arXiv subjects

Meng Wang

Publications and source records attributed to Meng Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Prediction bias in biological ageing markers

Biological ageing markers have attracted growing interest, with models estimating age from organ imaging or blood biomarkers. An estimated age above chronological age or the age-specific population expectation is assumed to reflect accelerated ageing and poorer health. Previous research has supported this assumption through positive associations between disease and age gaps or acceleration. However, in this study, we identified a widespread health-dependent prediction bias in ageing markers that affects their key interpretation and application. Specifically, we investigated five ageing markers derived from retinal images, brain MRI, chest radiographs, abdominal CT, and blood tests, and evaluated them using association analyses. We observed the well-recognised phenomenon of regression to the mean (RTM) in the four organ-image based markers, whereby estimated ages were shifted towards the mean age of the training cohort. More importantly, we revealed that the strength of RTM varied with health status, with stronger RTM in unhealthy than in healthy individuals. This differential RTM introduced a health-dependent prediction bias that persisted after calibration and systematically altered associations across age subgroups, suggesting that whole cohort associations may not reflect those observed within individual age subgroups. Additionally, we showed that the tested ageing markers, including PhenoAge derived from blood biomarkers, had limited ability to distinguish health status at the individual level. These findings call for careful interpretation of biological ageing markers and their use in clinical studies, and highlight the need for further development and validation before these ageing markers can reliably inform individual health assessments.

q-bio.QM↗

arXiv2026

Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust

World models enable agents to learn and plan in imagination, but predictions beyond their experience can become unreliable and mislead decisions. Existing uncertainty estimates derived from predictions can remain overconfident on unfamiliar state-action pairs. We propose the Lucid World Model (LucidWM), which learns doubt from experience and propagates trust through imagination. By integrating Subjective Logic into categorical latent transitions, LucidWM distinguishes predicted outcomes from their evidential support and assigns each transition a degree of doubt. The complement of this doubt defines transition-level trust, which accumulates multiplicatively along imagined trajectories to reweight returns for policy learning and guide action selection. Uncertainty estimation requires no additional parameters or forward passes. Evaluated on four base world models against seventeen uncertainty readouts, LucidWM detects environmental changes and signals uncertainty during action-corrupted rollouts. In a controlled navigation case study, acting on trust reduces the number of steps required to reach the goal from 362 to 190. Fifteen demonstration videos show how LucidWM doubts its dreams and acts on that doubt. Videos are available at https://lucidwm.github.io.

cs.LG↗

arXiv2026

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the distinct forecasting roles of endogenous and exogenous modalities. In this work, we propose QiYao-M, a role-aware multimodal TSFM that models the two types of modalities separately. For endogenous modalities, to capture how they evolve along with the underlying temporal dynamics, we introduce an Endo-Multimodal Predictor and Endo-Multimodal Supervision to explicitly learn their evolution from history to the future. For exogenous modalities, to generalize across domains and across various modality types and numbers under the scarcity of exo-multimodal pretraining data, we propose an Exo-Multimodal Retrieval Enhancer that enables rapid downstream adaptation without updating the TSFM parameters. We further introduce Endo-Modality Proxy Training to train this retrieval module without exogenous multimodal pretraining data. Extensive experiments across unimodal and multimodal benchmarks demonstrate strong forecasting performance in scenarios both with and without exogenous modalities.

cs.LG↗

arXiv2026

EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold

Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use incoming observations as context but keep their parameters fixed, leaving conversational patterns unused as a learning signal. We introduce EvolvingAvatar, a causal generator that uses test-time training to adapt to user face video and dyadic audio during interaction. Its dyadic context prediction objective provides a self-supervised learning signal from audiovisual context without target motion labels at test time. Persistent fast weights accumulate these updates within each conversation to guide motion generation, while transient jaw adaptation responds to current audiovisual context. Predicted speech activity controls how persistent adaptation guides motion. We also introduce InterHead-Bench, a unified 455.95-hour benchmark built from single-view and dual-view conversation videos. Experiments show improved conversational motion statistics over strong baselines. On the hardest out-of-distribution split, generation improves as conversations unfold, reducing mismatch with recorded user-avatar expression statistics by up to 11.1% from the first interval.

cs.CV↗

arXiv2026

Aerial GRIPPER: A Gradient-based Real-time Inverse-game Predictor and Planner

Accurate capture of non-cooperative targets is critical. In an attempt to tackle this intractable challenge, an aerial gripper system integrated with a Gradient-based Real-time Inverse-game Predictor and PlannER (GRIPPER) framework is proposed. The interaction is formulated as a general-sum pursuit-evasion game under incomplete information. Specifically, underlying cost parameters of the target are inferred online, and the open-loop Nash equilibrium (OLNE) strategy is iteratively refined within a receding-horizon loop. To ensure high-frequency execution, a computationally friendly gradient-based inverse-game solver is developed. Without explicit computation of the Hessian inverse, the optimized solution is updated (> 50 Hz) based on implicit differentiation and fast Hessian-vector products. Meanwhile, an anti-disturbance controller is developed to overcome disturbances of uncertain payload and gripper actuation, enabling precise tracking of the planned trajectory and accurate grasping of the target. Simulations and real-world experiments illustrate the superior computational efficiency and task performance of GRIPPER. The task of capturing and delivering a non-cooperative target is accomplished, highlighting the robustness, adaptability, and real-time performance of the framework in highly adversarial scenarios.

cs.RO↗

arXiv2026

From Instruction to Event: Sound-Triggered Mobile Manipulation

Current mobile manipulation research predominantly follows an instruction-driven paradigm, where robots rely on predefined textual commands to execute tasks. However, this setting confines robots to a passive role, limiting robotic autonomy and the ability to react to dynamic environmental events. To address these limitations, we introduce Sound-Triggered Mobile Manipulation (STMM), where robots must actively perceive and interact with sound-emitting objects without explicit action instructions. To support STMM, we develop Habitat-Echo, a simulation platform that integrates sound rendering with physical interaction. We further propose a hierarchical baseline that translates high-level planning into low-level executions, where a task planner predicts a skill chain for policy models to execute sequentially. Experiments indicate the feasibility of perceiving auditory events and executing corresponding physical interactions without explicit instructions. Notably, in challenging multi-event scenarios, the robot successfully isolates the primary sources from overlapping acoustic interference to execute the first interactions, and subsequently proceeds to manipulate the secondary objects. These new challenges of planning-to-execution position STMM as a measurable research direction.The code and datasets will be released upon acceptance.

cs.RO↗

arXiv2026

Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-dependent normal--anomalous distinctions. We propose Context-Anchored Pair Supervision (CAPS), a supervision-recovery framework for TSAD. CAPS views ideal anomaly supervision as a matched comparison between normal and anomalous outcomes under the same temporal context, and seeks to recover such supervision without target-domain anomaly labels. Using simulated normal--anomalous pairs, CAPS learns structure and anomaly-semantic representations through reconstruction, background consistency, and within-pair counterfactual recombination. The resulting anomaly representations form a continuous semantic space with coarse modes and induce a sampleable multimodal prior. CAPS conditionally realizes sampled semantics as residual-form effects on target reference trajectories. The resulting context-anchored normal--anomalous counterparts provide temporal supervision for discriminative detector learning. Experiments on nine datasets show that CAPS achieves the strongest aggregate performance across all four evaluation metrics among the compared methods, while complementary ablations and transfer analyses support the roles of context anchoring, semantic disentanglement, and conditional realization.

cs.AI↗

arXiv2026

Rough Singular Integrals and Marcinkiewicz Integrals on Product Spaces, with $H^1$ Characterizations on Product Spheres

We prove the $L^p(\mathbb R^n\times\mathbb R^m)$ boundedness of the rough product singular integral $T_Ω$, its maximal truncation $T_Ω^*$, and the product Marcinkiewicz integral $μ_Ω$ for $1<p<\infty$, assuming that $Ω\in H^1(S^{n-1}\times S^{m-1})$ satisfies separate cancellation. These results include kernels in $H^1(S^{n-1}\times S^{m-1})$ that lie outside the Orlicz classes required in earlier work. We also obtain equivalent characterizations of the product Hardy space in terms of restricted, conjugate, and spectral Riesz transforms, together with radial maximal functions, non-tangential maximal functions, and area integrals associated with the spectral and ball Poisson extensions. The operator bounds are proved using restricted Riesz transforms and the Calderón--Zygmund rotation method. For the Marcinkiewicz integral, we further establish integrable bounds for the radial profiles of the transformed kernels.

math.CA↗

arXiv2026

Identifying the structure of La3Ni2O7 in the pressurized superconducting state

The crystal structure of La3Ni2O7 in its high-pressure superconducting state has been the subject of intense debate, with conflicting reports proposing orthorhombic (Amam or Fmmm) and tetragonal (I4/mmm) symmetries. Here, using high-pressure Raman spectroscopy down to 3 K, we resolve this controversy by tracking the structural evolution of La3Ni2O7 up to 32.7 GPa. Leveraging rigorous symmetry-based selection rules, we identify a single structural transition from the orthorhombic Amam phase to the Fmmm phase at ~14.5 GPa, signaled by a profound phonon renormalization. Crucially, the persistence of D2h symmetry across the transition rules out the tetragonal I4/mmm phase in the superconducting state in our measurements. The emergence of bulk superconductivity coincides precisely with this transition. Our results establish the orthorhombic Fmmm structure as the intrinsic host of superconductivity in La3Ni2O7 below 19.45 GPa, resolving a central structural controversy and providing a critical foundation for understanding the superconducting mechanism in bilayer nickelates.

cond-mat.supr-con↗

arXiv2026

TNLearn: An Open Source Python Package for Task-based Neurons

The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customized neurons, as task-based neurons capture useful prior knowledge from task-related data. To facilitate the use of task-based neurons in scientific research and industrial applications, we introduce TNLearn, an open-source Python package that provides automated construction of task-based neurons and networks, enabling smooth training of task-based networks. Comprehensive documentation, including technical exposition, API reference, and representative examples, is available online. TNLearn is open-sourced at https://github.com/NewT123-WM/tnlearn and has become a PyTorch ecosystem project.

cs.LG↗

arXiv2026

Limit Theory for $N$-Player $α$-Potential Games

The recently introduced framework of $α$-potential games facilitates the analysis of finite-player dynamic games by reducing the search for approximate Nash equilibria to the minimization of a single $α$-potential function. In this work, we show that the approximate potential structure of finite-player games persists in the large-population limit, leading naturally to a notion of $α$-potential mean field games (MFGs). Specifically, we show that both the optimal values and the minimizers of normalized $N$-player $α_N$-potential functions converge to those of a mean field control (MFC) problem with measure-valued controls. We identify the objective of this limiting MFC problem as an $α_\infty$-potential function for an MFG, and characterize the parameter $α_\infty$ analytically. We further show that $\lim_{N\to\infty}α_N= 0$ is equivalent to standard conditions for potential MFGs, in which case the limiting MFC objective becomes a potential function for the MFG. Together, these results provide new constructions of potential MFGs from finite-player games through the asymptotic condition $\lim_{N\to \infty}α_N= 0$, and establish propagation of chaos of potential-selected equilibria from $N$-player games to MFGs with general controlled diffusions, common noise, and non-separable state-control interactions.

math.OC↗

arXiv2026

POZZER: A Power Side Channel-guided Fuzzer for Black-Box Embedded Systems

Firmware fuzzing is an effective technique for discovering vulnerabilities in embedded systems. However, existing coverage-guided firmware fuzzers typically obtain feedback through firmware instrumentation, hardware debug interfaces, or firmware rehosting, which requires access to the firmware source code or binary image. Such requirements are often infeasible for off-the-shelf embedded devices, where firmware binaries are inaccessible, unrehostable, immodifiable, or undebuggable, necessitating fuzzing under black-box conditions. In this paper, we present POZZER, a power side-channel-guided fuzzer for black-box embedded systems. POZZER uses power traces as feedback to identify previously unseen behavior via an incrementally constructed graph-based representation of observed executions, guiding the fuzzer toward unexplored execution paths. Its non-profiling design requires neither prior firmware knowledge nor a clone device, extracting meaningful feedback from a single power trace per execution while remaining robust to measurement noise. We evaluate POZZER on 15 firmware targets across two platforms and two real-world commercial embedded devices. Across the resulting target-platform combinations, POZZER outperforms a blind fuzzer under the same time budget in 26 out of 30 target-platform combinations. Furthermore, POZZER discovers two previously unknown vulnerabilities in one of the commercial devices, both confirmed by the vendor, demonstrating its potential for identifying vulnerabilities in black-box embedded systems.

cs.CR↗

arXiv2026

Traits Run Deeper: Trait-Specific Asymmetric Fusion for Multimodal Personality Assessment

Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing approaches often adopt a uniform multimodal fusion strategy for all personality dimensions, overlooking trait-specific modality preferences and causing cross-modal interference. To address this, we propose Traits Run Deeper, a novel personality assessment framework consisting of three components. First, the Multimodal Foundation Representation (MFR) module constructs personality-oriented inputs and incorporates psychology-informed semantic templates as anchors, enabling foundation models to capture trait-relevant behaviors. Second, the Trait-Specific Modality Fusion (TSMF) module employs an asymmetric fusion mechanism, allowing each dimension to selectively exploit different modality pathways to capture heterogeneous preferences while reducing cross-modal contamination. Third, the Distribution-Calibrated Personality Regression (DCPR) module mitigates label imbalance and central tendency bias through target distribution calibration, improving robustness and stability. Experimental results on the AVI Challenge 2026 validation set show that our framework reduces mean squared error (MSE) by approximately 25% compared with the baseline. Consistent improvements on the official test set demonstrate that our method achieves the best performance and ranks first in the AVI Challenge 2026 Personality Assessment Track. The source code will be made available at [https://github.com/MSA-LMC/TraitsRunDeeper](https://github.com/MSA-LMC/TraitsRunDeeper).

cs.CV↗

arXiv2026

Zing-0.5: Toward Playable Worlds with Real-Time Joint Action and Text Control

We introduce Zing-0.5, a 5B autoregressive world model designed for playability: users can explore generated worlds, influence unfolding events, and respond to the resulting feedback through joint keyboard and online text control. Our approach brings together three technical contributions: (1) Unified action and text conditioning, combining magnitude-aware keyboard inputs with temporally aligned text instructions and jointly annotated videos to learn navigation and event control within the same sequence; (2) Event-scale supervision for incremental generation, using a segment-level teacher trained on connected multi-prompt videos to supervise a block-level causal student through distribution-matching distillation; and (3) Low-cost real-time interaction, combining four-step generation with context-preserving streaming to support 832 x 480 inference at 24 FPS at an estimated server rental cost of approximately USD 0.009 per stream-minute. Zing-0.5 achieves an overall score of 81.0 and a consistency score of 88.5 across 158 WBench Navigation cases. A joint-control demonstration shows a text-directed event change during continued navigation without restarting generation. We release the model weights, inference code, and Zing-SGLang serving implementation to support further work on playable generated worlds.

cs.CV↗

arXiv2026

FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture

Realizing large-scale quantum advantage is expected to require quantum error correction (QEC), making the compilation and optimization of logical operations a critical area of research. Logical computation imposes distinct constraints and operational paradigms that differ from those of the Noisy Intermediate-Scale Quantum (NISQ) regime, motivating the continued evolution of compilation tools. Given the complexity of this emerging stack, where factors such as gate decomposition precision and computational models must be co-designed, standardized benchmarks and toolkits are valuable for evaluating progress. To support this need, we introduce FTCircuitBench, which serves as: (1) a benchmark suite of impactful quantum algorithms, featuring pre-compiled instances in both Clifford+T and Pauli Based Computation models; (2) a modular end-to-end pipeline allowing users to compile and decompose algorithms for various fault-tolerant architectures, supporting both prebuilt and custom optimization passes; and (3) a toolkit for evaluating the impact of algorithms and optimization across the full compilation stack, providing detailed numerical analysis at each stage. FTCircuitBench is fully open-sourced and maintained on Github.

quant-ph↗

arXiv2026

Search for neutrinoless quadruple beta decay of $^{136}$Xe in PandaX-4T detector

The observation of neutrinoless quadruple beta decay (0$ν$4$β$) in the absence of neutrinoless double beta decay (0$ν$2$β$) has been argued to provide a strong indication that neutrinos are Dirac particles. We report a search for 0$ν$4$β$ decay of $^{136}\text{Xe}$ using a total $^{136}\text{Xe}$ exposure of 148.4 kg$\cdot$yr, collected during the commissioning and the first science runs of the PandaX-4T experiment. No significant excess of events over the background is observed. A lower limit on the 0$ν$4$β$ decay half-life of $^{136}\text{Xe}$ is set at 6.01 x $10^{24}$ yr at the 90% confidence level. This result establishes the most stringent constraint on this process in xenon, demonstrating the unique capability of the PandaX-4T detector in probing lepton number violation and shedding light on the fundamental nature of neutrinos.

nucl-ex↗

arXiv2026

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.

cs.CV↗

arXiv2026

Positive Sample Propagation along the Audio-Visual Event Line

Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. In order to learn discriminative features for a classifier, it is pivotal to identify the helpful (or positive) audio-visual segment pairs while filtering out the irrelevant ones, regardless whether they are synchronized or not. To this end, we propose a new positive sample propagation (PSP) module to discover and exploit the closely related audio-visual pairs by evaluating the relationship within every possible pair. It can be done by constructing an all-pair similarity map between each audio and visual segment, and only aggregating the features from the pairs with high similarity scores. To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed. We also propose a new weighting branch to better exploit the temporal correlations in weakly supervised setting. We perform extensive experiments on the public AVE dataset and achieve new state-of-the-art accuracy in both fully and weakly supervised settings, thus verifying the effectiveness of our method.

cs.CV↗