arXiv subjects

Shuang Li

Publications and source records attributed to Shuang Li.

At least 19 recordsLinked to original sources

arXiv2026

Grounding Across Views: Cross-View Referring Reasoning for Visual Grounding

Cross-view object geo-localization (CVOGL) aims to localize a target in a satellite image from an observation captured under another viewpoint. Existing methods typically specify the query with a click point or bounding box, marking the target region but underusing the semantic attributes and relations conveyed by natural language. Moreover, referring expressions are often viewpoint-dependent, making query-view descriptions difficult to ground directly in the satellite image. In this paper, we formulate cross-view visual grounding (CVVG), extending conventional point- or box-prompted CVOGL to instance grounding jointly guided by a referring expression and a visual exemplar. We present GAV (Grounding Across Views), a view-aware framework for selectively transferring visual and linguistic evidence across views. Its core is view-aware referring reasoning: target-centered descriptions are derived from the marked query image, checked for target-category consistency, and augmented with a direction-free role cue. Exemplar prompting, cross-view alignment, and scene-level discrimination ground this semantic evidence in a satellite instance. GAV achieves a new state of the art on MoP-UAV with 62.55% Acc@0.5. We also introduce CVOGL-Text, a benchmark for text-guided grounding from street- and drone-view queries to satellite images. GAV outperforms the click-point baseline by 7.09 and 6.79 Acc@0.25 points on the street- and drone-view splits, respectively, demonstrating robustness to larger viewpoint gaps. The code and annotations will be publicly released.

cs.CV↗

arXiv2026

From skew Combinatorial Nullstellensatz to sharp Ax-Katz congruences and zero counts

We give a unified account of zero counting in finite-field polynomial rings with commuting variables twisted by a common automorphism. A decomposition into fixed-field slices connects skew evaluation with ordinary polynomial evaluation. First, we derive a slice coefficient formula from the skew Combinatorial Nullstellensatz and use it to deduce skew Chevalley-Warning, including the sufficient condition $n>h\sum_i d_i$. Next, classical Ax-Katz gives the corrected integer congruence $Q^{\max\{0,\lceil(n-h\sum_i d_i)/D\rceil\}}\mid N+(M-1)δ$. We construct homogeneous examples proving optimality for every positive degree table and every number of variables. We also establish a sharp lower bound with a prescribed zero, an input-sensitive coefficient-filtration refinement, and transfers of known bounds for nonlinear zero sets. Detailed calculations explain the origin correction, incomplete norm blocks, and the distinction between characteristic-$p$ summation and higher integer divisibility. Existing geometric, interpolation, and counting theorems are explicitly attributed.

math.CO↗

arXiv2026

Distillation of supersinglet states

We introduce an entanglement distillation (purification) protocol for supersinglet states composed of N qubits. The supersinglet state we target is a total spin zero state with zero spin variance, and has a fully entangled structure involving all qubits. In our distillation protocol, three copies of an initial spin zero state are measured in the local total spin basis such that a higher fidelity supersinglet state is generated upon postselection. The initial state can be prepared using conventional Bell state distillation methods distributed in a way to target the supersinglet symmetries. The protocol uses only local operations and classical communications, and is suitable for long-distance applications such as quantum clock synchronization and cryptography, and avoids a high dimensional Schur transform such that it can be used for tasks such as quantum metrology.

quant-ph↗

arXiv2026

Quantum circuit representation and incomplete Bell measurements in Compton scattering

We formulate massless tree-level Compton scattering as an effective two-qubit operation on electron and photon helicities. In the Bell basis, the scattering map separates into two parity sectors and admits a simple quantum circuit representation with one nonunitary filter. After Kraus normalization, this filter defines an unsharp incomplete Bell measurement that distinguishes the two Bell-parity sectors but not the relative Bell label within each sector. We further show that the incomplete measurement alone does not surpass the classical teleportation threshold, whereas a subsequent complete Bell-basis readout enables unit conditional teleportation fidelity.

hep-ph↗

arXiv2026

Logic-Logit: A Logic-Based Approach to Choice Modeling

In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice models have been widely applied across various domains---such as commercial demand forecasting, recommendation systems, and consumer behavior analysis---typically categorized as parametric, nonparametric, or deep network-based. While recent innovations have favored neural network approaches for their computational power, these flexible models often involve large parameter sets and lack interpretability, limiting their effectiveness in contexts where transparency is essential. Previous empirical evidence shows that individuals usually use heuristic decision rules to form their consideration sets, from which they then choose. These rules are often represented as disjunctions of conjunctions (i.e., OR-of-ANDs). These rules-driven, consider-then-choose decision processes enable people to quickly screen numerous alternatives while reducing cognitive and search costs. Motivated by this insight, our approach leverages logic rules to elucidate human choices, providing a fresh perspective on preference modeling. We introduce a unique combination of column generation techniques and the Frank-Wolfe algorithm to facilitate efficient rule extraction for preference modeling---a process recognized as NP-hard. Our empirical evaluation, conducted on both synthetic datasets and real-world data from commercial and healthcare domains, demonstrates that Logic-Logit significantly outperforms baseline models in terms of interpretability and accuracy.

cs.LG↗

arXiv2026

Strict-Saddle Landscapes and Multi-Rank Geometry in Low-Tubal-Rank Tensor Sensing

We study the optimization landscape of low-tubal-rank tensor sensing through a balanced factorization. Under a tubal restricted isometry condition, we establish a quantitative strict-saddle landscape with no spurious local minima for arbitrary Fourier multi-rank profiles. We further show that the local geometry depends on the Fourier-slice ranks rather than the tubal rank alone. Uniform ranks yield quadratic growth transverse to the solution orbit, whereas nonuniform ranks produce quartically flat directions through hidden frequency-wise overparameterization, even when the factor width equals the exact tubal rank. Numerical experiments illustrate the global optimization behavior and the contrasting local geometries.

cs.LG↗

arXiv2026

Critical behavior and critical exponents of rotating QCD matter

We investigate the thermodynamic properties and critical behavior of rotating strongly interacting matter within the two-flavor Nambu--Jona-Lasinio model. The phase structure and critical endpoint (CEP) are determined in the temperature-angular-velocity $(T,ω)$ plane. Exploiting the thermodynamic conjugacy between the angular velocity $ω$ and rotational polarization $J$, we characterize the CEP through complementary thermal, coexistence, susceptibility, and critical-isotherm responses. The corresponding effective critical exponents are extracted along distinct thermodynamic trajectories and are found to be consistent with $α_ω\simeq0$, $β_ω\simeq1/2$, $γ_ω\simeq1$, and $δ_ω\simeq3$. These exponents are numerically compatible with the standard scaling relations and form a mutually consistent Landau mean-field scaling pattern. Our results establish a coherent thermodynamic characterization of the rotation-induced CEP and demonstrate how its singular behavior is encoded in rotational response observables associated with the conjugate pair $(ω,J)$.

hep-ph↗

arXiv2026

Substructure grooming of inclusive and photon-tagged jets in heavy-ion collisions

Jet substructure provides a powerful probe of partonic interactions within the quark-gluon plasma (QGP) in heavy-ion collisions. In this paper, we present a systematic theoretical study of the groomed substructures for both inclusive jets and photon-tagged jets ($γ+$jets) utilizing the Dynamical and Soft-Drop Grooming algorithms in PbPb collisions by employing the SHELL transport model. Our theoretical calculations exhibit a suppression at high $k_{\rm T,g}$, the relative transverse momentum between the two subjets in the groomed substructure, consistent with the recent ALICE measurements. We show that the suppression of high $k_{\rm T,g}$ arises from the combined effects of the reduction of the subleading subjet transverse momentum due to partonic energy loss and the narrowing of the groomed jet radius $R_g$ induced by selection bias. Our findings demonstrate that no enhancement is observed at high $k_{\rm T,g}$, even in the complete absence of selection bias. Furthermore, we propose that the broadening of $R_g$ in photon-tagged jets, which are less susceptible to selection bias compared to inclusive jets, provides relatively direct evidence of the jet substructure broadening. Our analysis reveals that the $R_g$ broadening becomes more pronounced as the jet radius increases, where the medium-induced gluon radiation plays a dominant role in driving such broadening. In particular, we find that as the jet radius increases, the Soft Drop grooming algorithm exhibits a better resolving power for the contribution of the medium response to the jet substructure broadening.

hep-ph↗

arXiv2026

Gated Residual Body-Hand Coordination for Whole-Body Humanoid Teleoperation

Whole-body humanoid teleoperation commonly combines a motion-tracking policy with a separate dexterous-hand retargeter. However, independently generated commands do not explicitly preserve body-hand geometric relations, leading to mismatches in relative wrist poses and fingertip positions during bimanual interaction. We present a gated residual coordination framework that keeps both modules frozen and applies bounded corrections to their outputs. A motion-conditioned action gate allocates correction authority across joint groups, while reference-geometry-dependent reward gates emphasize relevant interaction objectives during training. To establish the nominal body controller on Agile One, we introduce multi-pose morphology calibration that jointly estimates triaxial scales and effector-local offsets, together with staged motion dataset curation for training a SONIC-based tracker. The residual policy uses human motion references, initial commands, and robot proprioception without explicit object or contact observations. In simulation, it reduces wrist and fingertip geometry errors by 39.2-56.3% over direct composition on held-out GRAB motions, while preserving whole-body tracking on AMASS, with success rates of 89.03% without residual coordination and 89.29% with it. Ablations characterize the contributions of reward gating, adaptive correction authority, and separate body and hand correction heads.

cs.RO↗

arXiv2026

AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference

Multimodal large language models (MLLMs) require substantial computation to process numerous visual tokens across all transformer layers. Most methods for efficient MLLM inference exploit horizontal redundancy by compressing visual tokens. Beyond token reduction, recent studies exploit vertical redundancy through early exit or fixed-layer skipping. However, we find that the extent and distribution of this redundancy vary across inputs and differ between self-attention and MLP modules. Motivated by these observations, we propose AdaVSkip, which equips each layer with two lightweight routers that independently determine whether visual tokens pass through by or skip the self-attention and MLP modules. These decisions collectively define an input-specific visual-computation path, but their discrete and non-differentiable nature makes learning effective paths challenging. To address this challenge, we develop a progressive two-stage training framework that updates only the routers while keeping the backbone frozen. Stage I establishes an initial routing policy through supervised training with input-specific targets derived from module-wise necessity scores. To further align the routing policy with task performance, Stage II uses reinforcement learning to optimize routing decisions with direct feedback from generated answers. It combines an answer correctness reward with a skip-consistency reward that discourages excessive retention of visual-token computation. Across three MLLM backbones, AdaVSkip maintains strong task performance with substantially less computation. On LLaVA-NeXT-7B, AdaVSkip reduces FLOPs by 53.2\% while preserving the original model's average performance. Combining it with visual token compression increases this reduction to 91.2\%, while retaining 97.2\% of the original performance on average.

cs.CV↗

arXiv2026

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Pruning

Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens. Visual token pruning can reduce this cost, but requires accurate token importance estimates. Recent studies have demonstrated that text-to-vision attention from middle language model layers can effectively guide visual token pruning, typically using attention from a predefined middle layer to select the visual tokens to retain. Two problems therefore remain. First, our analysis shows that the layer whose attention is most responsive to the question varies substantially across samples, making a fixed layer suboptimal. Second, obtaining attention from the appropriate middle layer requires processing numerous visual tokens through several language model layers, by which point considerable computation has already been spent. To address both problems, we propose Middle-layer Attention Prediction (MAP), which uses Question Contrastive Teacher Selection to identify a sample-specific teacher layer by contrasting attention under the original and reference questions, and distills attention from the selected layer into a lightweight predictor that estimates visual token importance from multi-modal input features. During inference, MAP combines the predicted importance scores with a diversity criterion to prune visual tokens before the first language model layer. Thus, MAP requires no attention maps for pruning and remains compatible with existing inference acceleration techniques. Across ten benchmarks on LLaVA-NeXT-7B, MAP retains 97.5% of the unpruned model performance with only 5.56% of the visual tokens, yielding a 3.09x end-to-end speedup.

cs.AI↗

arXiv2026

SEGA: Drivable 3D Gaussian Head Avatar from a Single Image

Creating photorealistic 3D head avatars from limited input has become increasingly important for applications in virtual reality, telepresence, and digital entertainment. While recent advances like neural rendering and 3D Gaussian splatting have enabled high-quality digital human avatar creation and animation, most methods rely on multiple images or multi-view inputs, limiting their practicality for real-world use. In this paper, we propose SEGA, a novel approach for Single-imagE-based 3D drivable Gaussian head Avatar creation that combines generalized prior models with a new hierarchical UV-space Gaussian Splatting framework. SEGA seamlessly combines priors derived from large-scale 2D datasets with 3D priors learned from multi-view, multi-expression, and multi-ID data, achieving robust generalization to unseen identities while ensuring 3D consistency across novel viewpoints and expressions. We further present a hierarchical UV-space Gaussian Splatting framework that leverages FLAME-based structural priors and employs a dual-branch architecture to disentangle dynamic and static facial components effectively. The dynamic branch encodes expression-driven fine details, while the static branch focuses on expression-invariant regions, enabling efficient parameter inference and precomputation. This design maximizes the utility of limited 3D data and achieves real-time performance for animation and rendering. Additionally, SEGA performs person-specific fine-tuning to further enhance the fidelity and realism of the generated avatars. Experiments show our method outperforms state-of-the-art approaches in generalization ability, identity preservation, and expression realism, advancing one-shot avatar creation for practical applications.

cs.GR↗

arXiv2026

Heavy-quark transport across the QCD crossover driven by a lattice-constrained in-medium potential

We present a self-consistent framework for heavy-quark transport in the quark-gluon plasma across the QCD crossover region. By synthesizing perturbative and nonperturbative interactions into a unified interaction kernel, we circumvent the traditional reliance on arbitrary soft-hard momentum separation scales. The interaction is governed by an in-medium effective potential, incorporating short-range Yukawa screening and long-range confining string contributions, both rigorously constrained by the latest lattice QCD data. Our results reveal that the nonperturbative string tension is indispensable for capturing the extreme opacity of the medium near the critical temperature $T_c$. Specifically, our model predicts a spatial diffusion coefficient of $2πT D_s \approx 0.5 \sim 1.7$, demonstrating a striking quantitative agreement with the recent lattice QCD extractions. Ultimately, our results provide a robust dynamical interpretation of the strong heavy-quark coupling near the QCD crossover and offer a unified framework for describing heavy-flavor transport in hot and dense QCD matter.

hep-ph↗

arXiv2026

Wasserstein Policy Gradient for Entropy-Regularized Linear-Quadratic Control

Wasserstein policy gradient (WPG) updates state-conditional action laws by transport in the action space. We study entropy-regularized discounted linear-quadratic (LQ) control. A Bellman verification argument shows that the unrestricted problem has a linear-Gaussian optimal policy, and the discounted-occupancy-weighted statewise Wasserstein gradient is tangent to this policy class. WPG therefore reduces exactly to a finite-dimensional ODE for the feedback gain and action covariance. We prove that this ODE is globally well posed and converges exponentially from every admissible initialization. For each fixed LQ problem, the exponent has a positive limit as the entropy temperature tends to zero and contains no perturbative factor of the form $\exp(-c/τ)$, while retaining the usual dependence on the conditioning of the control problem.

math.OC↗

arXiv2026

Gated Memory Policy: In-Context Memorization and Adaptation

Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that demand in-context memorization of historical information within a single trial or in-context adaptation based on the outcomes of multiple past trials. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a significant performance drop due to distribution shift and overfitting. To address these issues, we propose Gated Memory Policy (GMP), a visuomotor policy that learns both when to recall memory and what to recall. To learn when to recall memory, GMP employs a learned memory gate mechanism that selectively activates history context only when necessary, improving robustness and reactivity. To learn what to recall efficiently, GMP introduces a lightweight cross-attention module that constructs effective latent memory representations. To further enhance robustness, GMP injects diffusion noise into historical actions, mitigating sensitivity to noisy or inaccurate histories during both training and inference. On our proposed non-Markovian benchmark MemMimic, GMP achieves a 30.1% average success rate improvement over long-history baselines, while maintaining competitive performance on Markovian tasks in RoboMimic. All code, data and in-the-wild deployment instructions are available on our project website https://gated-memory-policy.github.io/.

cs.RO↗

arXiv2026

A novel sampling method for reconstruction of a moving point acoustic source in $\mathbb{R}^3$

In this paper, we introduce a novel sampling method to recovering the trajectory of a moving point source in R^3, where both the spatial location and emission moment of the moving point source are unknown. Combining algebraic theory with geometric knowledge, we prove the uniqueness of the source location by using measured data from five observation points. Our sampling method constructs an indicator function based on the property that the residual of the time difference of arrival constraint formula vanishes at the true source location. It achieves the reconstruction of the spatial positions and emission moments of a moving point source only using data from five observation points and their corresponding arrival times. The algorithm not only reduces the required number of observation points, but also improves computational efficiency, stability, and noise resistance. Numerical experiments verify the effectiveness of the method.

math.NA↗

arXiv2026

SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation

Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studies have incorporated multimodal signals to provide richer token-level evidence for generation. However, existing approaches largely rely on alignment-centric fusion and underexplore synergistic information across modalities. In practice, synergistic information plays a critical role in capturing emergent item properties that cannot be inferred from any single modality alone. Such properties encode intrinsic item semantics and guide user preferences, enabling models to move beyond surface-level feature matching. To address this limitation, we propose \textbf{SynGR}, a synergistic generative recommendation framework that explicitly encourages the exploitation of cross-modal dependencies during generation. By constraining overreliance on dominant modalities, SynGR enables the model to capture emergent item semantics beyond shared or modality-specific signals. Extensive experiments across three benchmark datasets demonstrate that SynGR achieves superior performance.

cs.IR↗

arXiv2026

Dual-Domain Self-Supervised Artifact Removal Framework for Photoacoustic Computed Tomography

Photoacoustic Computed Tomography (PACT) often faces severe challenges from reconstruction artifacts due to sparse detection conditions. In this work, based on the distinct differences in artifact patterns between back-projection-based and Fourier-based reconstruction algorithms, we propose a self-supervised artifact removal framework that employs a lightweight Siamese Neural Network and a composite loss function integrating cross-domain fidelity and uncertainty-weighted consistency, effectively decoupling dual-domain features and filtering artifacts. Comprehensive validations using simulations, phantoms, in vivo rat and human experimental data demonstrate that the proposed method can significantly suppress image artifacts. Furthermore, enabled by the acceleration of the spatial-domain and frequency-domain inverse operator, this end-to-end approach also achieves exceptional computational efficiency.

cs.CV↗