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01.

medRxiv (Medicine) 2026-06-11 DOI: HASH:2216df3fdc9843cc928a68ae06d0a664

Hantavirus Disease in Uruguay: Trends and Mortality Before and During the COVID-19 Pandemic.

作者:

criscuolo↗Blanco↗Ferrara↗Ciaccio↗Gomez Carassale↗Gonzalez Reyes↗Machado Rivero↗Sosa Dias↗Facal Castro↗J. A↗

Introduction: Hantavirus disease is an emerging and potentially severe zoonosis of global distribution. In Uruguay, it is transmitted by rodents inhabiting peridomestic, suburban, and rural areas. Global incidence is estimated at 150,000 to 200,000 cases per year, with up to 300 annual cases in the Americas. Since 1997, Uruguay's Ministry of Public Health (MPH) has monitored Hantavirus cardiopulmonary syndrome (HCPS), the most common clinical presentation in the region. By 2019, a total of 271 cases had been identified in the country, with an estimated mortality rate of nearly 50%. Objectives: To describe the clinical, epidemiological, and occupational characteristics of patients with Hantavirus disease in Uruguay during the pre-pandemic (2018-2019) and pandemic (2020-2021) periods. Methods: A descriptive, cross-sectional, observational study was conducted, including all serologically confirmed cases of Hantavirus infection reported to the MPH between 2018 and 2021. Clinical and demographic data were extracted from the mandatory reporting form for zoonotic diseases. Incidence and case fatality rates were calculated, and factors associated with fatal outcomes were analyzed. Results: A total of 58 confirmed cases were identified between 2018 and 2021. Most patients were male (62%), with a mean age of 36.5 years (SD 16). A decline in incidence was observed during 2020-2021, with no significant change in case fatality. Direct rodent exposure was the most frequently associated risk factor. Montevideo and Canelones were the most affected departments. Renal and pulmonary involvement were significantly associated with mortality. Conclusion: Hantavirus remains a relevant public health concern in Uruguay. Although a decrease in incidence was observed during the COVID-19 pandemic years, case fatality rates remained high. The findings underscore the need for sustained surveillance and early recognition, particularly in urbanizing regions.

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02.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.15393

Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces

作者:

Binyamin Perets↗Shie Mannor↗

arXiv:2606.15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the procedure should account for the inherent structure in the hypothesis space, when such structure exists. Here, we present a framework for controlling the false discovery rate both when each hypothesis is evidenced only by a finite count of null draws, leaving its p-value uncertain, and when the hypothesis space carries arbitrary structure, requiring only that the structure be represented through a suitable reproducing kernel. We present two decision rules that are both robust to structural mis-specification, yet offer a distinct trade-off between exact FDR control and statistical power. The first rule guarantees exact FDR control; the second maximizes power by adapting mirror-statistic control into count space, utilizing an analytical framework to assess FDR control when exact mirror symmetry is relaxed. Furthermore, the tractability gained by the RKHS framework allows us to directly investigate finite-data uncertainties, which we leverage to suggest a policy for the efficient allocation of null distribution samples.

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03.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2606.18816

SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface

作者:

Gourav Siddhad↗Yogesh Kumar Meena↗

arXiv:2606.18816v1 Announce Type: cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware. To address this, we propose SwitchBraidNet, a compact EEG classification architecture designed for low-power deployment. The model employs a dual-path temporal braid to extract multiscale oscillatory features, an adaptive squeeze-and-excitation spatial switch for electrode gating, and a log-variance readout layer for direct band-power encoding. Furthermore, through systematic quantisation-aware training on the OpenBMI dataset, we compared SwitchBraidNet against four established baselines across FP32, FP16, and INT8 precisions. Experimental results demonstrate superior efficiency and performance, achieving MI accuracy of 69.49% (FP16), SSVEP accuracy of 93.48% (FP32), and a hybrid information transfer rate of 64.82 bits/min (FP16). With an INT8 footprint of only 3.03 KB, SwitchBraidNet maintains high accuracy across varying numerical precisions, demonstrating its suitability for low-power embedded BCI deployment.

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04.

arXiv (quant-ph) 2026-06-12 DOI: arXiv:2507.15918

Coarse-grained quantum thermodynamics: Observation-dependent quantities, observation-independent laws

作者:

Giulia Rubino↗\v{C}aslav Brukner↗Gonzalo Manzano↗

arXiv:2507.15918v2 Announce Type: replace Abstract: In both classical and quantum thermodynamics, physical quantities are typically assigned objective values defined independently of our observations. We then refer to the 'work performed by a gas', or the 'entropy of the gas', regardless of how they are evaluated. Here, we question this conception in the context of quantum thermodynamics, estimating how the definition of pivotal thermodynamic quantities is affected by experimental instruments of limited precision. We find that the coarse-grained thermodynamic quantities frequently lead to different conclusions from those drawn in fine-grained scenarios. For instance, the irreversibility of a process, or its work payoff, can significantly vary with the instrument precision. We show nonetheless that coarse-grained thermodynamic quantities satisfy the same relations (i.e., the second law inequality, the relation between dissipation and distinguishability of a process from its time-reverse, and the quantum work fluctuation theorems) as their fine-grained counterparts. These results highlight the observation-independence of relations linking thermodynamic quantities which are themselves observation-dependent.

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05.

arXiv (quant-ph) 2026-06-12 DOI: arXiv:2606.12526

Multi-entropy in heavy local quenches

作者:

Kosei Fujiki↗Kenya Tasuki↗

arXiv:2606.12526v1 Announce Type: cross Abstract: We study the time evolution of tripartite entanglement in heavy local quenches in two-dimensional holographic conformal field theories. Our diagnostic is the genuine multi-entropy of adjacent intervals, computed from both bulk and boundary perspectives. A perturbative bulk analysis shows that the first-order small-mass perturbation around the vacuum geodesic network cancels identically at any time after the quench. In the fully back-reacted geometry, a vacuum-subtracted genuine multi-entropy arises from a mismatch between the winding selected by the trivalent geodesic network and the windings selected independently by the pairwise geodesics. In the sharp quench limit, the time dependence of genuine multi-entropy is kinematically fixed to logarithms of rational functions of time and is independent of the heavy operator dimension. The CFT calculation reproduces the same formula within the heavy-light vacuum block approximation, where the branch choice in the heavy-background uniformization map corresponds to the winding selection in the bulk. These results indicate that, in this setup, the genuine multi-entropy is controlled by global saddle selection, rather than by a local energy response or quasiparticle propagation.

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06.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.17056

The Value Axis: Language Models Encode Whether They're on the Right Track

作者:

Nick Jiang↗Isaac Kauvar↗Jack Lindsey↗

We investigate whether language models internally track the value of their current trajectory, defined as the likelihood that their ongoing strategy will achieve their goals. Using synthetic, in-context reinforcement learning data, we construct a "value" axis for Qwen3-8B. We find that activations along this axis distinguish between high vs. low verbalized confidence, rollouts without and with backtracking, and correct vs. corrupted code. Steering towards high value causally suppresses self-correction and reduces explanatory verbosity, while steering towards low value induces backtracking and exploration. We demonstrate that direct preference optimization (DPO) can increase the internal value of rewarded behaviors (e.g. use a certain word), causing the model to act more confidently after exhibiting them. Finally, we apply the value axis to study in-the-wild settings. For example, we find that Qwen assigns low value to politically sensitive chat queries after post-training and that supervised fine-tuning increases internal confidence within the training domain. Our results suggest that language models linearly encode an estimate of expected goal success that modulates their confidence in pursuing a direction.

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07.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2506.11139

Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals

作者:

Namhoon Kim↗Sara Fridovich-Keil↗

Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth, as well as both overfitting and generalization tasks including tomography, super-resolution, and denoising. By stratifying performance according to model size as well as signal type and bandwidth, our results shed light on how different INR and grid representations allocate their capacity. We find that, for many tasks involving dense signals, a simple regularized grid with interpolation trains faster and to higher or comparable quality than any INR with the same number of parameters. We also find limited settings – namely fitting binary signals such as shape contours – where INRs outperform grids, to guide future development and use of INRs towards the most advantageous applications.

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08.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14746

Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning

作者:

Shiwen Zhang↗Haoyuan Wang↗Xianghao Zang↗Haibin Huang↗Chi Zhang↗Xuelong Li↗

Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to entangled content and style features. With a reverse triplet synthesis pipeline to build a million-scale training set and a dual-branch Style-Content DiT (SC-DiT) that decouples style and content via separate ROPE embeddings and causal masking, we observe that such a one-stage training paradigm on mixed style categories causes semantic styles to dominate, hindering texture style learning, and harming content preservation. To address these issues, we propose Style-CCL, a Multi-Stage Curriculum Continual Learning framework that trains SC-DiT from semantic (easy) to texture (hard) styles, and from clean to synthetic data, with Random Memory Rehearsal across stages to avoid catastrophic forgetting. Extensive experiments demonstrate that our Style-CCL achieves state-of-the-art performance in three core metrics: style similarity, content consistency, and aesthetic quality.

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09.

medRxiv (Medicine) 2026-06-11 DOI: HASH:bc550c4dbf0329acfee557b88bbb634a

Parent and physiotherapist perceptions about movement skills of young children with juvenile idiopathic arthritis

作者:

Letts↗Herrington↗Batthish↗Bedard↗Bremer↗Gorter↗J. W↗King-Dowling↗Obeid↗

Objective: The onset of juvenile idiopathic arthritis (JIA) in the early years ([≤]5 years) may negatively impact movement skill (encompassing related concepts of gross motor skills, fundamental movement skills, and functional ability) development. Few studies have explored the perceptions and needs of parents and physiotherapists towards children's difficulty with these movement skills, essential to identify potential areas for added support. The objective of this study is to understand the perceptions of physiotherapists and parents towards movement skills of children with JIA. Methods: Seventeen parents and 24 physiotherapists completed an online questionnaire consisting of multiple choice and open-ended questions about the movement skills of young children with JIA. Demographic and multiple choice questions were quantitively analysed using descriptive statistics. Open-ended responses were analyzed using qualitative conventional content analysis. Results: About half (47%) of parents perceived their children to have movement difficulties, and 75% of physiotherapists described the movement skills of children with JIA as worse than other children of the same age. Our qualitative analysis revealed three general themes including: functional task difficulties; clinical variability in movement skills; and psychosocial components of movement skill difficulties. Conclusion: This study provides an analysis of perceptions of physiotherapists and parents towards the movement skills of young children with JIA. A significant proportion of parents and physiotherapists identify movement difficulties among children with JIA that impact daily life. Future interventions co-designed with both parents and care providers targeting movement skills are needed.

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10.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11835

Designing AI-Supported Focus Groups: A Role x Modality Playbook

作者:

Zhiqing Wang↗Steven Dow↗

arXiv:2606.11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research. Focus groups are uniquely valuable because participants not only share individual accounts but also respond to one another, surfacing comparison, disagreement, and collective sensemaking. However, focus groups are resource-intensive and highly sensitive to facilitation: moderators must probe for specificity, balance participation, manage topic flow, and sustain psychological safety, and subtle facilitation choices can shape what becomes salient. Recent HCI work and commercial meeting tools show that generative AI can scaffold live conversation through prompting, turn regulation, thematic mapping, and real-time summarization. Yet UXR teams lack a clear map of what these capabilities mean in focus groups and what methodological risks they introduce. We synthesize AI supports for live conversation and translate them into a focus-group-specific playbook organized by AI role (tool, co-host, host) and modality (text, voice, embodied).We synthesize prior work on AI-supported live conversation and propose a focus-group-specific playbook of AI supports organized by role (tool, co-host, host) and modality (text, voice, embodied). We characterize interactional trade-offs and identify open questions for evaluating AI-supported focus groups as methodological configurations.

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11.

medRxiv (Medicine) 2026-06-22 DOI: HASH:fa9f900f92be4c2eefe3c8fb9f32ee24

Biopsychosocial determinants of HPV vaccine perception in university students of both sexes in Cucuta, Colombia, 2024: a cross-sectional study

作者:

ARIAS-SANCHEZ↗Florez↗Pereira↗Caceres-Penaloza↗D. Y↗Dallos Rivera↗L. S↗Nunez-Villamizar↗K. G↗Beltran - Arroyave↗

Colombia has been internationally recognised as a paradigmatic case of vaccine confidence crisis since the 2014 Carmen de Bolivar event, and national HPV vaccination coverage remains far below the World Health Organization 2030 target. Most published evidence focuses on female adolescents and on cervical cancer; the perception of the HPV vaccine in university-age populations of both sexes–and across the broader spectrum of HPV-attributable disease–remains comparatively understudied. We aimed to describe the influence of biopsychosocial determinants on HPV vaccine perception among university students of both sexes in Cucuta, Norte de Santander, Colombia. We conducted a cross-sectional study with a mixed quantitative-qualitative approach in 2024 among four universities (Universidad de Santander, Universidad Francisco de Paula Santander, Universidad de Pamplona and Universidad Libre; combined enrolment 21,033 students). Using convenience sampling stratified by institution, 750 actively enrolled undergraduate students of both sexes (18-60 years) completed a structured online questionnaire adapted from previously validated instruments. The instrument captured sociodemographic information, HPV knowledge and HPV vaccine perception. Data were analysed using Students t-test, one-way analysis of variance, Tukey post-hoc tests, effect sizes and 95% confidence intervals, with a 0.05 significance threshold. Of 750 respondents, 54.2% were women, 61.3% were under 20 years of age, and 75.1% attended public universities. HPV knowledge was high in 39.2%, intermediate in 42.4% and low in 18.4%; women and students aged 26 years or older displayed higher knowledge. Although 91.2% had heard of HPV and 82.5% knew that both sexes could acquire it, recognition of clinical manifestations and complications was uneven: cervical cancer 51.7%, penile cancer 30.5%, vaginal warts 45.9% and warts in the penis, larynx, anus or rectum 34.0%. Vaccine-specific knowledge was low in 77.1%, with men disproportionately represented (85.9% versus 69.5% in women). Overall positive perception of HPV vaccination was 66.6%, slightly higher in women (68.8%) than men (63.9%), in students aged 26 years or older (70.1%) and in students from private universities (68.1% versus 65.9%). Inferential analysis identified sex (Cohens d = -0.357), type of university (d = 0.189) and HPV knowledge (partial eta-squared = 0.096) as the only significant determinants. Age, socioeconomic stratum, age at sexual debut and vaccine-specific knowledge did not reach meaningful significance. HPV vaccine perception was predominantly positive but conditioned by three biopsychosocial determinants, with HPV knowledge as the primary driver. The persistent gender gap reflects historical anchoring of HPV messaging in cervical disease and female-targeted campaigns. Public-health strategies should adopt comprehensive, gender-inclusive educational interventions that explicitly visibilise non-cervical HPV-related cancers and address both sexes from a common evidence base.

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12.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2603.02818

Merged amplitude encoding for Chebyshev quantum Kolmogorov–Arnold networks: trading qubits for circuit executions

作者:

Hikaru Wakaura↗

arXiv:2603.02818v3 Announce Type: replace Abstract: Quantum Kolmogorov–Arnold networks based on Chebyshev polynomials (CCQKAN) evaluate each edge activation function as a quantum inner product, creating a trade-off between qubit count and the number of circuit executions per forward pass. We introduce merged amplitude encoding, a technique that packs the element-wise products of all $n$ input-edge vectors for a given output node into a single amplitude state, reducing circuit executions by a factor of $n$ at a cost of only 1–2 additional qubits relative to the sequential baseline. The merged and original circuits compute the same mathematical quantity exactly; the open question is whether they remain equally trainable within a gradient-based optimization loop. We address this question through numerical experiments on 10 network configurations under ideal, finite-shot, and noisy simulation conditions, comparing original, parameter-transferred, and independently initialized merged circuits over 16 random seeds. Wilcoxon signed-rank tests show no significant difference between the independently initialized merged circuit and the original ($p > 0.05$ in 28 of 30 comparisons), while parameter transfer yields significantly lower loss under ideal conditions ($p < 0.001$ in 9 of 10 configurations). On 10-class digit classification with the $8\times8$ MNIST dataset using a one-vs-all strategy, original and merged circuits achieve comparable test accuracies of 53–78\% with no significant difference in any configuration. These results provide empirical evidence that merged amplitude encoding preserves trainability under the simulation conditions tested.

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13.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.20563

JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising

作者:

Siang-Ling Zhang↗Huai-Hsun Cheng↗Tsung-Ju Yang↗Yu-Lun Liu↗

Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough challenge. Existing optimization-based methods are slow and can produce oversaturated colors. In contrast, naive stitching approaches fail to produce geometrically coherent objects. This results in visible unnatural seams and semantic leaks. In this paper, we present a fast and training-free framework for generating text-driven 3D visual illusions. Our approach decouples the generation into two stages. First, we propose a cross-space dual-branch denoising process. This process dynamically decodes 3D latents into voxel space for CLIP-guided orientation alignment and Signed Distance Field (SDF) blending, which ensures seamless geometric fusion. Second, we introduce a view-conditioned texture synthesis module that projects and aggregates view-specific 2D diffusion priors onto the fused geometry. Extensive experiments demonstrate that our method generates highly realistic, dual-semantic 3D illusions in just 3-5 minutes. It significantly outperforms existing methods in geometric integrity, semantic recognizability, and efficiency. Project page: https://siang1105.github.io/JanusMesh.github.io/

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14.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2512.02318

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

作者:

Junyu Wang↗Changjia Zhu↗Yuanbo Zhou↗Lingyao Li↗Xu He↗Mingkui Wei↗Junjie Xiong↗

arXiv:2512.02318v4 Announce Type: replace-cross Abstract: This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 representative MLLMs on 18 real-world CAPTCHA task types, measuring single-shot accuracy, success under limited retries, end-to-end latency, and per-solve cost. We further validate our findings through a supplemental external dataset and an adaptive-attacker setting with session memory, while also analyzing the impact of task-specific prompt engineering and few-shot demonstrations on solver effectiveness. We reveal that MLLMs can reliably solve recognition-oriented and low-interaction CAPTCHA tasks at human-like cost and latency, whereas tasks requiring fine-grained localization, multi-step spatial reasoning, or cross-frame consistency remain significantly harder for current models. By examining the reasoning traces of such MLLMs, we investigate the underlying mechanisms of why models succeed/fail on specific CAPTCHA puzzles and use these insights to derive defense-oriented guidelines for selecting and strengthening CAPTCHA tasks. To validate these principles, we present a proof-of-concept by hardening a vulnerable CAPTCHA type using our guidelines. We demonstrate that incorporating fine-grained localization and implicit counting reduces the success rate of state-of-the-art MLLMs from over 95\% to 0\%, confirming that structural changes can effectively mitigate the threat. We conclude by emphasizing the urgent need for CAPTCHA redesign as MLLM capabilities increasingly threaten existing defenses. Code Availability (https://doi.org/10.5281/zenodo.20406852).

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15.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16596

How Far Can Machine Translation Quality Take You? Extrinsic Discourse Evaluation in Goal-Oriented Setups

作者:

Wafaa Mohammed↗Kata Naszadi↗Vlad Niculae↗

Existing machine translation (MT) metrics and discourse-focused evaluations primarily assess translation quality intrinsically, without measuring the downstream consequences of translation errors. In this work, we focus on extrinsic discourse evaluation of machine translation under two distinct regimes: static and interactive. Under the static regime, we propose an entity counting task as a probe of referential consistency in discourse. We show that high intrinsic MT quality does not reliably predict downstream discourse success and strong MT systems still produce referential inconsistencies. For the interactive regime, we study the goal-oriented multi-agent Welfare Diplomacy game as a probe of long-horizon communication and coordination. We find that interaction-specific translation failures impact downstream coordination. Our results highlight goal-oriented environments as a viable framework for discourse-sensitive extrinsic MT evaluation.

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16.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.14997

AI Engram: In Search of Memory Traces in Artificial Intelligence

作者:

Jea Kwon↗Dong-Kyum Kim↗Jiwon Kim↗Yonghyun Kim↗Woong Kook↗Meeyoung Cha↗

arXiv:2606.14997v1 Announce Type: new Abstract: Memory formation is fundamental to intelligence, yet whether deep neural networks preserve identifiable memory traces analogous to biological memory units remains an open question. This work introduces a geometric framework to identify such "AI engrams" by formalizing the neuroscientific criteria of specificity, reactivation, sufficiency, and necessity into a constrained inverse problem. We derive a closed-form estimator that isolates individual memory traces from globally entangled parameters, and show that this biologically-derived solution corresponds to a natural gradient update on the parameter manifold. AI engrams enable surgical manipulation of learned knowledge: any subset of memories can be composed or erased through linear arithmetic, without iterative optimization. Experiments ranging from simple MLPs to LLMs demonstrate the causal validity and substantial scalability of AI engrams. Together, these results bridge theories of biological memory and artificial representation learning and offer geometric insight into how deep networks simultaneously support functional specificity within distributed storage.

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17.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2606.11381

From Simulation to Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting

作者:

Woojung Son↗Won Suk Lee↗Zijing Huang↗Daeun Choi↗Catia Silva↗Yu She↗Yan Gu↗

Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing 6D pose estimation methods have therefore relied solely on synthetic data that lacks scene-level realism, leaving their performance under real agricultural field conditions unquantified. In this work, we present, to the best of our knowledge, the first real-world 6D pose ground truth dataset of strawberries collected in actual agricultural fields (12,040 images). We also introduce a synthetic dataset rendered in NVIDIA Isaac Sim, featuring scene-level realism and domain randomization. Nevertheless, our experiments reveal that a significant sim-to-real gap persists, underscoring the necessity of real agricultural field data for reliable evaluation. We further quantify the sim-to-real gap through baseline 6D pose estimation results across backbone encoders, serving as a reference for future work. The real-world dataset will be made available upon acceptance.

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18.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2507.00875

TransLaw: A Large-Scale Dataset and Multi-Agent Benchmark Simulating Professional Translation of Hong Kong Case Law

作者:

Xi Xuan↗Chunyu Kit↗

Translating Hong Kong Court Judgments from English to Traditional Chinese is mandated by Articles 8-9 of the Basic Law, yet remains constrained by a shortage of parallel resources and rigorous demands on legal terminology, citation format, and judicial style. We introduce HKCFA Judgment 97-22, the first large-scale sentence-aligned parallel corpus for HK case law, comprising 344 professionally translated judgments (11,099 sentence pairs; 2.1M tokens) spanning 1997-2022. Building on this resource, we propose TransLaw, a multi-agent framework that decomposes translation into word-level expression, sentence-level translation, and multidimensional review, integrating a specialized Hong Kong legal glossary database, Retrieval-Augmented Generation, and iterative feedback, with four-dimensional expert review covering semantic alignment, terminology, citation, and style. Benchmarking 13 open-source and commercial LLMs, we demonstrate that TransLaw significantly outperforms single-agent baselines across all evaluated models, with convergence within 3 iterations. Human evaluation by 10 certified legal translators using our proposed Legal ACS metric confirms gains in legal-semantic accuracy, while showing that TransLaw still trails human experts in stylistic naturalness. The dataset and benchmark code are available at https://github.com/xuanxixi/TransLaw.

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19.

medRxiv (Medicine) 2026-06-22 DOI: HASH:b9a08ad45cb19f958318b6ea55660667

Longitudinal multi-omics characterization of the malignant evolution in multirelapsing glioblastoma

作者:

Lackman↗M. H↗Wardell↗Darrigues↗De Loose↗Lyle↗G. A↗Xue↗Learned↗Cheney↗Vaske↗O. M↗…

Linking glioblastoma (GBM) evolution to clinical progression is challenged by multiple factors, including tumor location for repeated sample collection, and short patient survival. In a single individual, we collected and analysed samples from 11 operations distributed across 31 months of multi-relapsing and multifocal GBM, including terminal leptomeningeal progression. All samples shared genomic ancestry of the retinoblastoma protein 1 (RB1) and neurofibromin 1 (NF1) mutations while advanced progression and extracranial metastases featured mutations of tuberous sclerosis complex 2 (TSC2), PBRM1, CD22 and Fanconi anemia supplementation group I (FANCI), correlated with clinical resistance to immunotherapies and DNA-damaging agents. Single-cell analytics revealed distinct yet reversible shifts in response to the precision medicine arsenal. GBM parenchymal dissemination and extracranial progression were associated with strengthening of neuron-like cell phenotypes. Our multidimensional study describes GBM evolution over a rarely reported time scale, and provides a valuable resource linking genetic, molecular, cellular and clinical progressions.

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20.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.15117

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

作者:

Elham Abolhasani↗Maryam Ramezani↗Hamid R. Rabiee↗

The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both. This raises severe privacy and societal concerns. Numerous studies in this area have yielded promising intra-domain results; however, these models frequently exhibit decreased efficacy when faced with data from dissimilar domains. Consequently, recent deepfake detection approaches focus on enhancing the generalization ability through multiple techniques that incorporate all input modalities, including audio, images, and their interactions. In this regard, we propose the EAV-DFD method, a generalized deep ensemble audio-visual model (EAV-DFD) combined with a domain adaptation mechanism utilizing a teacher-student framework to enhance the model's ability to perform and generalize effectively across unseen domains. To evaluate the model's performance, we used the FakeAVCeleb dataset as the primary domain and the DFDC, Deepfake_TIMIT, and PolyGlotFake datasets as an unseen domain. Our experimental results demonstrate that the proposed framework is efficient in domain adaptation, improving AUC performance of the model by 4.09%, 17.94%, and 0.5% on three unseen datasets, using only a small portion of them to train the student model. This leads to a novel deepfake detection model capable of adapting to new domains and interpreting which modality has been manipulated, highlighting the potential of our approach for real-world applications.

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21.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.00807

Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation

作者:

Nicholas Davis↗

arXiv:2606.00807v2 Announce Type: replace Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI, machine learning, and many generative systems, the dominant unit of analysis remains the individual model or autonomous system evaluated through outputs, benchmarks, prediction accuracy, or optimization performance. While these approaches have produced major advances, they often under-theorize the role of interaction in the emergence of intelligence, creativity, meaning, and adaptive behavior. This paper proposes interaction as the primary unit of analysis for co-creative AI and interaction-centered intelligence more broadly. Drawing from distributed cognition, embodied cognition, enaction, participatory sense-making, human-computer interaction, and computational creativity, the paper traces a historical progression toward increasingly relational accounts of intelligence. Building upon prior work in Creative Sense-Making, quantified co-creation, and co-creative systems such as the Drawing Apprentice and AI Drawing Partner, it argues that intelligence emerges through evolving interaction dynamics among agents, environments, and socio-technical systems rather than solely through internal computation. The paper introduces Interaction-Centered Intelligence as a framework for understanding human-AI co-creation, collaborative emergence, adaptive participation, and interactional dynamics. Rather than evaluating intelligence solely through generated outputs, the framework emphasizes interaction trajectories, coordination patterns, participatory engagement, adaptive regulation, and interactional drift unfolding through time. Implications for explainable co-creative AI, hybrid intelligence, enactive AI, and future human-AI systems are discussed.

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22.

arXiv (math.PR) 2026-06-18 DOI: arXiv:2606.11085

Geometric obstructions to Lipschitz transport between weighted Hessian $\mathrm{CD}(\kappa,\infty)$ manifolds

作者:

William Dudarov↗Dan Mikulincer↗

arXiv:2606.11085v2 Announce Type: replace Abstract: We construct a weighted Riemannian manifold $(\mathbb R^2,g,\mu)$ satisfying $\mathrm{CD}(1/2,\infty)$, the curvature-dimension condition, with the following property: if $\gamma$ denotes a centered Gaussian measure on $\mathbb R^2$, then there is no Lipschitz map $T:(\mathbb R^2,\|\cdot\|) \to (\mathbb R^2,g)$ satisfying $T_\#\gamma=\mu$. Building on this, we prove a Weyl-type asymptotic law for the eigenvalues of the weighted Laplacian $-\Delta_{g,\mu}$ and show that they are asymptotically negligible when compared to the eigenvalues of $-\Delta_{\gamma}$. These results give strong counterexamples to two questions of E. Milman and complement the recent counterexample of Aryan.

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23.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2602.14367

InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

作者:

Shuofei Qiao↗Yunxiang Wei↗Xuehai Wang↗Bin Wu↗Boyang Xue↗Ningyu Zhang↗Hossein A. Rahmani↗Yanshan Wang↗Qiang Zhang↗Keyan Ding↗Jeff Z. Pan↗Huajun Chen↗…

The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. The fundamental nature of scientific evaluation needs knowledgeable grounding, collective deliberation, and multi-criteria decision-making. However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions, and the inherent bias in LLM-as-a-Judge. To address these, we regard idea evaluation as a knowledge-grounded, multi-perspective reasoning problem and introduce InnoEval, a deep innovation evaluation framework designed to emulate human-level idea assessment. We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources. We further achieve review consensus with an innovation review board containing reviewers with distinct academic backgrounds, enabling a multi-dimensional decoupled evaluation across multiple metrics. We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval. Experiments demonstrate that InnoEval can consistently outperform baselines in point-wise, pair-wise, and group-wise evaluation tasks, exhibiting judgment patterns and consensus highly aligned with human experts.

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24.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.13602

EpiBench: Verifiable Evaluation of AI Agents on Epigenomics Analysis

作者:

Harihara Muralidharan↗Reema Baskar↗Soo Hee Lee↗Tim Proctor↗Kenny Workman↗

arXiv:2606.13602v1 Announce Type: new Abstract: We introduce EpiBench, a verifiable benchmark for short-horizon epigenomics analysis. EpiBench evaluates whether agents can make well-defined analysis decisions from realistic workflow states and return deterministically gradable answers. The benchmark includes 106 evaluations across CUT\&Tag/CUT\&RUN, ATAC-seq, ChIP-seq, and DNA methylation workflows. Across 5,088 valid trajectories from 16 model-harness pairs, no system passed a majority of attempts: GPT-5.5 / Pi led at 45.0\% (143/318 attempts; 95\% confidence interval (CI), 36.3–53.7), followed by GPT-5.5 / OpenAI Codex at 39.9\% (127/318 attempts; 95\% CI, 31.6–48.3). Claude Opus 4.8 Max / Pi and GPT-5.4 / Pi each passed 39.0\% (124/318 attempts; 95\% CI, 30.2–47.8 and 31.0–47.0, respectively). Performance varies across assay types, and many failed runs still contain parts of the correct answer. Agents often found the right files and computed useful intermediate results, but failed when the task required deeper, assay-specific scientific judgment.

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25.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16753

P3B3: A Multi-Turn Conversational Benchmark for Measuring European and Brazilian Portuguese Variety Bias in LLMs

作者:

Rafael Ferreira↗In\^es Vieira↗In\^es Calvo↗James Furtado↗Iago Paulo↗Diogo Tavares↗Diogo Gl\'oria-Silva↗David Semedo↗Jo\~ao Magalh\~aes↗

As Large Language Models (LLMs) become embedded in everyday communication, capturing regional linguistic variation is essential for reliable and equitable language use. In Portuguese, European (pt-PT) and Brazilian (pt-BR) varieties remain unevenly represented, with pt-BR dominating in data quantity, while LLM preference for Portuguese variants remains underexplored. To address this gap, we introduce P3B3, an expert-curated language variety agnostic benchmark of conversational prompts, along with an evaluation framework for measuring variety bias and controllability. Experiments on several models show that most LLMs exhibit a strong bias toward pt-BR, with variation in controllability across models. These results highlight the need for more balanced multilingual representation across language varieties.

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