case in Uganda

Bundibugyo virus disease (BVD) remains a high-consequence threat in Eastern and Central Africa, where cross-border mobility, nonspecific early symptoms, and delayed recognition can obscure transmission. In this case report, we describe Uganda’s 2026 BVD index case: a male patient who traveled from the Democratic Republic of the Congo to Uganda and was admitted to a private hospital in Kampala on 11 May 2026 after more than two weeks of vomiting and diarrhea, with epigastric pain, weakness, and hiccups. He deteriorated rapidly, developing acute kidney injury, pulmonary edema, hepatic dysfunction, hypoxemia, delirium, atrial flutter, possible disseminated intravascular coagulation, and multiorgan failure, and died on 14 May. A posthumous EDTA whole-blood specimen tested at the Central Emergency Response and Surveillance Laboratory was positive for orthoebolavirus RNA and confirmed as Bundibugyo virus (BDBV) by RT-qPCR. Sequencing achieved 99% genome coverage at ≥100× depth. The 2026 BDBV genome formed a distinct lineage approximately equidistant from the 2007–2008 Butalya and 2012 Isiro variants, differing by 216–227 nucleotides (~1.2% sequence divergence). Here, we demonstrate the value of fatality surveillance, private-sector surveillance, diagnostic optimization through national specimen referral, and rapid molecular-genomic diagnostics for early detection, transmission chain interruption, and public health response coordination.