arXiv subjects

Qi Wu

Publications and source records attributed to Qi Wu.

At least 73 records · Page 4Linked to original sources

arXiv2026

Frequency Decoupled Framework for Screen Content Image Super-Resolution

Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they overlooked the inherent frequency characteristics, leading to suboptimal performance. We propose a frequency decoupled framework (FDF) that rethinks SCISR from a phasor perspective by capturing structured energy in amplitude and relational continuity in phase, and jointly exploiting them with bespoke implicit representations to faithfully recover the regular textures and global configuration of Screen Content Image (SCI). Amplitude-Phase Factorization Network (APFN) first separates images into amplitude and phase streams, where Amplitude Clustering Module (ACM) organizes sparse yet high-energy amplitude responses into representative prototypes for periodic pattern extraction, while Phase Consistency Self-Attention (PCSA) progressively reinforces configuration through continuous consistency propagation. And Oscillation-Anharmonic Implicit Fitting Network (OAIF-Net) integrates periodic and coherent implicit representations for efficient exploitation of the periodic patterns and coherent context embedded in SCI. Experimental results show FDF achieves state-of-the-art SCISR performance at multiple scales across four public SCI datasets. Ablation experiments further demonstrate the effectiveness of each component in extracting and exploiting periodic patterns and coherent context.

cs.CV↗

arXiv2026

Local Turán inequalities for walks and the spectral radius

Nikiforov's well-known spectral Turán inequality for walks states that, for every graph $G$ with clique number $ω(G)$, $λ^r(G)\le w_r(G)(1-1/ω(G))$, where $λ(G)$ is the largest eigenvalue of the adjacency matrix of $G$, and $w_r(G)$ is the number of walks with $r$ vertices in $G$. For $r=1$, this is Wilf's inequality; for $r=2$, it gives Nikiforov's spectral Turán theorem. Recently, Liu and Ning proved local versions of these two inequalities, strengthening both Wilf's inequality and Nikiforov's spectral Turán theorem. It is natural to ask whether Nikiforov's spectral Turán inequality for walks also admits a local strengthening. Motivated by this question, Kannan, Kumar, and Pragada conjectured the vertex-local bound $λ^r(G)\le \sum_{v\in V(G)} w_r(v)(1-1/c_G(v))$, where $w_r(v)$ denotes the number of walks with $r$ vertices starting at $v$, and $c_G(v)$ is the maximum order of a clique containing $v$. This conjecture is important because it gives the most natural local form of Nikiforov's spectral Turán inequality for walks. In this paper, we confirm this conjecture. More precisely, for $r\ge 2$, we prove the stronger edge-local inequality $$λ^r(G) \le \sum_{uv\in E(G)} \frac{c_G(uv)-1}{c_G(uv)} \bigl(w_{r-1}(u)+w_{r-1}(v)\bigr),$$ where $c_G(uv)$ is the maximum order of a clique containing the edge $uv$. Our result implies Nikiforov's spectral Turán inequality for walks and unifies several local spectral extremal results of Liu and Ning. We also determine all extremal graphs for both the edge-local and vertex-local inequalities. The main new ingredient is a Markov-chain estimate whose transition matrix is constructed from a Perron vector of $A(G)$; this estimate carries the local edge coefficient through walks of arbitrary length.

math.CO↗

arXiv2026

CG-MambaNet: A spatiotemporal framework for cross-patient epileptic seizure prediction using CNN-GCN-Mamba-BiLSTM with event-level clinical evaluation

Epileptic seizure prediction from scalp EEG is critical for closed-loop neurostimulation therapy. Existing deep-learning methods share two architectural limitations: they model EEG channels independently, neglecting inter-channel spatial synchrony, and process raw time-domain samples without frequency decomposition. A methodological limitation also affects the field: most studies use data splits that permit patient-level information leakage, yielding optimistic estimates that do not generalise to unseen patients. We present CG-MambaNet, a spatiotemporal seizure prediction framework addressing all three limitations. A depthwise separable CNN front-end decomposes each EEG patch into multi-scale spectro-temporal features, capturing delta-to-gamma band dynamics before sequence modelling. A two-layer graph convolutional network with a learnable adjacency matrix captures inter-channel functional synchrony without montage-specific coordinates, applicable to bipolar (CHB-MIT) and referential (SIENA) montages. A bidirectional Mamba encoder followed by a bidirectional LSTM models long- and short-range temporal dynamics, and a two-layer MLP produces the final seizure probability. This serial hierarchy ensures frequency decomposition precedes spatial mixing, which precedes temporal integration. Under strict leave-one-patient-out cross-validation with five independent random seeds, CG-MambaNet achieves AUC-ROC of 0.8152+/-0.0176 on CHB-MIT (n=22) and 0.7104+/-0.0261 on SIENA (n=6), surpassing all published cross-patient methods without domain adaptation. An event-level evaluation framework merging consecutive alarmed windows via a persistence filter reduces false predictions to 0.32 alarms/hour on CHB-MIT, demonstrating clinically meaningful alarm burden.

eess.SP↗

arXiv2026

Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation

Instance Goal Navigation (IGN) requires an embodied agent to find a specific object instance among distractors from an under-specified natural-language description. Such ambiguity often cannot be resolved from perception and language alone, making interaction with an oracle a natural mechanism for disambiguation. Prior interactive methods allow oracle queries but treat lightweight clarification and route-level guidance alike, letting agents boost success rate through repeated high-information questions rather than by resolving the underlying ambiguity efficiently. We recast interactive IGN as a cost-sensitive uncertainty-reduction problem, where the agent should ask the question whose answer provides the largest reduction in navigation uncertainty relative to its penalty. To this end, we apply an information-gain analysis on existing navigation corpora to identify which cues reduce navigation uncertainty, yielding a compact set of question types and data-derived weights. However, existing interactive navigation benchmarks do not model the cost of different question types or evaluate how efficiently agents use interaction, making them unsuitable for studying cost-sensitive interaction. Based on this taxonomy, we construct a benchmark for diagnosing interaction behavior and efficiency, together with a Weighted Success Rate metric that penalizes each query by its derived cost. We further propose a zero-shot MLLM navigator that selectively queries at each decision step only when the expected uncertainty reduction justifies the interaction cost.

cs.CV↗

arXiv2026

Multicast Capacity of XL-RIS Assisted Hybrid Near- and Far-Field mmWave Communications

Multicast transmission in millimeter-wave (mmWave) networks is fundamentally limited by the weakest user, and blockages further exacerbate this problem. Large-scale reconfigurable intelligent surfaces (XL-RIS) offer a promising solution by providing high array gain to overcome blockages. However, the large aperture of XL-RIS significantly expands the near-field region, creating a hybrid-field scenario where some users lie in the near-field while others remain in the far-field. Existing hybrid-field studies on XL-RIS have primarily focused on channel estimation and deployment optimization, leaving multicast capacity analysis unexplored. This paper investigates the fundamental capacity limits of XL-RIS-assisted multicast communications in hybrid-field scenarios. For the fundamental two-user case consisting of one near-field and one far-field user, we derive the optimal closed-form covariance matrix and optimize the RIS phase shifts via manifold optimization. We establish that the multicast capacity scales as $Θ(\log_2(MN))$ as the number of transmit antennas M and/or RIS elements N grow large, and prove this scaling is order-tight. Numerical results validate the bounds and show the impact of M, $N$, and distance on the multicast rate.

eess.SP↗

arXiv2026

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion

Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space, and a decoder maps the generated latents back to pixels. Yet the latent-to-pixel decoder is reconstruction-oriented, optimized to invert the encoder rather than synthesize more details, and becomes increasingly costly at megapixel scale. This drawback calls for a more expressive and efficient decoding paradigm. Motivated by recent progress in scalable pixel-space diffusion, we introduce PiD, a Pixel diffusion Decoder that reformulates latent decoding as conditional pixel diffusion, unifying decoding and upsampling into one generative module. By denoising directly in high-resolution pixel space, PiD synthesizes $4\times$ and even $8\times$ upscaled images with low latency. For latent conditioning, a lightweight sigma-aware adapter injects noise-corrupted latents into the pixel diffusion backbone, enabling PiD to decode partially denoised latents and terminate the latent diffusion process early. To further improve efficiency, we distill the model using DMD2, reducing inference to just 4 steps. PiD applies to both conventional VAE latents and semantic latents (e.g., SigLIP, DINOv2) used in recent RAE-based models. PiD decodes latents of $512 \times 512$ images into $2048 \times 2048$ pixels in under 1 second with 13 GB peak memory on a consumer RTX 5090, and as fast as 210 ms on a GB200 GPU, about $6\times$ faster than cascaded diffusion-based super-resolution pipelines with better visual fidelity.

cs.CV↗

arXiv2026

X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction

Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report, we introduce X-OmniClaw, a unified mobile agent designed for multimodal understanding and interaction in the Android ecosystem. This unified architecture of perception, memory, and action enables the agent to handle complex mobile tasks with high contextual awareness. Specifically, Omni Perception provides a unified multimodal ingress pipeline that integrates UI states, real-world visual contexts, and speech inputs, leveraging a temporal alignment module to decompose raw data into structured multimodal intent representations. Omni Memory leverages multimodal memory optimization to enhance personalized intelligence by integrating runtime working memory for task continuity with long-term personal memory distilled from local data, enabling highly context-aware and personalized interactions. Finally, Omni Action employs a hybrid grounding strategy that combines structural XML metadata with visual perception for robust interaction. Through Behavior Cloning and Trajectory Replay, the system captures user navigation as reusable skills, enabling precise direct-access execution. Demonstrations across diverse scenarios show that X-OmniClaw effectively enhances interaction efficiency and task reliability, providing a practical architectural blueprint for the next generation of mobile-native personal assistants.

cs.CV↗

arXiv2026

Risk-Neutral Generative Networks

We present a generative approach to price options and extract risk-neutral densities from the market. Specifically, we model the underlying log-returns on the time-to-maturity continuum as a generative model from standard normal. Neural nets are used to represent the term structures of the location, the scale, and the higher-order moments. We impose stringent conditions on the learning process to ensure no arbitrage. This model allows for the efficient generation of samples to price options across strikes and maturities. We have validated the effectiveness of this approach by benchmarking it against a comprehensive set of baseline models. Experiments show that the extracted risk-neutral densities accommodate a diverse range of shapes. Its accuracy significantly outperforms the extensive set of baseline models--including three parametric models and nine stochastic process models--in terms of accuracy and stability. The success of this approach is attributed to its capacity to offer flexible term structures for risk-neutral skewness and kurtosis.

q-fin.MF↗

arXiv2026

Triangles in graphs without the expansion of $4$-cycle

The expansion $F^{\triangle}$ of a graph $F$ is the graph obtained from $F$ by replacing each edge with a triangle. Lv \etal proposed a conjecture on the maximum number of triangles in a graph without $P_k^{\triangle}$ or $C_k^{\triangle}$ for every $k \ge 4$. Their conjecture was confirmed in previous work for $P_k^{\triangle}$ when $k \ge 4$ and $C_k^{\triangle}$ when $k \ge 5$. In this note, we resolve the remaining case $C_4^{\triangle}$, demonstrating that this is the only counterexample to their conjecture.

math.CO↗

arXiv2026

Shear-stress-constrained superconductivity in Ruddlesden-Popper nickelates

Ruddlesden-Popper nickelates exhibit superconductivity under pressure in bulk crystals and under epitaxial constraint in thin films, while remaining highly sensitive to sample quality, oxygen content, defects, and stress conditions. We propose that the metastable RP lattice becomes superconducting only when the local constrained deformation of the Ni-O framework falls within a bounded shear-strain window. This deformation controls octahedral rotations, the interlayer Ni-O-Ni bond angle, and coupling between Ni dz2 and dx2-y2 orbitals. This shear-stress-constrained superconductivity scenario unifies the understanding of the pressure threshold, reversibility, spatial inhomogeneity, pressure-medium dependence, film-substrate sensitivity, and reproducibility.

cond-mat.supr-con↗

arXiv2026

Hunting for $B\bar B$ molecular state $X_{b0}$ via radiative transition of $Υ(10753)$

We investigate the radiative decay $Υ(10753) \to γX_{b0}$ within the framework of nonrelativistic effective field theory (NREFT). The $Υ(10753)$ is treated as an $S$-$D$ mixed state of the $Υ(4S)$ and $\YD$, while the $X_{b0}$ is interpreted as a weakly bound $B\bar{B}$ molecule with $J^{PC}=0^{++}$. The decay process was assumed to occur via the intermediate meson loops involving the $S$-wave $B^{(*)}$ and $P$-wave $B_1^{(\prime)}$ mesons. Our calculated results indicate that the decay $Υ(10753) \to γX_{b0}$ is dominated by the $B_1^{(\prime)}$ meson loops. The partial decay width is predicted to be $0.2-1.5~\mathrm{keV}$ for a binding energy range of $ε_X=0-10~\mathrm{MeV}$, corresponding to a branching fraction of $10^{-6}-10^{-5}$. The decay width is found to be insensitive to the full width of the $B_1^{\prime}$ meson. Our study suggests that the radiative decay of the $Υ(10753)$ is a promising channel to search for the $X_{b0}$ state, which is crucial for testing heavy-quark symmetries and understanding the exotic hadron spectrum in the bottom sector.

hep-ph↗

arXiv2026

TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs

Explainability for Large Language Model (LLM) agents is especially challenging in interactive, partially observable settings, where decisions depend on evolving beliefs and other agents. We present \textbf{TriEx}, a tri-view explainability framework that instruments sequential decision making with aligned artifacts: (i) structured first-person self-reasoning bound to an action, (ii) explicit second-person belief states about opponents updated over time, and (iii) third-person oracle audits grounded in environment-derived reference signals. This design turns explanations from free-form narratives into evidence-anchored objects that can be compared and checked across time and perspectives. Using imperfect-information strategic games as a controlled testbed, we show that TriEx enables scalable analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do. Our results highlight explainability as an interaction-dependent property and motivate multi-view, evidence-grounded evaluation for LLM agents. Code is available at https://github.com/Einsam1819/TriEx.

cs.CL↗

arXiv2026

Study of $χ_{cJ}\to ηηη^\prime$ via intermediate charmed meson loop mechanisms and its implications for non-observation of $η_1(1855)$ in $χ_{cJ}$ decays

Recently, the BESIII Collaboration reported the first observation of the decays $χ_{cJ} \to ηηη^\prime$ in order to search for the $1^{-+}$ exotic state $η_1(1855)$. A partial wave analysis of the $ηη^\prime$ invariant mass spectrum shows no significant signal for the $η_1(1855)$. In this work, we, using an effective Lagrangian approach, investigate the processes $χ_{cJ} \to ηηη^\prime$ via the box and triangle loops involving charmed mesons and the scalar meson $f_0(1500)$. Our calculations reproduce well the experimental branching fractions of $χ_{cJ} \to ηηη^\prime$. Furthermore, we present the predictions of the relevant invariant mass spectra of $ηη^\prime$ and $ηη$ produced in the $χ_{c1}$ decay, which seem overall consistent with the BESIII measurements. In the present model, the decay $χ_{c1} \to ηηη^\prime$ is dominated by the triangle and box loop contributions. The consistency between our theoretical results and the BESIII measurements sheds light on the underlying decay mechanism of the $χ_{cJ}$ decaying into light mesons and might be helpful to understand the absence of the $η_1(1855)$ signal in the decay channels $χ_{cJ} \to ηηη^\prime$.

hep-ph↗

arXiv2026

FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation

Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are largely developed in isolation, overlooking the benefits of leveraging shared detection patterns across platforms to improve performance and promptly identify emerging bot variants. The heterogeneity of data distributions and model architectures further complicates the design of an effective cross-platform and cross-model detection framework. To address these challenges, we propose FedRio (Personalized Federated Social Bot Detection with Cooperative Reinforced Contrastive Adversarial Distillation framework. We first introduce an adaptive message-passing module as the graph neural network backbone for each client. To facilitate efficient knowledge sharing of global data distributions, we design a federated knowledge extraction mechanism based on generative adversarial networks. Additionally, we employ a multi-stage adversarial contrastive learning strategy to enforce feature space consistency among clients and reduce divergence between local and global models. Finally, we adopt adaptive server-side parameter aggregation and reinforcement learning-based client-side parameter control to better accommodate data heterogeneity in heterogeneous federated settings. Extensive experiments on two real-world social bot detection benchmarks demonstrate that FedRio consistently outperforms state-of-the-art federated learning baselines in detection accuracy, communication efficiency, and feature space consistency, while remaining competitive with published centralized results under substantially stronger privacy constraints.

cs.AI↗

arXiv2026

Why Instruction-Based Unlearning Fails in Diffusion Models?

Instruction-based unlearning has proven effective for modifying the behavior of large language models at inference time, but whether this paradigm extends to other generative models remains unclear. In this work, we investigate instruction-based unlearning in diffusion-based image generation models and show, through controlled experiments across multiple concepts and prompt variants, that diffusion models systematically fail to suppress targeted concepts when guided solely by natural-language unlearning instructions. By analyzing both the CLIP text encoder and cross-attention dynamics during the denoising process, we find that unlearning instructions do not induce sustained reductions in attention to the targeted concept tokens, causing the targeted concept representations to persist throughout generation. These results reveal a fundamental limitation of prompt-level instruction in diffusion models and suggest that effective unlearning requires interventions beyond inference-time language control.

cs.CL↗

arXiv2026

Pressure-enhanced superconductivity and its correlation with suppressed resistance dip in (La,Pr)3Ni2O7 films

The discovery of superconductivity with a transition temperature (Tc) exceeding 40 K in La3Ni2O7 and (La,Pr)3Ni2O7 thin films at ambient pressure provides a viable platform for the experiments that can only be conducted under ambient-pressure conditions, and for the theoretical investigations aimed at understanding the commonalities and peculiarities of the behaviors related to the superconductivity between the film and the compressed bulk systems - including the effects of oxygen vacancies and strain. Consequently, it is crucial to determine whether Tc can be further enhanced and to uncover the underlying physics that controls the Tc value in these ambient-pressure superconducting thin films. Here, we report a systematic study of hydrostatic pressure effects on the superconducting properties of (La,Pr)3Ni2O7 thin films. We find that external pressure universally enhances Tc of the film samples regardless of their initial Tc value. The onset Tc of 68.5 K at 2.0 GPa demonstrates a notable increase from 62 K at 0.3 GPa. Furthermore, we observe that the samples without zero resistance show a resistance dip just above the superconducting transition, whereas the samples that exhibit zero resistance do not display this dip. Applying pressure can suppress the dips and drive the system toward zero resistance. Based on our results, we propose that this feature is associated with oxygen vacancies and that the depth of the dip can serve as an indicator of the concentration of the vacancies. It is plausible that the dip is caused by the localization of mobile electrons at the vacancy sites. Applying pressure can delocalize these electrons, which in turn may contribute to the increase in Tc.

cond-mat.supr-con↗

arXiv2026

SimULi: Real-Time LiDAR and Camera Simulation with Unscented Transforms

Rigorous testing of autonomous robots, such as self-driving vehicles, is essential to ensure their safety in real-world deployments. This requires building high-fidelity simulators to test scenarios beyond those that can be safely or exhaustively collected in the real-world. Existing neural rendering methods based on NeRF and 3DGS hold promise but suffer from low rendering speeds or can only render pinhole camera models, hindering their suitability to applications that commonly require high-distortion lenses and LiDAR data. Multi-sensor simulation poses additional challenges as existing methods handle cross-sensor inconsistencies by favoring the quality of one modality at the expense of others. To overcome these limitations, we propose SimULi, the first method capable of rendering arbitrary camera models and LiDAR data in real-time. Our method extends 3DGUT, which natively supports complex camera models, with LiDAR support, via an automated tiling strategy for arbitrary spinning LiDAR models and ray-based culling. To address cross-sensor inconsistencies, we design a factorized 3D Gaussian representation and anchoring strategy that reduces mean camera and depth error by up to 40% compared to existing methods. SimULi renders 10-20x faster than ray tracing approaches and 1.5-10x faster than prior rasterization-based work (and handles a wider range of camera models). When evaluated on two widely benchmarked autonomous driving datasets, SimULi matches or exceeds the fidelity of existing state-of-the-art methods across numerous camera and LiDAR metrics.

cs.CV↗

arXiv2026

LightMover: Generative Light Movement with Color and Intensity Controls

We present LightMover, a framework for controllable light manipulation in single images that leverages video diffusion priors to produce physically plausible illumination changes without re-rendering the scene. We formulate light editing as a sequence-to-sequence prediction problem in visual token space: given an image and light-control tokens, the model adjusts light position, color, and intensity together with resulting reflections, shadows, and falloff from a single view. This unified treatment of spatial (movement) and appearance (color, intensity) controls improves both manipulation and illumination understanding. We further introduce an adaptive token-pruning mechanism that preserves spatially informative tokens while compactly encoding non-spatial attributes, reducing control sequence length by 41% while maintaining editing fidelity. To train our framework, we construct a scalable rendering pipeline that generates large numbers of image pairs across varied light positions, colors, and intensities while keeping the scene content consistent with the original image. LightMover enables precise, independent control over light position, color, and intensity, and achieves high PSNR and strong semantic consistency (DINO, CLIP) across different tasks.

cs.CV↗