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

Artificial Intelligence in Ship Finance: Applications, Opportunities, and a Case Study in AI-Augmented Loan Origination

作者:
Lasse Dierich↗Orestis Schinas↗

arXiv:2606.11238v1 Announce Type: cross Abstract: Ship finance is a data-intensive and document-heavy segment of asset-based lending, requiring the integration of financial, technical, contractual, and regulatory information from heterogeneous and largely unstructured sources. Increasing environmental regulation and ESG reporting requirements are adding further complexity to underwriting and loan-origination processes. Recent advances in artificial intelligence (AI), particularly large language models (LLMs), create new opportunities for processing and analysing such information. This paper reviews potential applications of AI in ship finance, with a particular focus on LLM-based systems for document comprehension, information extraction, and workflow automation. We present ShipFinance.ai, a modular agentic architecture to support loan application workflows in ship finance. The proposed system combines an LLM-based extraction module, financial analysis components, external maritime data services, and a controlled document-generation module with a chatbot interface to support the preparation of standardized financing applications. The paper discusses the key challenges for using such models in production. We argue that AI-assisted systems can support maritime finance professionals in managing increasingly complex information and reporting requirements.

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02.
medRxiv (Medicine) 2026-06-15

Active commuting, anxiety symptoms and mental wellbeing: a dose-response study

作者:
Joensuu↗Jussila↗J. J↗Lanki↗Tiittanen↗Pasanen↗T. P↗Ekelund↗Halonen↗J. I↗

Climate change draws attention to the planetary health perspective in sport and exercise sciences, that is, to physical activity that supports both human wellbeing and environmental sustainability. Active commuting is a sustainable form of physical activity with well-established somatic health benefits. However, more knowledge is needed on its relationship with mental health. We examined dose-response associations between active commuting, anxiety symptoms, and mental wellbeing among Finnish adults, and whether green commuting environment moderates these relationships. We used data from the cross-sectional Environment and Health Survey collected in June-September 2023 in the ten largest cities in Finland. Employed participants with data on anxiety symptoms (Generalized Anxiety Disorder-7, GAD-7), mental wellbeing (World Health Organization-Five Well-Being Index, WHO-5), commuting profile over a year (mode, frequency, distance, and perceived greenness along the commute route), and sociodemographic and lifestyle factors were included (n=1,672; mean age 45.3 years; 53.8% women). Active commuting was defined as travelling the entire commute by walking or cycling (including e-biking) that was converted into approximated annual km/week and MET-h/week. We used linear and logistic regression with restricted cubic splines to evaluate dose-response associations, adjusted for key covariates. The role of perceived greenness was tested using an active commuting x commute greenness interaction term. We found no dose-response relationships between active commuting and anxiety symptoms or mental wellbeing in any of the models. No effect modification by commute greenness was observed. More research on how active commuting may support planetary health from a mental health perspective is needed.

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03.
arXiv (CS.CL) 2026-06-12

SupraBench: A Benchmark for Supramolecular Chemistry

作者:
Tianyi Ma↗Yijun Ma↗Zehong Wang↗Weixiang Sun↗Ziming Li↗Connor R. Schmidt↗Chuxu Zhang↗Matthew J. Webber↗Yanfang Ye↗

Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications. However, designing host-guest systems remains time-consuming, requiring days of dry-lab verification per candidate pair. Although LLMs have emerged as a fast alternative with strong performance on molecular binding tasks, no benchmark currently systematically evaluates LLMs for host-guest reasoning across fundamental supramolecular chemistry tasks, e.g., binding affinity prediction. To this end, we collaborate with domain experts to release the first Supramolecular Benchmark, called SupraBench, to evaluate LLMs in chemistry reasoning. Specifically, we design four fundamental tasks, i.e., binding affinity prediction, top-binder selection, solvent identification, and host-guest description, plus an auxiliary vision-based task for molecular identification. We also release SupraPMC, a curated 16M-token corpus of Supramolecular chemistry articles distilled from Europe PMC, to support the adaptation to the supramolecular domain. We benchmark a broad range of open and proprietary LLMs and find that LLMs leave substantial headroom across all tasks. Domain adaptation pretraining over SupraPMC transfers cleanly to in-distribution regression but trades off against strict letter-format output. Moreover, the difficulty profile differs sharply across task families, revealing distinct failure modes that indicate specific gaps in current supramolecular chemistry reasoning. Our source codes and benchmark datasets are available at https://github.com/Tianyi-Billy-Ma/SupraBench.

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

Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning

作者:
Qinwu Xu↗

Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning. Reconstruction-based approaches such as MAE and VideoMAE learn representations by recovering masked visual content [he2022mae,tong2022videomae], while contrastive methods such as CLIP learn semantically meaningful embedding spaces through representation alignment [radford2021clip]. In this work, we introduce a Momentum-Guided Semantic Forecasting framework (MoFore) for self-supervised video representation learning. Instead of optimizing for pixel-level reconstruction or task-specific semantic alignment, the proposed method learns temporally predictive video representations by forecasting future latent embeddings from temporally distant context clips. To improve robustness across temporal scales, we further introduce randomized temporal-gap forecasting during training. The framework combines predictive latent forecasting with contrastive regularization to encourage temporal consistency while preventing representation collapse. Experiments on the UCF101 dataset demonstrate that the proposed framework learns temporally consistent and semantically meaningful video representations without using action labels during training. Quantitative analysis shows strong temporal stability and emergent category-level structure in the learned embedding space, while qualitative retrieval experiments reveal motion-aware organization across related activities. Overall, the results suggest that long-range latent forecasting provides an effective and computationally efficient approach for self-supervised video representation learning without relying on reconstruction-based objectives.

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

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

作者:
Kareem Amin↗Rudrajit Das↗Alessandro Epasto↗Adel Javanmard↗Dennis Kraft↗M\'onica Ribero↗Sergei Vassilvitskii↗

arXiv:2606.16952v1 Announce Type: cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets. However, generating high-utility synthetic data often carries the risk of memorizing and regurgitating private information from the training corpus. In this work, we present a customizable empirical auditing framework designed to detect and explain such data disclosures. Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures''-where the system incidentally generates a user's data. By partitioning input data into training and holdout sets and applying rigorous statistical hypothesis testing, we determine if observed disclosures are consistent with strict privacy baselines, such as zero-learning or specific Differential Privacy (DP) bounds. Crucially, this approach requires no model access, no canary insertion, and no reference model training -only the synthetic output and a held-out control set. We demonstrate that this framework effectively functions as a membership inference attack, providing empirical lower bounds on privacy leakage that are tighter than prior data-based auditing methods. Our approach is model-agnostic, applies to any synthetic data generation mechanism, and requires orders of magnitude fewer computational resources than shadow-model or canary-based alternatives.

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

RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets

作者:
Jan Cegin↗Branislav Pecher↗Ivan Srba↗Jakub Simko↗

LLMs are powerful generators of synthetic data, which are used for training smaller, specific models. This is especially valuable for low-resource languages, where human-labelled data is scarce but LLMs can still produce high-quality text. However, LLMs differ in how useful their outputs are for training. Selecting the best LLM as a generator is challenging because extrinsic evaluation requires costly human annotations (which are often unavailable for low-resource languages), while intrinsic metrics correlate poorly with downstream performance. We introduce Round robin Synthetic data Evaluation (RoSE), a proxy metric for selecting the best LLM generator without human test sets. RoSE trains a small model on the outputs of a candidate generator (LLM) and then evaluates it on generated synthetic examples from all other candidate LLMs. The final RoSE score is the mean performance of this small model. Across six LLMs, eleven languages, and three tasks (sentiment, topic, intent), RoSE identifies the optimal generator more often than any other intrinsic heuristics. RoSE outperforms intrinsic heuristics and comes within 0.76 percentage points of the optimal generator baseline. This result is measured in terms of downstream performance, obtained by training a small model on the chosen generator's outputs (optimal vs. proxy metric selected) and evaluating it on human-labelled test data. Additionally, RoSE is the only metric to achieve a positive correlation with performance on human test data.

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

ROMPAR: Morphological Completion and Demographic Unlearning for Romanian-Accented Speech Recognition

作者:
Andrei-Marius Avram↗Aureliu-Valentin Antonie↗\c{S}tefan-Bogdan Badea↗Andrei Florea↗Robert-Nicolae Zaharoiu↗Dumitru-Clementin Cercel↗

Automated transcription of parliamentary proceedings faces significant hurdles due to demographic bias, dialectal variation, and technical artifacts such as utterance truncation during segmentation. This paper introduces the ROManian PARliamentary Speech Corpus (ROMPAR) dataset, a 17.80-hour corpus of Romanian and Moldavian parliamentary speech, featuring double-annotated ground truth and explicit labels for reconstructed word fragments. To build a robust ASR system, we propose a multi-task adversarial training framework that enforces demographic invariance across age, gender, and dialect. We address the inherent instability of adversarial objectives in generative architectures by introducing an exponential decay mechanism for the adversarial coefficients. Furthermore, we implement an LLM-guided decoding strategy with position-dependent weighting to facilitate morphological completion of truncated terminal words. Our results demonstrate that the proposed framework significantly reduces WER and achieves an F1-score of 96.6% in morphological reconstruction.

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08.
arXiv (CS.CV) 2026-06-12

InterleaveThinker: Reinforcing Agentic Interleaved Generation

作者:
Dian Zheng↗Harry Lee↗Manyuan Zhang↗Kaituo Feng↗Zoey Guo↗Ray Zhang↗Hongsheng Li↗

Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs, identify samples that deviate from the planned instructions, and refine the instructions for regeneration. To implement this pipeline, we construct the Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k to perform a format cold-start. Then we develop Interleave-Critic-RL-13k to reinforce the step-wise instruction correction capability within a generation trajectory using GRPO. Since a single interleaved generation trajectory may involve over 25 generator calls, optimizing the entire trajectory is computationally impractical. Therefore, we propose accuracy reward and step-wise reward, allowing single-step RL to effectively guide the entire generation trajectory. The results show that InterleaveThinker improves performance across various image generators. On interleaved generation benchmarks, it achieves performance comparable to Nano Banana and GPT-5. Surprisingly, it also significantly enhances the base model on reasoning-based benchmarks; for example, on 4-step FLUX.2-klein, we observe substantial gains on WISE and RISE.

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09.
PLOS Medicine 2026-05-26

Requiring code sharing to strengthen transparency and trust in research

作者:
Helen Lumbard↗

by Helen Lumbard, Lauren Cadwallader, Devin Soper, on behalf of the PLOS Medicine Staff Editors PLOS Medicine has always championed open science and data transparency. Now, recognizing that code is as essential a research artifact as the data it analyzes, we are strengthening our code sharing policy to further ensure reproducibility and trust in the scientific record. Recognizing that code is as essential a research artifact as the data it analyzes, this Editorial outlines how PLOS Medicine is strengthening its code sharing policy to further ensure reproducibility and trust in the scientific record.

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11.
arXiv (CS.AI) 2026-06-17

DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

作者:
Dake Bu↗Wei Huang↗Andi Han↗Hau-San Wong↗Qingfu Zhang↗Taiji Suzuki↗Atsushi Nitanda↗

arXiv:2604.24357v2 Announce Type: replace-cross Abstract: Diffusion language models generate without a fixed left-to-right order, leaving token ordering as a central algorithmic choice. Existing systems mainly use random masking or confidence-driven ordering, which respectively suffer from train–test mismatch and myopic exploration. We introduce DPRM (Doob -transform Process Reward Model), a plug-in token-ordering module that keeps the host architecture, denoising objective and supervision unchanged, and modifies only the ordering policy. DPRM starts from confidence-driven ordering and gradually shifts to process-reward-guided ordering through online estimates. We characterize the exact DPRM policy as a reward-tilted Gibbs reveal law, prove convergence of its stagewise Soft-BoN approximation, show that the online bucketized controller tracks the exact DPRM score at empirical-Bernstein rates, and establish a sample-complexity advantage under tractable optimization assumptions. Across nine hosts covering language reasoning, test-time scaling, protein, single-cell, molecular, DNA, text-to-image generation, and VQA, DPRM order variants improve several language, DNA, and multimodal settings while also identifying boundary cases where confidence-only ordering or task-specific utilities are preferable. Code is available at: https://github.com/DakeBU/DPRM-DLLM

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12.
arXiv (CS.LG) 2026-06-19

Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities

作者:
Alexandra Carpentier↗Christophe Giraud↗Nicolas Verzelen↗

arXiv:2509.15822v3 Announce Type: replace-cross Abstract: Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number $K$ of communities is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold. This conjecture has given rise to a rich literature, proving that non-trivial community recovery is indeed possible in SBM above the KS threshold. Failure of low-degree polynomials (LDP) below the KS threshold was also proven, as long as $K\ll \sqrt{n}$, where $n$ is the number of nodes in the observed graph. When $K\geq \sqrt{n}$, Chin et al.(2025) recently proved that, in a sparse regime, community recovery in polynomial time is possible below the KS threshold by counting non-backtracking paths. This breakthrough led them to postulate a new threshold for the many-communities regime $K\geq \sqrt{n}$. In this work, we provide evidence supporting their conjecture:\\ 1- We prove that, for any graph density, LDP fail to recover communities below the threshold postulated by Chin et al.(2025) ;\\ 2- We prove that community recovery is possible in polynomial time above the postulated threshold, not only in the sparse regime considered in Chin et al.~(2025), but also in moderately sparse regimes, by counting occurrences of some specific motifs inspired by the LDP analysis.\\ In particular, counting self-avoiding paths of length $\log(n)$, which is closely related to spectral algorithms based on the Non-Backtracking operator, is optimal only in the sparse regime. More complex motifs based on the blow-up of a cycle must be considered in denser regimes.

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

Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure

作者:
Moritz Schlager↗Friederike Jungmann↗Samuel Schmidgall↗Philipp Raffler↗Franziska Hartl↗Eva Wende↗Paula Ro{\ss}m\"uller↗Conrad Ketzer↗Avinatan Hassidim↗Dale R. Webster↗Yossi Matias↗Yun Liu↗…

arXiv:2606.17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies. However, existing approaches often lack realism and controllability, often oversharing information unprompted, and failing to capture the wide variability of patient behavior. Here, we introduce PatientsWithPersonality (PWP), a patient simulation framework that generates realistic yet diverse virtual patient responses through explicit personality parametrization over a latent patient state. Grounded in HEXACO, a six-dimensional personality space used to quantify and parameterize human behavioral traits, our approach enables fine-grained control over conversational style, cooperativeness, and information disclosure within a unified framework. In a clinician evaluation, PWP is judged nearly as realistic as recorded human actors and clearly ahead of prior simulators, while being flagged as "too informative" far less often. Conditioning on HEXACO axes yields personas whose configured traits are recoverable by both clinicians and an autorater, span a substantially wider behavioral footprint than the closest baseline, and prevent oversharing. Altogether, our framework paves the way for more accurate and informative LLM benchmarking through our realistic and steerable patient simulator.

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14.
arXiv (CS.CL) 2026-06-12

LLMs Can Better Capture Human Judgments–With the Right Prompts

作者:
Danica Dillion↗Chen Cecilia Liu↗Baihui Wang↗Daniele Barolo↗Tanmay Rajore↗Niket Tandon↗Pranathi Ravikumar↗Kurt Gray↗

Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judgments are unstable across wording variations. We demonstrate simple prompting strategies that mitigate these limitations. Across two datasets–a U.S.-representative set of 144 moral scenarios and 38 moral beliefs from the International Social Survey Programme's Family and Changing Gender Roles module covering 32 countries–we show how simple elicitation techniques help improve AI-human alignment. First, prompting models to report standard deviations and response proportions recovers the full range of human responses better than common strategies. Second, ensuring scenarios are clear to human participants–as reflected in human confusion ratings–boosts model alignment, and LLMs can track human confusion ratings. At the same time, we find that LLMs' estimates of their own error are poorly calibrated, though they can predict human variability relatively well. These results suggest that asking better questions to LLMs can yield better answers.

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15.
arXiv (CS.AI) 2026-06-16

The Initial Exploration Problem in Knowledge Graph Exploration

作者:
Claire McNamara↗Lucy Hederman↗Declan O'Sullivan↗

arXiv:2602.21066v2 Announce Type: replace Abstract: Knowledge Graphs (KGs) enable the integration and representation of complex information across domains, but their semantic richness and structural complexity create substantial barriers for lay users without expertise in semantic web technologies. When encountering an unfamiliar KG, such users face a distinct orientation challenge: they do not know what questions are possible, how the knowledge is structured, or how to begin exploration. This paper identifies and theorises this phenomenon as the Initial Exploration Problem (IEP). Drawing on theories from information behaviour and human-computer interaction, including ASK, exploratory search, information foraging, and cognitive load theory, we develop a conceptual framing of the IEP characterised by three interdependent barriers: scope uncertainty, ontology opacity, and query incapacity. We argue that these barriers converge at the moment of first contact, distinguishing the IEP from related concepts that presuppose an existing starting point or information goal. Analysing KG exploration interfaces at the level of interaction primitives, we suggest that many systems rely on epistemic assumptions that do not hold at first contact. This reveals a structural gap in the design space: the absence of interaction primitives for scope revelation, mechanisms that communicate what a KG contains without requiring users to formulate queries or interpret ontological structures. In articulating the IEP, this paper provides a theoretical lens for evaluating KG interfaces and for designing entry-point scaffolding that supports initial exploration.

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

AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization

作者:
Tianyi Li↗Zixuan Wang↗Guidong Lei↗Xiaodong Li↗Hui Li↗

arXiv:2603.21613v2 Announce Type: replace-cross Abstract: Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at https://anonymous.4open.science/r/AgenticRec-FB16.

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17.
arXiv (CS.CL) 2026-06-11

System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5

作者:
Haotao Xie↗

Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited. The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited. To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference. Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this domain. We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model. Experimental results on the CCL25-Eval Task 5 benchmark demonstrate that PoetryQwen achieves a score of 0.757, representing a 9.7% improvement over the Qwen2.5-14B-Instruct baseline (0.690). These findings clearly indicate that PoetryQwen significantly enhances performance in precise translation and emotional understanding of classical poetry. We present new dataset and methodological considerations intended to support the domain-specific optimization of LLMs.

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

Graph Regularized Non-negative Reduced Biquaternion Matrix Factorization for Color Image Recognition

作者:
Hailang Wu↗Yonghe Liu↗Bingxuan Yu↗Chaoqian Li↗

Non-negative reduced biquaternion matrix factorization (NRBMF) uses the product of reduced biquaternion (RB) matrices to incorporate the non-negativity constraints of color image pixels into the factorization process. However, NRBMF mainly focuses on reconstruction accuracy and does not explicitly exploit the local geometric structure of image data, which may limit the discriminative ability of the obtained low-dimensional coefficient representations. To address this issue, we propose a graph regularized non-negative reduced biquaternion matrix factorization (GNRBMF) model for color image recognition. The proposed model incorporates a graph Laplacian regularizer into the reduced biquaternion coefficient matrix, encouraging nearby samples in the original space to have similar coefficient representations. Meanwhile, GNRBMF retains the non-negativity property of NRBMF in the reduced biquaternion algebra. To solve the optimization problem, a component-wise alternating projected gradient algorithm is derived, and its convergence properties are analyzed. Experimental results on three color image datasets show that the proposed GNRBMF model achieves competitive or superior recognition performance compared with several methods in most tested settings.

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

Improved Stochastic Optimization of LogSumExp

作者:
Egor Gladin↗Alexey Kroshnin↗Jia-Jie Zhu↗Pavel Dvurechensky↗

arXiv:2509.24894v4 Announce Type: replace-cross Abstract: The LogSumExp function, dual to the Kullback-Leibler (KL) divergence, plays a central role in many important optimization problems, including entropy-regularized optimal transport (OT) and distributionally robust optimization (DRO). In practice, when the number of exponential terms inside the logarithm is large or infinite, optimization becomes challenging since computing the gradient requires differentiating every term. We propose a novel convexity- and smoothness-preserving approximation to LogSumExp that can be efficiently optimized using stochastic gradient methods. This approximation is rooted in a sound modification of the KL divergence in the dual, resulting in a new $f$-divergence called the Safe KL divergence. Our experiments and theoretical analysis of the LogSumExp-based stochastic optimization, arising in DRO and continuous OT, demonstrate the advantages of our approach over existing baselines.

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20.
arXiv (math.PR) 2026-06-16

An Algebraic Matrix Spencer Theorem

作者:
Emrullah Akbas↗Suvrit Sra↗

arXiv:2606.16005v1 Announce Type: new Abstract: We develop an algebraic approach to matrix discrepancy based on the representation theory of finite-dimensional C$^*$-algebras. As an application, we resolve a substantial structured special case of the Matrix Spencer conjecture. In particular, we show that for every family of contractions $A_1,\ldots,A_n$ that are contained in a finite-dimensional $C^*$-algebra $\mathcal A$ with $dim_{\mathbb C} (\mathcal A) \lesssim n$, there exists signs $x\in\{\pm1\}^n$ such that $\|\sum_{i=1}^n x_i A_i\| \le O(\sqrt n)$. As a noteworthy special case, our main result also resolves the Group Spencer conjecture of (Bandeira'24). We furthermore prove that Matrix Spencer continues to hold for low-rank perturbations of matrix families coming from an $C^*$-algebra of small dimension.

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21.
arXiv (math.PR) 2026-06-18

Probabilistic representation and classical solutions of wave equations with complex polynomial nonlinearities

作者:
Joshua J. Y. Chan↗Nicolas Privault↗

arXiv:2606.18919v1 Announce Type: cross Abstract: We review the probabilistic representation of solutions of wave equations with polynomial nonlinearities in spatial dimensions d=1,2,3 using stochastic branching processes. Under regularity assumptions on the initial data, we derive conditions ensuring the integrability of the corresponding Monte Carlo estimator, and the existence and smoothness of mild and classical solutions. We also present numerical results and comparisons with grid-based algorithms for the solution of nonlinear wave equations.

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22.
arXiv (CS.CL) 2026-06-15

LoSoNA: A Benchmark for Local Social Norm Adaptation in Group Conversations

作者:
Mateusz Winiarek↗Maksymilian Bilski↗Mateusz Jacniacki↗

Online group chats are social spaces with local conversational norms that are rarely stated explicitly. The ability and willingness of LLM-based agents to recognize and adapt to these norms remains mostly unexplored. We introduce LoSoNA, a benchmark for local social norm adaptation in multi-party chat. Each scenario gives a subject model a curated group-chat transcript in which non-subject participants demonstrate a hidden local norm, followed by a final elicitor turn that forces a response revealing whether the subject has inferred that norm. We evaluate eight frontier and open-weight models under four prompting conditions that vary how explicitly the model is told to treat the prior conversation as evidence for how it should answer. Naive prompting remains limited for most models; explicit norm-aware prompting helps unevenly, with Gemini 3.1 Pro reaching $84.2\%$ and Claude Fable 5 reaching $81.6\%$, while several other models show small gains or regressions. LoSoNA contributes to recent calls for evaluating LLM social capabilities by testing whether models can infer local conversational norms from precedent and use them in a one-turn group-chat response.

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23.
medRxiv (Medicine) 2026-06-15

A More-Than-Human Approach to Designing for Mental Health: Remixing Prototypes for the Contexts of Complex Healthcare Infrastructures

作者:
Allen↗Stasiak↗Lottridge↗

Digital mental health tools (DMHTs) often fail to be successfully implemented in clinical settings. While user- and human-centred design frameworks are frequently proposed for developing effective tools, they are insufficient to address the sociotechnical complexity of healthcare environments. This paper addresses this limitation by detailing the application of a more-than-human design framework to incorporate wider contextual factors into design decisions. To demonstrate the application of this more-than-human design framework, we present a case study showcasing the design of one specific feature within a DMHT intended to support Health Improvement Practitioners (HIPs) in New Zealand's Integrated Primary Mental Health and Addictions (IPMHA) service. Our process blends usage-context storyboards with interface prototypes, using think-aloud interviews to test the contextual fit of our prototypes. The initial design concept failed due to contextual factors such as inconsistent wait times and the administrative burden on clients and clinic staff. This led to a pivot to a more context-appropriate, practitioner-focused, in-session concept for digital psychometric administration and automated scoring. This case study demonstrates that for DMHTs to be viable within complex healthcare environments, design must focus on more than the needs of a single user, incorporating multiple stakeholders and contextual variables across the wider service-delivery context.

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24.
arXiv (math.PR) 2026-06-16

Universality in the target arrival statistics of non-conservative search processes

作者:
Jos\'e Giral-Barajas↗Samantha Linn↗Paul C. Bressloff↗

arXiv:2606.16025v1 Announce Type: cross Abstract: Stochastic search processes in which searchers are continuously introduced to and removed from a target search domain are fundamental to a wide class of physical and artificial systems. The theory of such non-conservative search processes is, however, much less developed than for search processes with a fixed number of particles. Here we exploit a natural mapping between non-conservative stochastic search and queueing theory to derive the full time-dependent distribution of target arrivals under minimal assumptions on the underlying search process. Remarkably, we find that the steady-state inter-arrival time distribution is exactly exponential, regardless of the details of the search process, showing a robust universality that emerges directly from the queueing framework. Thus, counterintuitively, the arrival statistics of a non-conservative search process are much simpler than sequential search-and-capture processes involving a fixed number of searchers. This has major implications for target resource accumulation, where the delivery of resources is counter-balanced by their downstream consumption.

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25.
arXiv (CS.CV) 2026-06-11

Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning

作者:
Romana Qureshi↗Hafida Benhidour↗Said Kerrache↗Nahlah Aljeraisy↗

Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance. Although the Lottery Ticket Hypothesis (LTH) shows that sparse subnetworks can match dense networks when trained from suitable initializations, its iterative pruning procedure requires multiple complete training cycles. This work evaluates progressive magnitude-based pruning as a single-cycle alternative. The method gradually increases sparsity during training using a linear schedule and updates pruning masks based on active weight magnitudes. We conduct systematic experiments on CIFAR-10 and MNIST across ResNet, VGG-style, and LeNet architectures, comparing the proposed method with representative iterative and initialization-based pruning baselines, including LTH, SNIP, and GraSP. On CIFAR-10, the method achieves 95.12\% accuracy on ResNet-18 at 72.9\% sparsity, compared with 90.5\% reported for LTH. At extreme sparsity, it achieves 93.13\% accuracy on a VGG-like architecture at 97\% sparsity, compared with approximately 92.0\% for SNIP, and 93.44\% accuracy on VGG-19 at 97.97\% sparsity, compared with 92.19\% for GraSP at 98\% sparsity. A sparsity-accuracy analysis on ResNet-18 further shows that accuracy remains within 0.1 percentage points of the dense baseline across 70–85\% sparsity. These results indicate that progressive magnitude-based pruning provides an effective single-cycle approach for neural network sparsification under the evaluated settings.

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