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01.
medRxiv (Medicine) 2026-06-18

Artificial Intelligence-informed mobile behavioural interventions to support adolescents mental health in schools: protocol for a randomised controlled trial using the MindCraft app

Authors:
Freccero↗Elkes↗Kadirvelu↗Versi↗Faisal↗Dewa↗L. H↗Di Simplicio↗Nicholls↗

Background: Children and young people (CYP) are particularly affected by mental health problems. Mobile apps provide a scalable and accessible approach to adolescent mental health support, and schools are well-positioned to address multiple risk factors and deliver large-scale interventions. By combining active (self-reported) and passive (sensor-derived) data, mobile apps can model mental states and deliver context-aware support. Artificial Intelligence (AI) enables adaptive, context-aware recommendations tailored to each user. However, there is limited research on AI-based mental health interventions in community CYP. MindCraft is a mobile app designed to monitor adolescents mental health using active and passive data and provide AI-informed recommendations ("nudges"). This study aims to investigate the effectiveness of personalised AI nudges delivered through MindCraft on improving mental health outcomes among adolescents in schools in the United Kingdom. Methods: The study is a three-arm RCT using a prospective cohort of secondary school students aged 14-19. Following informed consent, participants complete a baseline online assessment at school and download MindCraft. The primary outcome is the Strengths and Difficulties Questionnaire global and subscale scores. Secondary outcomes include the Eating Disorders Diagnostic Scale, the Sleep Condition Indicator Questionnaire, the Self-Injurious Thoughts and Behaviours Interview, the Self-Efficacy Questionnaire for Children and the World Health Organisation-Five Well-Being Index. Participants are randomised to: (1) an AI-informed intervention group receiving personalised nudges, (2) an active control receiving non-personalised nudges, or (3) a control group with self-monitoring only. Participants use the app for four weeks, with follow-up at one month. Repeated-measures analyses will assess changes across time points. Discussion: We hypothesise that AI nudges will have a greater positive effect on mental health outcomes at one month than general nudges and self-monitoring. Our findings will provide key evidence on the effectiveness of personalised mobile AI recommendations for adolescents mental health and inform school-based mental health prevention and early intervention. This study will contribute evidence on the ethical, acceptable, and scalable integration of AI-enabled digital mental health tools within public health and educational systems, with implications for the design of future digital public health interventions and policies supporting their safe integration in schools.

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02.
arXiv (CS.AI) 2026-06-18

Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation

Authors:
Chenghao Xu↗

arXiv:2606.18828v1 Announce Type: cross Abstract: Traditional approaches place intelligence in the agent, whether as a learned policy or a search procedure. We instead place intelligence in the space itself: a scene induces a Riemannian metric on the configuration manifold, and action reduces to following the geodesics of that metric rather than invoking a separate planner or collision checker. A single Encoder-Router network realizes this idea through three complementary parameter groups – frame parameters that orient the generators, modulation parameters that govern their spatial propagation, and basic coefficients that determine their strength. These groups combine through a shared semigroup-superposition mechanism to produce a single Riemannian metric field, yielding a compact architecture whose geometry scales naturally with scene complexity. Trained on a single two-obstacle scene, the model demonstrates robust zero-shot generalization across unseen obstacle configurations, with orders-of-magnitude separation between collision-free and obstacle-penetrating path costs.

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03.
medRxiv (Medicine) 2026-06-22

Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis

Authors:
Romero Mila↗Hoang↗N. P↗Pinto-Orellana↗Daida↗Kanai↗Kuroda↗Hussain↗S. A↗Shrey↗D. W↗Asano↗…

Objective: EEG biomarkers for seizure-generating tissue have historically been identified visually, which lacks objectivity and limits utility of automated approaches. For example, high frequency oscillations and interictal epileptiform discharges were promising markers to improve surgical outcomes for refractory epilepsy, but low specificity has hindered clinical implementation, and automated algorithms have not improved this. Methods: We developed Intracranial EEG Pattern Identification and Categorization, an automated, data-driven time-frequency framework for EEG biomarker discovery. It detects transient high-power intracranial EEG waveforms (1-500 Hz) and characterizes them using eight features. In seizure-free patients, waveforms occurring predominantly in resected intracranial EEG channels are candidate biomarkers. Results: In retrospective data from 14 seizure-free post-surgical patients from University of California, Los Angeles, we identified 9 waveform categories strongly associated with resected intracranial EEG channels. These included beta, gamma, and ripple band bursts, sometimes co-occurring with interictal epileptiform discharges; however, many were visually imperceptible in the broadband EEG. Using a support vector machine, we generated a unified classification metric based on these waveforms and tested it on 87 seizure-free subjects from Detroit Medical Center. This metric achieved higher area under the precision-recall curve than six state-of-the-art benchmark algorithms (p

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04.
Nature (Science) 2026-06-10

Human migration has surged since 2000 — these maps reveal where people are going

Authors:
Miryam Naddaf↗

Modelling with artificial-intelligence tools has filled gaps in migration data, revealing detailed global population movements from 1990 to 2023. Modelling with artificial-intelligence tools has filled gaps in migration data, revealing detailed global population movements from 1990 to 2023.

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05.
arXiv (CS.CL) 2026-06-19

Source-Grounded Data Generation for Text-to-JSON Learning

Authors:
Sunghee Ahn↗Guijin Son↗Youngjae Yu↗

From financial filings to clinical records, legacy industries rely heavily on long, unstructured documents to store high-value information. Reliably extracting this information into structured, machine-readable representations is a key prerequisite to making the contents accessible to automated systems. JSON is a natural target for such structured extraction, yet constructing reliable and scalable text-to-JSON training data remains challenging. To address this gap, we propose STAGE (Spreadsheet-grounded Text-to-JSON Artifact GEneration), a source-grounded data generation pipeline that constructs reports and JSON schema by using LLMs for scalable synthesis while validating ground-truth values against the underlying spreadsheet. Evaluations on STAGE-Eval, our source-grounded benchmark with an 851-example test set, show that STAGE produces stronger training data than existing approaches. This improves Qwen3-4B exact match from 31.37% to 74.27% and value accuracy from 45.46% to 90.69%.

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

Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS

Authors:
Siyuan Dai↗Yang Du↗Kun Zhao↗Zhusuyi Chen↗Heng Huang↗Paul Thompson↗Chao Shi↗Haoteng Tang↗Liang Zhan↗

arXiv:2606.18287v1 Announce Type: new Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks. However, demographic factors such as age and sex systematically confound the relationship between brain connectivity and clinical outcomes, causing GNNs to exploit spurious shortcuts rather than learning causally invariant representations. While recent causal GNN methods introduce causality at the graph-modeling level, their causal mechanisms remain domain-agnostic without accounting for the real-world confounders inherent in clinical neuroimaging data. Moreover, brain networks are constructed from atlas-based parcellations where each region exhibits distinct sensitivity to demographic factors, necessitating region-aware adjustment. We propose Artemis, a region-level causal framework that bridges this gap with causal intervention at each brain region independently by learning region-specific confounder representations with lightweight parameters. Our adjustment comprehensively utilized the multimodal functional and structural features for graph reasoning as a plug-in module compatible with arbitrary GNN backbones. Experiments on three benchmarks, ADNI for disease diagnosis, OASIS for dementia staging, and HCP for sex classification, demonstrate consistent improvements over representative GNN-based baselines. Multiple supporting experiments further demonstrate statistical significance and neuroscientific interpretability.

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

WavSLM: Single-Stream Speech Language Modeling via WavLM Distillation

Authors:
Luca Della Libera↗Cem Subakan↗Mirco Ravanelli↗

Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the entanglement of semantic and acoustic information. Most existing speech language models rely on text supervision, hierarchical token streams, or complex hybrid architectures, departing from the single-stream generative pretraining paradigm that has proven effective in text. In this work, we introduce WavSLM, a speech language model trained by quantizing and distilling self-supervised WavLM representations into a single codebook and optimizing an autoregressive next-chunk prediction objective. WavSLM jointly models semantic and acoustic information within a single token stream without text supervision or text pretraining. Despite its simplicity, it achieves competitive performance on consistency benchmarks and speech generation while using fewer parameters, less training data, and supporting streaming inference.

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08.
arXiv (CS.AI) 2026-06-11

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

Authors:
Ahasan Kabir↗Jiaqi Xue↗Mengxin Zheng↗Qian Lou↗

arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep request queues while equally capable alternatives sit idle. In multi-agent pipelines, where each query triggers multiple sequential model calls, these delays then compound across every downstream step. Closing this gap is challenging because the relevant infrastructure signals (queue depths, KV-cache pressure, latencies) are dynamic and noisy, and they must drive three different decisions: planning, per-step routing, and scheduling. We introduce INFRAMIND, a framework that makes the entire multi-agent stack infrastructure-aware. An infra-aware planner conditions topology and role selection on real-time system load and remaining budget, biasing toward simpler graphs under congestion and richer ones at low load. An infra-aware executor then observes per-model queue depths, cache utilization, and response latencies at each agent step to decide which model to call and how deeply to reason; a budget-aware scheduler further reorders each model's queue so that urgent requests are served first. Cast as a hierarchical constrained MDP and solved end-to-end via reinforcement learning, the system learns to balance quality against latency automatically. Across five benchmarks, INFRAMIND delivers up to +7.6 pp accuracy over the prior baseline at low load with up to 7x lower latency, and sustains up to 99.9% SLO compliance under high load where every baseline drops below 50%.

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09.
arXiv (CS.LG) 2026-06-17

Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds

Authors:
Hao Liang↗Cheng Tang↗Yunzong Xu↗

arXiv:2606.18218v1 Announce Type: cross Abstract: We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained service resources. Arrivals may be dependent, time-varying, and adapted to the past; the standing load condition is uniform interior slack, meaning the conditional mean arrival vector stays in a fixed contraction of the capacity region. We show that this slack reshapes the finite-time peak law for drift-minimizing scheduling policies such as MaxWeight. The square-root envelope that is sharp without slack persists only up to a geometry-dependent threshold; beyond that threshold, the running maximum grows only logarithmically with the horizon, both with high probability and in expectation. The mechanism is self-normalization: in the current queue direction, the projected fluctuation scale is normalized by the stabilizing drift scale. This removes capacity geometry from the logarithmic coefficient, while geometry remains in the threshold. Matching lower bounds show that both the logarithmic term and a geometric threshold are unavoidable. When finite-time state-space collapse is available, the threshold can be sharpened using local bottleneck geometry. For generalized input-queued switches, we obtain finite-time peak bounds with tight logarithmic coefficients. Simulations illustrate the two-phase envelope, local geometric refinements, and variance-sensitive improvements predicted by the theory.

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10.
arXiv (CS.CL) 2026-06-17

Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models

Authors:
Keyan Guo↗Ayush Utkarsh↗Wenbo Ding↗Isabelle Ondracek↗Ziming Zhao↗Guo Freeman↗Nishant Vishwamitra↗Hongxin Hu↗

Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment. However, they pose a heightened risk of exposure to explicit content, raising growing concerns for the online safety of children and adolescents. Despite these concerns, few studies have addressed the issue of illicit image-based promotions of unsafe UGCGs on social media, which can inadvertently attract young users. This challenge arises from the difficulty of obtaining comprehensive training data for UGCG images and the unique nature of these images, which differ from traditional unsafe content. In this work, we take the first step towards studying the threat of illicit promotions of unsafe UGCGs. We collect a real-world dataset comprising 2,924 images that display diverse sexually explicit and violent content used to promote UGCGs by their game creators. Our in-depth studies reveal a new understanding of this problem and the urgent need for automatically flagging illicit UGCG promotions. We additionally create a cutting-edge system, UGCG-Guard, designed to aid social media platforms in effectively identifying images used for illicit UGCG promotions. This system leverages recently introduced large vision-language models (VLMs) and employs a novel conditional prompting strategy for zero-shot domain adaptation, along with chain-of-thought (CoT) reasoning for contextual identification. UGCG-Guard achieves outstanding results, with an accuracy rate of 94% in detecting these images used for the illicit promotion of such games in real-world scenarios.

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11.
arXiv (CS.CV) 2026-06-16

MotionVLA: Vision-Language-Action Model for Humanoid Motion

Authors:
Nonghai Zhang↗Siyu Zhai↗Yanjun Li↗Zeyu Zhang↗Zhihan Yin↗Yandong Guo↗Boxin Shi↗Hao Tang↗

Generating realistic humanoid motion from scene images and text involves both low-frequency pose semantics and high-frequency physical dynamics. However, many existing methods tokenize motion with a single shared codebook, forcing heterogeneous motion signals into the same quantization space. Our frequency-domain analysis of human motion data reveals a clear mismatch between single-codebook quantization and motion statistics: five DCT coefficients capture 93% of joint-position energy but only 37% of joint-velocity energy, which can bias quantization toward pose statistics and under-represent high-frequency velocity components. A second challenge lies in adapting a standard autoregressive model to effectively model high-frequency physical signals in motion sequences. Therefore, we propose DSFT, a dual-stream frequency tokenizer that separates motion into Base and physical streams and compresses them independently with DCT truncation and BPE. Furthermore, we present MotionVLA, a Qwen3.5-based model that arranges Base and physical tokens in a unified sequence, where Phys tokens are predicted after Base tokens. Experiments on HumanML3D and MBench show that, despite using a lightweight 2B backbone, MotionVLA reduces the Diversity gap to real data by over 50% on HumanML3D and improves Motion-Condition Consistency by 3.8% on MBench, supporting frequency-aware dual-stream decoupling as an effective formulation for autoregressive motion generation. Code: https://github.com/AIGeeksGroup/MotionVLA. Website: https://aigeeksgroup.github.io/MotionVLA.

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

PRInTS: Reward Modeling for Long-Horizon Information Seeking

Authors:
Jaewoo Lee↗Archiki Prasad↗Justin Chih-Yao Chen↗Zaid Khan↗Elias Stengel-Eskin↗Mohit Bansal↗

Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step information-seeking tasks remain challenging for agents backed by language models. While process reward models (PRMs) can guide agents by ranking candidate steps at test-time, existing PRMs - designed for short reasoning with binary judgment - cannot capture richer dimensions of information-seeking steps, such as tool interactions and reasoning over tool outputs, nor handle the rapidly growing context in long-horizon tasks. To address these limitations, we introduce PRInTS, a generative PRM trained with dual capabilities: (1) dense scoring based on the PRM's reasoning across multiple dimensions of step quality (e.g., interpretation of tool outputs, tool call informativeness) and (2) trajectory summarization that compresses the growing context while preserving essential information for step evaluation. Extensive evaluations across FRAMES, GAIA (levels 1-3), and WebWalkerQA (easy-hard) benchmarks on multiple models reveal that best-of-n sampling with PRInTS enhances information-seeking in open-source models as well as specialized agents, matching or surpassing frontier models with a much smaller backbone agent and outperforming other strong reward modeling baselines.

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13.
bioRxiv (Bioinfo) 2026-06-16

Accelerating String Comparison in RLZ Compressed Sequences via LCE Jumps

Authors:
Varki↗Boucher↗

Relative Lempel-Ziv (RLZ) is an effective compression method for large, repetitive collections; however, the fundamental primitives required to elevate it from a passive archival format to a tractable representation for compressed construction have yet to be fully established. In this paper, we introduce an algorithmic framework for structurally comparing and lexicographically sorting sequences of RLZ factors. We characterize when direct factor comparisons are necessary and when they can be bypassed using RLZ specific shortcuts. We further introduce a method for extending truncated factors into right-maximal matches, enabling the recovery of matching statistics from the RLZ parse. Experimentally, RLZ sorting achieved speedups of up to 3.93x over character-based sorting. Together, these results advance the use of the RLZ format as a foundation for compressed construction.

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14.
bioRxiv (Bioinfo) 2026-06-12

ProMiSE: Protein Multi-State Evaluation Benchmark in Biological Contexts

Authors:
Ku↗Kim↗Park↗Seok↗

Proteins are inherently dynamic, with biological functions often emerging from transitions between multiple conformational states. While recent breakthroughs have largely addressed the static structure prediction problem, no systematic benchmark exists to demonstrate how well current models capture functionally relevant dynamics. We introduce ProMiSE, the first benchmark that provides both a dataset and an evaluation scheme, based on native biological assemblies and integrating major conformational change mechanisms - intrinsic, ligand-induced, and protein-induced - within a single curated dataset. We conducted a comprehensive evaluation of state-of-the-art structure prediction models, including AlphaFold3 and recent generative approaches. Our findings reveal that current models exhibit a limited ability to sample intrinsic multi-states and are often insensitive to biological context in induced scenarios. Internal representation analysis suggests that training-data exposure can shift predictions toward dominant conformational states over alternative biologically relevant states, primarily at the structure module. In contrast, results from BioEmu indicate that reducing decoding-stage bias can substantially improve multi-state sampling without major changes to upstream pair representations.

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15.
arXiv (CS.AI) 2026-06-11

CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning

Authors:
Jinshi Liu↗Lei He↗Pan Liu↗

arXiv:2601.11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance. We propose CoVar, a confidence–variance framework that assesses pseudo-label reliability by jointly modeling Maximum Confidence (MC) and Residual-Class Variance (RCV). Starting from entropy minimization, we derive a second-order cross-entropy approximation showing that low-loss pseudo-labels are favored when MC is high and RCV is low, with a confidence-dependent penalty that becomes stronger for near-certain predictions. Based on this criterion, CoVar embeds predictions into a two-dimensional confidence–variance space and uses SVD-based spectral relaxation to separate reliable and unreliable predictions without hand-tuned confidence thresholds. Cluster-wise Gaussian weighting then converts this separation into per-sample training weights. The resulting weights can be integrated into existing semi-supervised segmentation and classification pipelines during training and introduce no inference-time overhead. Experiments on PASCAL VOC 2012, Cityscapes, CIFAR-10, CIFAR-100, SVHN, and STL-10 show clear gains on VOC and Cityscapes under matched backbones, as well as competitive or improved error rates on standard classification benchmarks. These results indicate that residual-class dispersion provides a useful signal complementary to confidence for robust pseudo-label selection.

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

SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines

Authors:
Olga Ovcharenko↗Luciano Duarte↗Sebastian Schelter↗

arXiv:2606.14361v1 Announce Type: new Abstract: Machine learning (ML) pipelines require extensive data preparation, feature engineering, and integration across heterogeneous sources, making them tedious and error-prone to develop. While large language models (LLMs) have recently shown promise for assisting programming tasks, chat-based interfaces provide limited control over pipeline behavior and often produce code that is difficult to optimize or integrate into production systems. We demonstrate SemPipes, a novel programming model that extends ML pipelines with declarative, LLM-powered semantic data operators. SemPipes allows developers to specify high-level natural language instructions for data-centric operations, while seamlessly combining these operators with arbitrary Python code from standard data science libraries. For the semantic operators, it synthesizes specialized implementations at pipeline training time, conditioned on dataset characteristics and pipeline context, enabling the flexible yet controlled integration of LLM capabilities. We demonstrate SemPipes through SemPiper, an interactive interface that visualizes computational graphs of the pipelines, synthesized operator implementations, and optimization trajectories produced by an evolutionary search procedure. Attendees can explore three end-to-end scenarios, modify pipelines, inspect generated code, and observe how semantic operators are synthesized and iteratively optimized. The demonstration highlights how declarative semantic operators enable controllable, optimizable, and practical integration of LLMs into ML pipeline development.

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

Systematic Evaluation of Novel View Synthesis for Video Place Recognition

Authors:
Muhammad Zawad Mahmud↗Samiha Islam↗Damian Lyons↗

The generation of synthetic novel views has the potential to positively impact robot navigation in several ways. In image-based navigation, a novel overhead view generated from a scene taken by a ground robot could be used to guide an aerial robot to that location. In Video Place Recognition (VPR), novel views of ground locations from the air can be added that enable a UAV to identify places seen by the ground robot, and similarly, overhead views can be used to generate novel ground views. This paper presents a systematic evaluation of synthetic novel views in VPR using five public VPR image databases and seven typical image similarity methods. We show that for small synthetic additions, novel views improve VPR recognition statistics. We find that for larger additions, the magnitude of viewpoint change is less important than the number of views added and the type of imagery in the dataset.

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

Learning What to Remember: A Cognitively Grounded Multi-Factor Value Model for Agentic Memory

Authors:
Zhibao Chen↗Qian Cheng↗

arXiv:2606.12945v1 Announce Type: new Abstract: Long-running LLM agents accumulate interaction histories far larger than any context window, forcing a standing decision: what to encode deeply, what to forget, and what to retrieve under a fixed memory budget. Production systems answer with semantic similarity or recency – both mis-specified for the forgetting decision, which is made at consolidation time before the future query is known. We propose a multi-factor memory value function V(m)=\sum_i w_i f_i(m) over seven interpretable factors (emotional intensity, goal relevance, value alignment, self/user relevance, task utility, reliability, and usage history) drawn from cognitive psychology, whose weights are learned from a downstream objective by a gradient-free optimiser, and whose single scalar uniformly controls encoding depth, forget risk, and retrieval rank. We make a methodological point: on LongMemEval, scoring goal relevance against the held-out evaluation question saturates gold-evidence retention at \approx 0.98 – this measures retrieval, not forgetting. In the realistic blind regime, a learned multi-factor value retains 0.770 \pm 0.011 of gold evidence across 479 usable cases, versus 0.657 for uniform weights, 0.518 for the best single factor, and 0.368 for recency; every paired gap's 95% bootstrap CI is above zero, and a neural network over the same factors ties the linear model. The learned weights are interpretable – reliability, emotional intensity, and self/user relevance dominate, while query-time goal similarity is correctly down-weighted for the forgetting decision. A controlled synthetic task with planted confounds confirms the learner recovers a separating weighting (1.00 retention) where uniform weighting fails (0.62). The substrate is open-source; all experiments run on a single CPU with no API calls.

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19.
arXiv (CS.CL) 2026-06-17

Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation

Authors:
Reihaneh Amooie↗Yun Hao↗Wietse de Vries↗Jelske Dijkstra↗Matt Coler↗Martijn Wieling↗

This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographically diverse language pairs, covering a range of language families and writing systems. To distinguish the two languages, during training, we pre-pend each input text with a language identification token. At inference, the model jointly predicts both the language and transcription from the speech input alone. As texts for which the language is incorrectly determined show low ASR performance, we also conduct a follow-up experiment in which the language identification token is provided both during training and inference. Our results show that bilingual fine-tuning can be beneficial when language identification accuracy is high, and that in cases where language identification performance is low, including the language identification token at inference helps to improve ASR performance.

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20.
arXiv (CS.AI) 2026-06-17

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization

Authors:
Jianwei Zhang↗Xinyu Nie↗Jiaxin Yue↗Yonggang Shi↗

arXiv:2606.17420v1 Announce Type: cross Abstract: Tau PET imaging is central to tracking Alzheimer's disease progression, but systematic differences between scanners, protocols, and radiotracers across sites introduce nonbiological variability that inflates biomarker variance, reduces sensitivity to disease effects, and can bias downstream clinical assessments. Harmonization methods aim to remove these site-induced shifts while preserving biologically meaningful signal, yet existing approaches struggle when source and target cohorts differ in subgroup composition, risking conflation of site effects with biological variation such as tau-positivity status. We propose the Feynman Kac Reweighted Schröodinger Bridge Matching (FKRSBM) model to address this problem. Rather than routing data through a Gaussian noise prior as in diffusion-based methods, FKRSBM learns a direct stochastic transport process between source and target distributions via entropy-regularized optimal transport. To enforce biologically consistent transport, FKRSBM incorporates a subgroup-aware endpoint proposal derived from a Feynman Kac reweighting of the reference bridge measure, implemented entirely through stratified importance sampling at the data level and requiring no changes to the underlying bridge-matching solver or network architecture. For surface-based neuroimaging, FKRSBM employs a spherical convolutional backbone operating on cortical meshes to perform vertex-level harmonization. We evaluate the method on tau PET SUVR maps, harmonizing PI-2620 data from the HABS-HD cohort into the AV-1451 domain of ADNI. Compared against ComBat, CycleGAN, a diffusion-based method (DF), and unregularized Diffusion Schröodinger Bridge Matching (DSBM), FKRSBM achieves superior distributional alignment, reduced tau-positivity sign mismatch, stronger APOE subgroup alignment, and improved downstream disease classification performance.

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21.
arXiv (CS.AI) 2026-06-18

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Authors:
Yuxuan Liu↗Zhaochen Su↗Lingyun Xie↗Yuhao Zhang↗Qing Zong↗Jiahe Guo↗Zhongwei Xie↗Yiyan Ji↗Yauwai Yim↗Hongyu Luo↗Xiyu Ren↗Ruan Chenyu↗…

arXiv:2606.01139v3 Announce Type: replace Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates, it retains the first verifier-passing skill within the revision budget and falls back to empirical utility only when no candidate succeeds. Evaluated across three benchmarks and five LLMs, SkillRevise substantially outperforms one-shot baselines, improving the base agent's success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor.

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22.
arXiv (quant-ph) 2026-06-17

Cavity-enhanced superconducting response in an underdoped cuprate

Authors:
Angela Montanaro↗Vadim Plastovets↗Nitesh Khatiwada↗Jacopo Fiore↗Giacomo Jarc↗Abdullah Alabbadi↗Antonio Mastropasqua↗Enrico Maria Rigoni↗Shahla Y. Mathengattil↗Simone Dal Zilio↗Francesca Fassioli Olsen↗Fabio Novelli↗…

arXiv:2606.18084v1 Announce Type: cross Abstract: Superconductors carry electrical current without resistance when paired electrons condense into a coherent macroscopic quantum state. In underdoped cuprates, evidence suggests that pairing-related correlations and superconducting fluctuations can survive above the temperature at which global coherence is lost, pointing to phase fluctuations as a key limitation on superconductivity in this regime. Motivated by recent demonstrations of cavity-modified collective states in quantum materials, we investigate whether superconducting coherence can be stabilized by engineering the electromagnetic environment of the superconductor. We study an underdoped YBa$_2$Cu$_3$O$_{7-\delta}$ thin film in a tunable terahertz cavity formed with a semi-transparent gold mirror. From temperature-dependent terahertz transmission measurements, we find that the cavity enhances the superconducting response below the critical temperature, with an increase of the inferred superfluid weight. The effect becomes more pronounced at smaller cavity lengths and is accompanied by an upward shift of the superconducting onset temperature. Calculations based on a cavity-coupled model for phase-fluctuating superconductors capture these trends and support an interpretation in terms of cavity-enhanced phase stiffness. These results showcase the potential of cavity engineering for designing emergent functionalities in correlated systems.

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23.
arXiv (quant-ph) 2026-06-15

Tensor network manifolds and Riemannian fundamental theorem for tensor networks

Authors:
Pablo P\'aez-Velasco↗

arXiv:2606.14613v1 Announce Type: cross Abstract: Tensor networks provide a powerful framework for efficiently representing high-dimensional data and many-body quantum states. Endowing tensor networks with a Riemannian manifold structure provides a natural setting for numerical optimization and analysis. A central feature of tensor networks is their gauge freedom, whose characterisation (captured by so-called fundamental theorems) underlies both their intrinsic structure and the design of numerical algorithms. In this work, we study the interaction between the Riemannian manifold structure and the gauge freedom for several families of tensor networks. Using group actions and Riemannian submersions, we establish a Riemannian fundamental theorem for the tensor network families studied.

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24.
Nature (Science) 2026-06-10

Mitochondria tethered to the nucleus secure its energy supply

Authors:
Philippa Clarke↗

Direct interactions between the cell’s powerhouses and nuclear pores might channel energy straight into the nucleus, fuelling cell division and differentiation. Direct interactions between the cell’s powerhouses and nuclear pores might channel energy straight into the nucleus, fuelling cell division and differentiation.

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25.
arXiv (CS.LG) 2026-06-12

Self-Guidance: Enhancing Neural Codecs via Decoder Manifold Alignment

Authors:
Xiang Li↗Yixuan Zhou↗Jingran Xie↗Zhiyong Wu↗Hui Wang↗

arXiv:2606.12940v1 Announce Type: cross Abstract: Neural speech codecs based on Vector-Quantized VAEs (VQ-VAEs) are core audio tokenizers for speech LLMs, yet their reconstruction fidelity is bottlenecked by quantization error. Modifying the quantizer or increasing model capacity are common fixes, but they complicate downstream language modeling. Our core idea is to align the decoder's internal feature manifolds when processing both the quantized tokens and their original continuous embeddings, using a lightweight feature-mapping loss. This requires minimal training overhead and no inference-time changes. Applied to XCodec2, self-guidance improves all reconstruction metrics, achieving state-of-the-art low-bitrate performance. Notably, it enables a 4x codebook reduction without fidelity loss, which downstream TTS experiments show significantly improves LLM-based synthesis by simplifying the token modeling space. Multiple statistical observations and visualizations corroborate the enhanced internal manifold alignment in the decoder. Extensive experiments confirm its generality across various inductive biases. Self-guidance thus establishes an efficient, broadly applicable method for high-fidelity neural audio coding.

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