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01.
arXiv (math.PR) 2026-06-16

High-Order Talagrand and Eldan–Gross Inequalities via Besov-Type Variance Functionals

作者:
Guangyue Han↗Peijie Li↗

arXiv:2606.14876v1 Announce Type: new Abstract: By introducing high-order Besov-type variance functionals that generalize the canonical variance, we develop a unified framework for proving high-order Talagrand-type inequalities that relate high-order energies to Fourier weights. Applying this machinery, we establish high-order Poincaré-type, $L^p$–$L^q$, isoperimetric-type, Falik–Samorodnitsky and Eldan–Gross inequalities, all with explicit constants, in both the Boolean and Gaussian settings. Fundamentally, our semigroup-based framework relies primarily on hypercontractivity and high-order Bismut-type derivative estimates, and is broadly applicable.

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02.
bioRxiv (Bioinfo) 2026-06-16

MetaPilot: genome-aware adaptive search-space refinement for unified DDA and DIA metaproteomics

作者:
Cheng↗Figeys↗

Metaproteomic peptide identification is constrained by the structure and size of the protein search space. Pooled gene catalogues provide coverage but obscure genome-level evidence, and current workflows for data-dependent (DDA) and data-independent (DIA) acquisition diverge in their database strategies. We present MetaPilot, a genome-aware workflow that uses conserved marker-protein evidence to rank candidate genomes from MGnify catalogues and construct adaptive, sample-specific search spaces. Applied to paired DDA/DIA datasets of defined mixtures and fecal samples, MetaPilot adapted genome selection to community complexity and reproduced published peptide evidence while expanding the detectable peptide space. In DDA-independent reanalysis of Orbitrap human gut DIA data, MetaPilot identified 24.4% more peptides than the published DDA-derived library and 2.06-fold more than the matched DDA-assisted DIA search. On timsTOF DIA-PASEF mouse intestinal data, it outperformed uMetaP by 41.8~119.7%, enabling genome-resolved functional interpretation without DDA-PASEF input.

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03.
arXiv (CS.CL) 2026-06-17

The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance

作者:
Csaba Kiss↗Roland Molontay↗Gabriele Pergola↗

Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance. The InferBERT framework integrates transformer models with Do-calculus, but its success hinges on the underlying classification model. This study evaluates the impact of model choice in InferBERT, assessing whether simpler models suffice, if domain-specific pre-training helps, whether scaling to LLMs improves causal detection, and the effect of post-hoc calibration. We performed a comparative study on two benchmarks: Analgesics-induced Acute Liver Failure (AILF) and Tramadol-related Mortalities (TRAM). Four models were evaluated-XGBoost (baseline), ALBERT (original InferBERT), BioBERT (biomedical transformer), and Med-LLaMA (medical LLM)-using 5-fold cross-validation repeated over 20 runs. We measured accuracy, Expected Calibration Error (ECE) pre- and post-isotonic regression, and Jaccard concordance of causal terms with PRR, ROR, and EBGM; significance was tested with paired t-tests. BioBERT achieved the highest accuracy on both datasets, while Med-LLaMA underperformed despite its size and parameter-efficient fine-tuning. Domain-specific pre-training was decisive. Calibration improved ECE but had mixed effects on accuracy and causal discovery. BioBERT's superiority also yielded the strongest concordance with traditional pharmacovigilance signals. These results show that domain-specific pre-training provides a clear advantage over simpler baselines and larger LLMs. Investing in manageable, domain-aware models is more effective for computational pharmacovigilance than simply scaling model size.

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04.
arXiv (CS.AI) 2026-06-12

PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization

作者:
Hao Jiang↗Xin Li↗Annan Wang↗Zhi Yang↗Haoxiang Zhang↗Yichi Zhang↗Weisi Lin↗

arXiv:2606.12942v1 Announce Type: new Abstract: Generative listwise ranking with Large Multimodal Models (LMMs) aims to capture global list context in a single forward pass, but its effectiveness degrades in long-context multimodal scenarios. We identify a recurring failure mode, parse collapse, where the autoregressive decoder produces fluent yet incomplete rankings by silently omitting candidates and terminating early. This failure stems from limited context utilization rather than simple formatting mistakes, making prompt engineering and constrained decoding insufficient. We propose PRISMR (Parameterized Representation Internalization for Semantic Multimodal Ranking), a framework that replaces transient in-context list processing with parametric structural conditioning. PRISMR uses a lightweight hypernetwork to encode multimodal candidates in parallel and generate item-specific LoRA weights, which are synthesized into an instance-specific adapter for a LMM. This paradigm enables more robust internalization of list structure while preserving the base model. We further introduce a large-scale multimodal review-ranking benchmark for evaluation. Experiments demonstrate that PRISMR substantially reduces parse collapse, improves listwise ranking performance, and transfers effectively across domains and instruction-tuned backbones.

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05.
arXiv (CS.AI) 2026-06-18

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction

作者:
Rui Huang↗Lican Huang↗

arXiv:2605.21528v2 Announce Type: replace-cross Abstract: Accurate disease risk prediction is challenged by heterogeneous features, limited data, and class imbalance. This study presents yvsoucom-iterkit, a deterministic AutoML framework that models pipeline optimization as a configuration-level system with full reproducibility and traceable execution logs, enabling systematic analysis of component attribution, interactions, similarity, and cross-seed robustness. Experiments on the Pima Indians Diabetes and Stroke datasets across more than 18,000 pipeline configurations reveal a structured yet partially redundant search space, where performance is dominated by a small subset of interacting components. Ensemble models achieve stable performance, reaching a Weighted-F1 of 0.89 on Pima and 0.94 on Stroke. Macro-F1 reaches approximately 0.88 on Pima but drops to 0.6560 on Stroke due to severe imbalance. Cross-seed experiments show that ensembles reduce variance compared to single models. Friedman testing ($p < 0.05$) confirms significant ranking differences across configurations. Based on analysis of component attribution, interaction, and similarity, optimal configuration design reveals dataset-dependent behavior. For the Pima dataset, computational efficiency benefits from simplified search spaces where redundant components can be removed, with split ratio playing a key role. In contrast, the Stroke dataset requires enhanced imbalance-aware strategies, where RandomOverSampler improves Macro-F1 from 0.6560 to 0.6766. These findings demonstrate that effective AutoML optimization is achieved through optimal configuration design, where carefully constraining the search space to high-impact components can improve performance, stability, and interpretability while reducing unnecessary search complexity.

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

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models

作者:
Dewi Gould↗Francis Rhys Ward↗Anders Cairns Woodruff↗Rauno Arike↗Josh Hills↗Alex Serrano↗Ida Caspary↗Jason Ross Brown↗Jo J. Jiao↗Patrick Leask↗Twm Stone↗Ram Potham↗…

arXiv:2606.07157v2 Announce Type: replace Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, and strategic reasoning. To compare models against humans, we estimate the $50\%$-task-completion time horizon (TH): the human time required for tasks a model completes with $50\%$ success rate. We complement this with a $50\%$ reasoning token horizon: the minimum number of o3-mini reasoning tokens needed for tasks a model solves with $50\%$ success rate. We find that the no-CoT $50\%$ TH of frontier models has been doubling roughly every year over the past six years, with GPT-5.5's TH reaching over 3 minutes and reasoning token horizon exceeding 1,500 tokens. Our median estimates predict that frontier no-CoT THs could exceed 7 minutes by 2028, and 25 minutes by 2030, though these projections carry substantial uncertainty. We recommend frontier developers track this explicitly.

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

PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty

作者:
Wenjing Tang↗Xuanjin Jin↗Yuan Liu↗Renming Huang↗Cewu Lu↗Panpan Cai↗

arXiv:2606.15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive. We introduce PO-PDDL, a symbolic formulation of POMDPs that preserves the relational structure and LLM-friendly syntax of the Planning Domain Definition Language (PDDL), while explicitly modeling partial observability, stochasticity, and beliefs. Building on this formulation, we propose a demonstration-driven pipeline for learning PO-PDDL models. The proposed method reconstructs latent symbolic state trajectories from real-robot execution videos, identifies partial observability via inconsistencies between inferred states and visual observations, and learns stochastic transition and observation models accordingly. The resulting PO-PDDL domains are reusable across tasks and enable online belief-space planning under both perception and execution uncertainty. Experiments on real-world long-horizon manipulation tasks show that our method consistently outperforms existing PDDL and POMDP model-learning approaches, achieving robust task planning under uncertainty with significantly lower planning cost.

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

When Multiple Scripts Matter: Evaluating ASR in Clinical Settings

作者:
Jean Seo↗Minkyu Kim↗Jeonguk Lee↗Jisoo Jung↗Wooseok Han↗Eunho Yang↗

Automatic speech recognition (ASR) in non-English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms. Conventional string-matching evaluation metrics often underestimate ASR performance by treating orthographic variants as errors. To address this issue, we introduce MultiClin, a clinical ASR benchmark designed to evaluate robustness to multiscript variability. Experiments across diverse ASR models show that multiscript-aware evaluation provides a fairer assessment of recognition quality than conventional single-reference evaluation. We further investigate the impact of script consistency during training and find that inconsistent script mappings increase orthographic uncertainty and hinder model convergence, with a balanced 50% mapping ratio producing the highest entropy. In contrast, script unification consistently yields the best ASR performance. Our dataset and code are publicly available at: https://github.com/aitrics-ronaldo/Interspeech_MultiClin.

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09.
arXiv (CS.AI) 2026-06-15

Distributional Biases in Post-Training: A Markovian Analysis of Reasoning Trajectories

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

arXiv:2511.07368v3 Announce Type: replace-cross Abstract: Foundation models exhibit broad knowledge but limited task-specific reasoning, motivating post-training strategies such as RL with verifiable rewards (RLVR) and test-time scaling (TTS). While recent work highlights the role of exploration in improving pass@K, empirical evidence points to a paradox: RLVR and ORM/PRM typically reinforce existing paths rather than expanding the reasoning scope, raising the question of why exploration helps if no new patterns emerge. To reconcile this paradox, we adopt the perspective of Kim et al. (2025), viewing easy (e.g., simplifying a fraction) versus hard (e.g., discovering the some symmetry) reasoning steps as low versus high probability Markov transitions. In this tractable model, pretraining corresponds to tree-graph discovering, while post-training corresponds to CoT reweighting. We provably show that, both RLVR and ORM/PRM would favor heavily to several high-probability paths, and thereby forget rare-but-crucial CoTs. Building on this, we further prove that exploration strategies such as rejecting easy instances and KL regularization help preserve rare CoTs. Empirical simulations corroborate our theoretical results.

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10.
arXiv (CS.CL) 2026-06-19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

作者:
Jinseok Chung↗Minkyoung Song↗Hyunji Jung↗Namhoon Lee↗

In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations. Uncertainty decomposition-separating aleatoric from epistemic sources-is particularly crucial in this setting, yet existing methods, designed for standard generation tasks, fail to capture the unique dynamics of ICL. To address this, we introduce a concept of self-function vectors, built upon Bayesian views and the mechanistic interpretability of ICL. These vectors leverage internal model representations to model the latent concept learned during in-context prompting, thereby enabling a direct estimation of aleatoric uncertainty within a Bayesian framework and circumventing the reliance on brittle input or decoding manipulations. Given the lack of established benchmarks and suitable evaluation protocols, we also propose the first and rigorous evaluation protocol, in which data is manipulated in controlled ways so as to quantify aleatoric uncertainty precisely and separately from epistemic uncertainty. With this new evaluation framework, initially grounded in synthetic tasks for conceptual development and subsequently extended to real-world datasets, we show that our proposed methodology can measure uncertainty of LLM predictions made under ICL more reliably than existing alternative methods. Moreover, we show it can be used as a practical tool for trustworthy-related applications, such as hallucination detection. Our findings pave a new direction for connecting the quantitative view of uncertainty with the mechanistic understanding of model behavior.

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

Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits

作者:
Lennert Saerens↗Bram Silue↗Eleni Litsa↗Peter Vrancx↗Pieter Libin↗

arXiv:2606.18785v1 Announce Type: cross Abstract: Identifying Pareto optimal solutions is critical to support multi-objective decision-making. We introduce the first anytime Multi-Objective Multi-Armed Bandit algorithm for the Pareto Set Identification problem, taking a Bayesian approach: Top-Two Pareto Front Thompson Sampling (TTPFTS). We benchmark TTPFTS against state-of-the-art fixed-budget Pareto Set Identification algorithms on synthetic environments. Next, we demonstrate its practical utility in a challenging multi-objective molecular discovery setting by efficiently exploring an ultra-large synthesis-on-demand molecular library. Furthermore, we introduce a novel uncertainty quantification metric that estimates our algorithm's confidence in the predicted Pareto set. We demonstrate that this metric effectively proxies true performance, yielding a robust methodology for monitoring learning progress in complex settings. Finally, we complement these empirical findings with a theoretical proof of the algorithm's asymptotic correctness.

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

Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting

作者:
Ye Xue↗Yiheng Wang↗Xinhua Shao↗Qi Yan↗Shutao Zhang↗Tsung-Hui Chang↗

arXiv:2606.18734v1 Announce Type: cross Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), which models the channel multipath angular power spectrum (APS) from the reference signal received power (RSRP) measurement, has emerged as a state-of-the-art method tailored for efficient network optimization. However, despite its effectiveness, LSCM cannot predict APS at the vast majority of locations where no measurements are available, which significantly restricts its applicability in large-scale, real-world scenarios. To address this challenge, we present point-cloud-assisted tangent Gaussian splatting (PC-TGS), the first framework to extrapolate APS to unmeasured outdoor grids by integrating sparse radio measurements with dense LiDAR-based geometry. PC-TGS represents environmental scatterers as anisotropic 3D Gaussians, initialized and refined through a relaxed-mean reparameterization of the raw point cloud. A tangent-plane projection accurately maps each Gaussian into the local angular domain, while a depth-aware electromagnetic splatting process aggregates their contributions. To ensure practical deployment, we derive a closed-form Gaussian-weighted average (GWA) for APS bin integration and provide a provable error bound. { Evaluations on a LiDAR-scanned city-scale dataset (5M points, 6,310 RSRP samples) demonstrate that PC-TGS achieves better APS and RSRP prediction performance compared to state-of-the-art baselines and faster inference time for APS extrapolation task. These results highlight the potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.

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13.
arXiv (CS.CV) 2026-06-18

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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14.
medRxiv (Medicine) 2026-06-15

Using wastewater surveillance to explore community-level dietary intake in sewered and non-sewered sanitation systems in Malawi, Africa

作者:
Holm↗R. H↗Chigwechokha↗Kaponda↗Stephens↗Limbong↗Ercumen↗Hart↗Workman↗C. L↗de los Reyes↗F. L↗…

Wastewater can be used to measure biomarkers that reflect population-level dietary intake and diversity; however, how this approach may apply in a low-income country remains a knowledge gap. This study aims to evaluate whether select dietary-related metabolites can be detected in wastewater and environmental surveillance (WES) samples from both sewered and non-sewered sanitation systems in Malawi, Africa. Fourteen WES samples were collected and analyzed from two university campuses in Mzuzu and Thyolo, Malawi. Four targets were analyzed: N-methyl-2-pyridone-5-carboxamide (2PY; a biomarker of vitamin B3), 4-pyridoxic acid (4-PA; a biomarker of vitamin B6), as well as enterodiol and enterolactone (biomarkers of dietary fiber and polyphenol consumption). An 18-question survey, paired spatiotemporally with the WES measurements, assessed self-reported daily dietary intake, food insecurity, and nutrient deficiency symptoms among 500 respondents. Among the 14 WES samples, 2PY, 4-PA, and enterolactone were detected, while enterodiol was not detected above the method limit (

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15.
arXiv (CS.AI) 2026-06-12

Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents

作者:
Kushal Raj Bhandari↗Ling Yue↗Ching-Yun Ko↗Dhaval Patel↗Shaowu Pan↗Pin-Yu Chen↗Jianxi Gao↗

arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small-corpus distillation. A few hundred teacher traces can teach workflow format, but rarely cover the recovery behavior needed to repair failed plans over changing tool catalogs. We introduce Evoflux, an inference-time evolutionary search method that treats compact tool use as the repair of executable tool workflows. It evolves typed workflow graphs through structured edits, execution feedback, adaptive intensity, meta-guided redesign, and diversity pruning. On held-out MCP-Bench tasks spanning live MCP servers and 250 tools, Evoflux raises execution feasibility from roughly 3% to 17-24% across small planners. In contrast, SFT and SFT+DPO on the same search-mined data match, underperform, or collapse below zero-shot performance; ReAct reaches higher peaks, but with higher variance and token cost. These results show that execution-grounded search is more reliable under scarce teacher-trace budgets.

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

Persona-Pruner: Sculpting Lightweight Models for Role-Playing

作者:
Jinsu Kim↗Jihoon Tack↗Noah Lee↗Jongheon Jeong↗

Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona. However, applying these capabilities to real-world applications (e.g., ecosystems with numerous NPCs interacting simultaneously) exposes a critical inefficiency due to the excessive computational cost. In this paper, we question the necessity of dedicating a full, generalist model to a single persona, hypothesizing that a specific character identity relies on only a fraction of the model's total capacity. We observe that naively pruning LMs often severely degrades the role-playing performance for a specific persona; it does not distinguish between redundant knowledge and essential character traits. We propose Persona-Pruner, a framework that sculpts a lightweight role-playing model by isolating persona-specific sub-networks from a single description. Our experiments consistently show that Persona-Pruner preserves role-playing performance substantially more effectively than existing state-of-the-art LLM pruning techniques, reducing the performance drop from the dense model by up to 93.8% over the strongest baseline on RoleBench in LLM-as-a-judge score, while still maintaining general LLM capabilities. Code is available at https://github.com/jsu-kim/Persona-Pruner.

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

Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems

作者:
Terence Zeng↗Abhishek K. Umrawal↗

arXiv:2606.13858v1 Announce Type: cross Abstract: Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with similar patterns, it performs poorly in domains where user-item interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Genre, instrumentation, and lyrics have been explored; however, relatively little attention has been given to emotion recognition. Since a user's emotional state strongly influences their music choice, incorporating mood signals offers a promising direction for personalization. In this work, we propose a mood-conditioned ranking framework that integrates user affective signals into the recommendation process via softmax-based sampling in the energy-valence space. We evaluate the approach via single-blind experiments in which participants compare recommendations from the proposed system against a baseline. The results indicate improved perceived recommendation quality, providing preliminary evidence for the effectiveness of incorporating mood-based inputs into music recommendations.

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

PT-WNO: Point Transformer with Wavelet Neural Operator for 3D Point Cloud Semantic Segmentation

作者:
Nhut Le↗Maryam Rahnemoonfar↗

Point cloud semantic segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure. Transformer-based networks have demonstrated strong performance by focusing on detailed local feature aggregation; however, global context is conveyed primarily through skip connections across encoder-decoder stages, which we argue is insufficient for full scene understanding. We hypothesize that augmenting skip connections with a learnable global feature extraction module allows the network to acquire scene-level knowledge before descending into local detail, leading to richer and more contextually grounded representations. To this end, we propose Point Transformer with Wavelet Neural Operato (PT-WNO), which integrates a shared Wavelet Neural Operator (WNO) branch alongside the skip connections of a point cloud transformer backbone. At each encoder-decoder transition, point features are projected onto a dense 3D volumetric grid where the WNO captures multi-scale global spectral context through learnable wavelet decomposition and reconstruction. These global features are fused back into the network via lightweight adapters, complementing rather than replacing the existing skip connections. Experiments on four large-scale 3D point cloud benchmarks demonstrate the effectiveness of PT-WNO. On S3DIS (Area 5), PT-WNO achieves 71.59% mIoU, outperforming the Point Transformer v3 (PTv3) baseline by +1.03 points. On DALES it achieves 81.05% mIoU (+1.47 over the baseline). On ScanNet~v2, PT-WNO obtains 76.19% mIoU, remaining competitive with the baseline (76.36%).

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20.
medRxiv (Medicine) 2026-06-16

High-Risk Anti-Seizure Medication Use in Childbearing-Age People with Epilepsy in a Taenia solium Endemic Region

作者:
Allen↗S. E↗Wardle↗M. T↗Moyano↗L. M↗Vilchez↗Bustos↗J. A↗Garcia↗H. H↗O'Neal↗…

Background: People of childbearing potential with epilepsy in regions endemic for Taenia solium, where neurocysticercosis (NCC) is highly prevalent, represent a vulnerable population due to the elevated burden of epilepsy and resource limitations. Clinical practice in these settings remains poorly characterized. This study characterized anti-seizure medication (ASM) prescribing patterns by medication risk profiles among people of childbearing potential with epilepsy in Northern Peru, a region highly endemic for T. solium. Methods: Participants were drawn from a prospective, population-based epilepsy cohort in Tumbes, Peru (2006 to 2020). The analytic population included females with epilepsy aged 15 to 49 years. The primary outcome was pregnancy-associated ASM risk of congenital malformations and adverse neurodevelopmental outcomes. ASMs were classified as ''Established Low Risk'' (lamotrigine, levetiracetam), ''Possible Risk/Inadequate Data'' (carbamazepine, phenobarbital, phenytoin), and ''Established High Risk'' (valproic acid). Prescription patterns were examined in relation to demographic and clinical characteristics. Results: Among 1,975 individuals with epilepsy, 685 were people of childbearing potential. Approximately 34.9% met criteria for probable or definite NCC. Most ASM prescriptions were in the ''Possible Risk/Inadequate Data'' category (87.0%), and 12.8% received ''Established High Risk'' medications. In multivariable analysis, high-risk prescribing was associated with prior ASM use and polytherapy. Discussion: People of childbearing potential with epilepsy were predominantly treated with carbamazepine, phenytoin, phenobarbital, and valproate, reflecting local ASM availability. Despite evidence supporting lamotrigine and levetiracetam in pregnancy, prescribing patterns reflect local formulary constraints. These findings highlight a gap between guideline recommendations and real-world prescribing in resource-limited settings, underscoring the need for context-specific treatment strategies.

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

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

作者:
Bochen Yang↗Lianlei Shan↗

arXiv:2606.17924v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding actions from vision-language backbone representations enables low-latency control, whereas explicit reasoning through textual chains, pixel-level subgoals, or action search can improve planning but incurs substantial latency and computational cost. We propose PearlVLA, a VLA framework that moves deliberation into the latent space of a vision-language model (VLM). PearlVLA separates VLM meta-query representations into a fixed visual grounding branch and an iterative latent plan branch. At each refinement round, a plan-conditioned world query probes a lightweight frozen latent world model for an action-free future observation latent, which is fed back to guide plan refinement. A future-guided RefineNet then applies scheduled residual updates to progressively refine a coarse semantic draft into a fine-grained latent action plan. The refined plan after K rounds is then decoded in parallel into an action chunk for low-latency execution. We further introduce Causal Refinement-Grouped Process-Reward RL to optimize the latent refinement process with rewards from longer-horizon imagined futures induced by latent plan edits. Empirical evaluations on the LIBERO benchmark demonstrate that PearlVLA achieves state-of-the-art performance among existing methods.

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22.
arXiv (CS.CV) 2026-06-18

ROSA-TFormer: A Radar-Optical Sensor-Aware Temporal Transformer for Pinus sylvestris Plantation Classification in Northern Shaanxi Using GEE-Derived Sentinel-1/2 Time Series

作者:
Nengbo Zhang↗Chang sheng↗

Accurate identification of Pinus sylvestris var. mongolica plantations is important for monitoring afforestation quality and ecological restoration in northern Shaanxi. This paper proposes ROSA-TFormer, a radar-optical sensor-aware temporal Transformer for P. sylvestris classification using Sentinel-1/2 time-series data generated on Google Earth Engine. The model integrates separate SAR and optical embedding branches, a sensor-aware gate, and temporal attention pooling to capture multi-source seasonal features. Experiments on monthly and half-month point-level datasets show that ROSA-TFormer achieves strong classification performance, with 99.67% overall accuracy, 99.56% macro F1, and 98.91% P. sylvestris F1 on the HalfMonth-dataBig dataset. Spatial block validation and ablation results further indicate the effectiveness of radar-optical temporal fusion and sensor-aware modeling. The results demonstrate the potential of ROSA-TFormer for point-level P. sylvestris plantation classification, while broader wall-to-wall validation remains necessary.

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23.
arXiv (quant-ph) 2026-06-19

Quantum Kernels are Spectral Tensor Networks

作者:
Erik M. {\AA}sgrim↗Stefano Markidis↗

arXiv:2606.20402v1 Announce Type: new Abstract: Quantum kernels admit Fourier representations whose frequencies are determined by the data-encoding gates of the underlying feature map. We show that entangling tensor kernels are matrix product operator factorizations of the corresponding Fourier coefficient tensors, thereby identifying quantum kernels as spectral tensor networks. By grouping gate-level frequency configurations that yield the same feature-wise frequency, we obtain a grouped Fourier form that induces a more compact spectral tensor network representation of the kernel. We further show that kernel target alignment serves as a bridge between the Fourier and tensor network views. On a grid that resolves the accessible Fourier modes, it becomes the Frobenius cosine similarity between Fourier coefficient tensors. Our numerical experiments show that layered quantum kernels admit accurate representations with small bond dimension, revealing a compressibility governed by correlations between Fourier modes. This compressibility provides a diagnostic of classical representability and of whether kernel evaluation is likely to remain classically tractable.

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24.
arXiv (CS.CV) 2026-06-19

HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining

作者:
Juncheng Ma↗Jianxin Bi↗Yufan Deng↗Xuanran Zhai↗Kewei Zhang↗Ye Huang↗Bo Liang↗Shukai Gong↗Jiankai Tu↗Xiaotian Tang↗Jiaxin Li↗Kaiqi Chen↗…

Embodied foundation models are expected to benefit from data scaling like large language models, but face a much tighter data bottleneck. Teleoperated real-robot trajectories remain the dominant pretraining source due to their precise action supervision and embodiment alignment, yet their scalability is limited by high collection cost, acquisition difficulty, and low behavioral and environmental diversity. These limitations have sparked interest in egocentric human video as a scalable, substantially lower-cost, and more diverse alternative for embodied model pretraining. However, its effectiveness compared to teleoperated real-robot data remains underexplored. To address this question, we conduct a systematic study comparing egocentric human video and teleoperated real-robot trajectories as pretraining data sources for embodied foundation models, under fixed post-training and validation protocols. Surprisingly, we find that egocentric data, when processed through a carefully designed filtering and labeling pipeline, is not merely a viable substitute for model pretraining but can lead to superior performance. With the same amount of pretraining data, models pretrained on egocentric data achieve a 24% lower validation loss on real-robot action prediction, as well as 52.5% and 90% higher success rates on in-distribution and out-of-distribution real-robot task execution, respectively. This finding verifies a scalable paradigm for embodied foundation models: pretrain on egocentric human video to learn diverse world representations, then adapt with a small amount of labeled real-robot data for action-space alignment. We hope this study encourages broader exploration of egocentric data and offers guidance for data quality assessment before costly robot data collection.

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25.
PLOS Computational Biology 2026-06-15

Environmental “knees” and “wiggles” as strong stabilizers of species’ range limits set by interspecific competition

作者:
Farshad Shirani↗

by Farshad Shirani, Benjamin G. Freeman Whether interspecific competition is a major contributing factor to setting species’ range limits has been debated for a long time. Theoretical studies have proposed that the interactions between interspecific competition and disruptive gene flow along an environmental gradient can halt range expansion of ecologically similar species where they meet. However, the stability of such range limits has not been well addressed. We use a deterministic mathematical model of adaptive range evolution over a continuous habitat to show that the range limits set by interspecific competition are unlikely to be evolutionarily stable if the environmental optima for fitness-related traits vary (almost) linearly in space. That is, in a linear environment without a dispersal barrier or a third (or more) species, the range borders formed between two competing species constantly move towards the weaker species. We demonstrate that environmental nonlinearities such as “knees” and “wiggles”—wherein an isolated sharp change or a step-like change occurs in the steepness of a trait optimum—can strongly stabilize competitively formed range limits. The stabilization mechanism relies on the contrast that such nonlinearities create in the level of disruptive gene flow to the peripheral population of each species, and succeeds when an additional process, such as Allee effects, prevents the establishment of an infinitesimal population in the presence of an abundant competitor. We show that the stability of the range limits at these nonlinearities is robust against moderate environmental disturbances. Whether strong disturbances such as rapid high-amplitude climate changes can destabilize such range limits depends on how the competitive dominance of the species changes across the nonlinearity. Therefore, our findings underscore the importance of assessing species’ competitive ability when predicting responses to climate change, and identify geographic regions where established range limits are likely to persist as well as regions where shifting limits may eventually stabilize.

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