arXiv subjects

Meng Wang

Publications and source records attributed to Meng Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Disentangling Dual Image References in Frequency Aware Diffusion Models for Personalized Generation

Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image customization for foreground and style transfer for background. Previous arts of diffusion models suffers from the text misalignment with background for image customization and foreground for style transfer during the denoising process. Such facts, as we observed, rooted from the entanglement among hybrid frequency bands during the denoising process. To address such salient limitation, in this paper, we study personalized generation based on dual references - customization and color and style reference - and propose a paradigm to disentangle these Dual image references within Frequency-aware Diffusion Models, dubbed Dual-FDM, to simultaneously tackle two crucial personalized image generation tasks: customization style transfer and color style transfer, by disentangling different frequency bands via mask strategy within frequency domain. For customization style transfer, we replace the mid-frequency band of the background in the style reference with that from the foreground of the customized reference. For color style transfer, we substitute the low-frequency band of the background in the style reference with that from both the foreground and background of the color reference. Both the substituted frequency bands are used as the key and value to reconstruct the query foreground and background of the denoised personalized image.Extensive experiments validate the superiority of Dual-FDM over the state-of-the-art diffusion models for personalized image generation. Our code can be accessed from https://github.com/htyjers/Dual-FDM.

cs.CV↗

arXiv2026

Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision

Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one regime and be routine in another, and the query alone may not reveal which regime applies. We study in-context learning (ICL) for time series anomaly detection (TSAD) through reference-conditioned detection, where a reference record provides evidence about expected behavior and model parameters remain fixed at inference. Supplying the reference is not enough: when training anomalies are recognizable from the query alone, the detector can fit its targets while ignoring the reference. We therefore introduce counterfactual supervision, which pairs one query with two references that support different normal rules and labels the query under each. At positions where the two labels disagree, no detector that ignores the reference can fit both targets. Anlu learns from this supervision by adding a reference memory and zero-initialized gated adapters to a frozen time-series foundation model (TSFM) pretrained for anomaly detection. On the 350 TSB-AD-U evaluation sequences, Anlu raises the mean VUS-PR of the frozen TSFM from 0.542 to 0.607. Replacing the reference with zeros lowers Anlu's score to 0.499.

cs.LG↗

arXiv2026

Central limit theorems for stochastic heat equation driven by Gaussian noise with heat-kernel covariance

In this article, we study the spatial fluctuations of the Skorohod solution to a stochastic heat equation on $\mathbb R^d$ for $d<4$, driven by multiplicative Gaussian noise with the non-separable covariance kernel $p_{|t-s|}(x-y)$. We prove that the solution is strictly stationary and spatially ergodic at every fixed time. For the centered spatial integral $F_R(t)=\int_{\{|x|<R\}}(u(t,x)-1)dx,$ we show that $\mathbb E[F_R(t)F_R(s)]\sim K(t,s)R^d$ as $R\to\infty$. Using moment estimates for the first two Malliavin derivatives and a second-order Gaussian Poincaré inequality, we establish a quantitative central limit theorem in total variation distance with rate $R^{-d/2}$. We also prove a functional central limit theorem for the process $\{R^{-d/2}F_R(t)\}_{t\geq0}$.

math.PR↗

arXiv2026

Adaptive Code Revision Attacks on AI Pull Request Reviewers

Pull-request review protects software before new code reaches users, helping prevent vulnerabilities that could expose users to attacks. AI agents increasingly perform these reviews and explain which problems need fixing. However, for an attacker submitting vulnerable code, this feedback also reveals what changes may secure approval. Existing PR attacks seek such approval through persuasive text and comments while keeping executable code fixed. This leaves unclear whether an attacker can use the feedback to repair the reported problem while preserving a vulnerability in the revised code. We therefore conduct an empirical study of this threat using AFCRA (Adaptive Feedback-guided Code Revision Attack). To distinguish successful attacks from genuine repairs, we construct AFCRA-Bench from 159 disclosed vulnerabilities, with executable exploits to verify vulnerabilities in code. Across five-round interactions with Sonnet 5 and GPT-5.5 reviewers, AFCRA reaches success rates 2.5x and 12.5x those of the strongest evaluated text- or comment-based attack. Case studies of these successes show how reviewers accept repairs of reported problems while overlooking surviving vulnerabilities. These findings establish feedback-guided code revision as a threat to automated PR review. To address this threat, we derive actionable implications for researchers, AI providers, PR reviewers, and PR authors on securing AI-assisted development.

cs.SE↗

arXiv2026

Beyond the boundaries of the second law of thermodynamics: Intrinsic-nonequilibrium distribution of large ions in charged small nanopores

Recent theoretical research on the fundamentals of statistical mechanics has led to a counterintuitive finding [2-5]: with a locally nonchaotic energy barrier, a macroscopic many-particle system may reach an intrinsic-nonequilibrium steady state without any external driving force, incompatible with the conventional statements of the second law of thermodynamics. In the present investigation, we experimentally demonstrate the concept of intrinsic nonequilibrium by replacing the weak gravitational force in the "toy model" with a strong Coulomb force. The tests are performed on a set of nanoporous carbon electrodes immersed in aqueous cesium pivalate solutions. The key characteristic is that the effective nanopore size is only slightly larger than the effective ion size but less than twice the ion size, so that the motion of the confined ions is quasi-one-dimensional and relatively nonchaotic. At first glance, the supercapacitive cells exhibit "normal" charge curves. However, the steady-state distribution of the large ions in the charged small nanopores (measured by the charge efficiency) inherently differs from equilibrium by nearly one order of magnitude, thereby breaking the boundaries of the second law of thermodynamics and potentially opening the possibility of producing useful work by absorbing heat from a single thermal reservoir with no other effect. Such an unusual phenomenon is compatible with the basic principle of maximum entropy and does not challenge the second law within its established domain of application (fully chaotic systems); instead, it extends our understanding of thermodynamics beyond the traditional framework.

cond-mat.soft↗

arXiv2026

Denser $\neq$ Better: Limits of On-Policy Self-Distillation for Continual Post-Training

Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgetting, with self-distillation as a particularly attractive approach. We revisit this optimistic claim through self-distillation policy optimization (SDPO). Our experiments show that SDPO accelerates in-domain specialization when teacher signals are stable and well aligned, but struggles to generalize out of distribution. In continual post-training, SDPO exhibits greater forgetting and can even collapse, whereas GRPO, the more established on-policy reinforcement learning method, adapts more conservatively and better preserves prior capabilities. Further analyses link these failures to increased drift in parameter and response space, and to amplification of high-frequency artifacts through a self-reinforcing teacher-student loop. Thus, on-policy data alone is insufficient for continual learning. Self-distillation is effective when teacher targets are stable and token-level supervision is reliable, but should not be treated as a default stabilizer for continual post-training. Our code is available at https://github.com/Moenupa/SDPO-CL.

cs.LG↗

arXiv2026

Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection

Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different detection criteria. While time-series foundation models provide transferable representations, coupling them with a fixed anomaly-scoring mechanism can overlook this variation. This motivates a different perspective on foundation-model-based TSAD: using foundation models to coordinate specialized anomaly criteria rather than directly imposing a universal one. Based on this view, we propose \textbf{TS-Router}, a generalist-representation, specialist-detection framework that estimates the relative competence of heterogeneous anomaly detectors from pretrained temporal representations and selects suitable specialists for each target series. To avoid relying on specialist-performance labels from real tasks, we derive soft competence supervision from specialists' relative performance on labeled simulated tasks. At deployment, routing requires no target anomaly labels, and only the selected specialists are fitted unsupervisedly on the target series. We bound Top-$k$ set-competence regret under representation coverage and conditional competence stability. Across 16 real-world benchmarks and four complementary evaluation metrics, TS-Router achieves the best overall average rank. Controlled ablations with multiple frozen TSFM encoders further support the use of pretrained representations for competence estimation and adaptive specialist selection. The code is available at https://anonymous.4open.science/r/TS-Router-D8FF.

cs.LG↗

arXiv2026

GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales

On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merging expert models into a single model. Existing diffusion OPD, however, mostly focus on the latter, with teachers and students sharing the same backbone and scale. We investigate large-to-small diffusion opd from large teachers to a small student and find that the standard recipe fails. To find the underlying cause, we propose Fixed-State KL, an effective and fair way to measure the distribution gap between student and teacher during OPD training for diffusion models. We are the first to clarify why large-to-small OPD is challenging for diffusion models: a smaller student struggles to perfectly match the distribution of a larger teacher, while classifier-free guidance can accumulate and amplify the distributional discrepancies between the student's conditional and unconditional branches and those of the teacher. To solve this problem, we propose GFD-OPD, a simple yet effective method that reduces the student-teacher gap while avoiding the error amplification of the CFG composition. Across numerous experiments, GFD outperforms previous baselines in both training efficiency and final performance, achieving state-of-the-art results on all benchmarks.

cs.LG↗

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↗