arXiv subjects

Hang Liu

Publications and source records attributed to Hang Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Embedding Hybrid Systems into Continuous Latent Vector Fields

This work proves that an $n$-dimensional hybrid system can be embedded into an $m$-dimensional Euclidean space equipped with a continuous vector field on its embedded image whenever $m>2n$. This result suggests that an intrinsically discontinuous hybrid system generically admits a continuous extrinsic representation that is well-posed for differentiable optimization. Building on this existence theorem, we show that a latent Neural ODE with consistency loss in both the latent and state space can accurately recover the flow of hybrid systems. Extensive experiments suggest the proposed method outperforms the existing method in learning hybrid systems with varying geometries from only time series data.

cs.LG↗

arXiv2026

Two-Sample Homogeneity Test via Entropic Optimal Transport

This paper proposes a two-sample homogeneity test based on entropic optimal transport (EOT) maps from a common reference distribution -- the uniform law on the unit ball. The test statistic is the squared $L^2$-distance between the two empirical EOT maps. For fixed entropic regularization parameter, we prove that the population map discrepancy is identifiable, derive a functional central limit theorem for the empirical map difference under the null, and establish the Gaussian quadratic-form null limit. We also prove consistency against fixed alternatives and characterize local asymptotic power under contiguous alternatives. A weighted multiplier bootstrap is proposed to calibrate the non-pivotal null distribution, and its validity is established. Extensive simulations demonstrate that the proposed EOT-map test has reliable finite-sample size control and exhibits competitive power compared with other existing methods. The method is particularly powerful for location alternatives and, beyond a single scalar discrepancy, it provides additional diagnostic information on how the two distributions differ. Finally, a real data application concludes the paper.

stat.ME↗

arXiv2026

Paratrophic Determinants over $\mathbb{Z}/N\mathbb{Z}$ via Discrete Fourier Transform

In this note, we investigate the paratrophic determinants attached to the multiplicative semigroup $\mathbb{Z}/N\mathbb{Z}$. We show that, via discrete Fourier, cosine, and sine transforms, these determinants factor into products of group determinants indexed by $d|N$. This yields explicit formulas for several determinant families, including determinants involving periodic Bernoulli functions and powers of the tangent function. As an application, we also prove a corrected version of a conjecture of Sun Zhi-Wei.

math.NT↗

arXiv2026

Hyperon-Nucleon Spectrometer

Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China.

physics.ins-det↗

arXiv2026

STAP: A Shuffle-Tokenized App Predictor with Ultra Long Context for Vocabulary-Free Mobile App Prediction

Predicting the next mobile application a user will launch is essential for intelligent device resource management and proactive assistance. Existing models rely on fixed app vocabularies, which prevents them from generalizing across different app ecosystems. Many also depend on user-specific knowledge, which complicates deployment in cold start scenarios. We propose STAP, a Transformer-based model that eliminates the need for a fixed vocabulary. STAP replaces true app identities with randomly reassigned virtual indices via a shuffle mechanism, and compensates for discarded semantic information by processing behavioral sequences with an ultra-long context design. A theoretical analysis shows that, given a sufficiently long context, the predicted distribution converges to the correct one despite the anonymity of the mapping. Experiments on two datasets from different continents demonstrate that STAP achieves strong cross-dataset zero-shot prediction accuracy -- a setting where all existing fixed-vocabulary methods are inherently inapplicable -- while its cold start performance within each dataset remains competitive with leading models. Furthermore, we introduce a deployment strategy that enables the model to retain a sufficiently long context during continuous inference while keeping latency within acceptable bounds.

cs.LG↗

arXiv2026

Enabling Safety-Critical Wireless Communications via Safe Reinforcement Learning

Ensuring strict safety guarantees is the paramount challenge for emerging 5G/6G wireless systems, particularly as they increasingly govern mission-critical applications ranging from autonomous UAV swarms to industrial automation. While deep reinforcement learning (DRL) offers a promising solution for complex resource allocation, standard algorithms frequently violate essential constraints, such as QoS mandates and power limits, posing unacceptable risks of system failure and regulatory non-compliance. We propose Safe-Deep Q-Learning, a novel algorithm that simultaneously addresses all three challenges: it handles mixed-integer nonconvex problems by approximating the Q-function, adapts to stochastic dynamics, and enforces dual-timescale constraints using integrated Lagrangian methods. Our framework features adaptive penalty scaling and constraint violation tracking, specifically tailored for wireless environments, and is designed to operate in both distributed and centralized architectural modes. We prove convergence to optimal constraint-satisfying policies under mild conditions and demonstrate robustness through dual variable stabilization. Validation on unmanned aerial vehicle (UAV) swarm control network and post-disaster emergency communications applications shows that Safe-Deep Q-Learning achieves stringent adherence to safety bounds with near-zero violation rates, significantly outperforming existing constrained RL baselines, establishing its effectiveness for safety-critical wireless deployments.

eess.SP↗

arXiv2026

A Precision Emulation Approach to the GPU Acceleration of Ab Initio Electronic Structure Calculations

This study explores the use of INT8-based emulation for accelerating traditional FP64-based HPC workloads on modern GPU architectures. Through SCILIB-Accel automatic BLAS offload tool for cache-coherent Unified Memory Architecture, we emulate FP64 matrix multiplications in the LSMS CPU application in the MuST suite without code changes. We find that accuracy depends on both arithmetic precision and the properties of the operator, which can be dealt with through tunable precision emulation. Unlike traditional mixed-precision approaches, this method preserves original algorithms while optimizing hardware utilization. We showcase the potential of improving accuracy and performance at the same time. This work highlights the potential of AI-driven hardware to transform HPC, advocating for adaptive precision strategies in future scientific computing.

cs.DC↗

arXiv2026

Ego-Vision World Model for Humanoid Contact Planning

Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based planners struggle with contact complexity, while on-policy reinforcement learning (RL) is sample-inefficient and has limited multi-task ability. We propose a framework combining a learned world model with sampling-based Model Predictive Control (MPC), trained on a demonstration-free offline dataset to predict future outcomes in a compressed latent space. To address sparse contact rewards and sensor noise, the MPC uses a learned surrogate value function for dense, robust planning. Our single, scalable model supports contact-aware tasks, including wall support after perturbation, blocking incoming objects, and traversing height-limited arches, with improved sample efficiency and multi-task capability over on-policy RL. Deployed on a physical humanoid, our system achieves robust, real-time contact planning from proprioception and ego-centric depth images. Code and dataset are available at our website: https://ego-vcp.github.io/

cs.RO↗

arXiv2026

A Lattice QCD study of $p-Λ$ scattering in continuum and chiral limits

We present a first systematic study of $I=1/2$ proton-$Λ$ ($p$-$Λ$) scattering from lattice QCD, using seven sets of $(2+1)$-flavor lattice ensembles with pion masses spanning 135-317 MeV and three lattice spacings with $a=(0.052,0.077, 0.105)$ fm. Using Lüscher's finite-volume method, effective range expansion and chiral/continuum extrapolations, we obtain the inverse of scattering length and effective range for the $^1S_0$ channel as 0.177(83) GeV and 2.9(1.4) fm, and for the $^3S_1$ channel as 0.016(76) GeV and 1.8(1.1) fm. From the derived S-wave phase shifts, we provide an estimate of the $p-Λ$ scattering cross section. Our results for scattering length, effective range and cross sections are in good agreement with available experimental measurements. We also find that the $p-Λ$ system sustains attractive interactions. These results provide critical input for the unification of nuclear force theories and the construction of neutron star equations of state.

hep-lat↗

arXiv2026

PMG: Parameterized Motion Generator for Human-like Locomotion Control

Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain. In particular, while low-level motion tracking and trajectory-following controllers are mature, whole-body reference-guided methods are difficult to adapt to higher-level command interfaces and diverse task contexts: they require large, high-quality datasets, are brittle across speed and pose regimes, and are sensitive to robot-specific calibration. To address these limitations, we propose the Parameterized Motion Generator (PMG), a real-time motion generator grounded in an analysis of human motion structure that synthesizes reference trajectories using only a compact set of parameterized motion data together with high-dimensional control commands. Combined with an imitation-learning pipeline and an optimization-based sim-to-real motor parameter identification module, we validate the complete approach on our humanoid prototype ZERITH Z1 and show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs-including VR-based teleoperation-and enables efficient, verifiable sim-to-real transfer. Together, these results establish a practical, experimentally validated pathway toward natural and deployable humanoid control. Website: https://pmg-icra26.github.io/

cs.RO↗

arXiv2026

MICON-Bench: Benchmarking and Enhancing Multi-Image Context Image Generation in Unified Multimodal Models

Recent advancements in Unified Multimodal Models (UMMs) have enabled remarkable image understanding and generation capabilities. However, while models like Gemini-2.5-Flash-Image show emerging abilities to reason over multiple related images, existing benchmarks rarely address the challenges of multi-image context generation, focusing mainly on text-to-image or single-image editing tasks. In this work, we introduce \textbf{MICON-Bench}, a comprehensive benchmark covering six tasks that evaluate cross-image composition, contextual reasoning, and identity preservation. We further propose an MLLM-driven Evaluation-by-Checkpoint framework for automatic verification of semantic and visual consistency, where multimodal large language model (MLLM) serves as a verifier. Additionally, we present \textbf{Dynamic Attention Rebalancing (DAR)}, a training-free, plug-and-play mechanism that dynamically adjusts attention during inference to enhance coherence and reduce hallucinations. Extensive experiments on various state-of-the-art open-source models demonstrate both the rigor of MICON-Bench in exposing multi-image reasoning challenges and the efficacy of DAR in improving generation quality and cross-image coherence. Github: https://github.com/Angusliuuu/MICON-Bench.

cs.CV↗

arXiv2026

MePoly: Max Entropy Polynomial Policy Optimization

Stochastic Optimal Control provides a unified mathematical framework for solving complex decision-making problems, encompassing paradigms such as maximum entropy reinforcement learning(RL) and imitation learning(IL). However, conventional parametric policies often struggle to represent the multi-modality of the solutions. Though diffusion-based policies are aimed at recovering the multi-modality, they lack an explicit probability density, which complicates policy-gradient optimization. To bridge this gap, we propose MePoly, a novel policy parameterization based on polynomial energy-based models. MePoly provides an explicit, tractable probability density, enabling exact entropy maximization. Theoretically, we ground our method in the classical moment problem, leveraging the universal approximation capabilities for arbitrary distributions. Empirically, we demonstrate that MePoly effectively captures complex non-convex manifolds and outperforms baselines in performance across diverse benchmarks.

cs.LG↗

arXiv2026

Vulnerabilities in Partial TEE-Shielded LLM Inference with Precomputed Noise

The deployment of large language models (LLMs) on third-party devices requires new ways to protect model intellectual property. While Trusted Execution Environments (TEEs) offer a promising solution, their performance limits can lead to a critical compromise: using a precomputed, static secret basis to accelerate cryptographic operations. We demonstrate that this mainstream design pattern introduces a classic cryptographic flaw, the reuse of secret keying material, into the system's protocol. We prove its vulnerability with two distinct attacks: First, our attack on a model confidentiality system achieves a full confidentiality break by recovering its secret permutations and model weights. Second, our integrity attack completely bypasses the integrity checks of systems like Soter and TSQP. We demonstrate the practicality of our attacks against state-of-the-art LLMs, recovering a layer's secrets from a LLaMA-3 8B model in about 6 minutes and showing the attack scales to compromise 405B-parameter LLMs across a variety of configurations.

cs.CR↗

arXiv2026

Lattice QCD Determination of the Collins-Soper Kernel in the Continuum and Physical Mass Limits

The Collins-Soper (CS) kernel governs the rapidity evolution of transverse-momentum-dependent (TMD) parton distributions, a cornerstone for QCD factorization and linking nucleon structure data across scales. Its nonperturbative behavior at large transverse separations ($b_{\perp}$) remains weakly constrained due to phenomenological model dependencies. We present a first-principles determination of the CS kernel at the continuum limit and physical pion mass from lattice QCD in the large-momentum effective theory framework. Using (2+1)-flavor configurations (lattice spacings $a \in[0.052, 0.105]$ fm, and pion mass $m_π \approx ( 136, 230, 300, 320)$ MeV), we simulating the nonlocal equal-time correlation function and extract the quasi-TMD wave functions. Taking into account systematic improvements including hypercubic smearing, nonperturbative renormalization, and a $b_{\perp}$-unexpanded matching kernel, we obtain the CS kernel at the continuum, chiral, and infinite-momentum limits. Our results are determined up to $b_{\perp} \sim 1$ fm, with controllable uncertainties, and agree with perturbative QCD at small $b_{\perp}$ and global TMD phenomenological extractions. We conduct a global analysis integrated with phenomenological fits and demonstrate the impact of our results on such fits. This work yields the most precise nonperturbative constraint on the CS kernel's long-distance behavior from Lattice QCD, which not only bridges Lattice QCD, perturbation theory, and nucleon structure experiments for TMD studies, but also boosts the utility of our constraint for future global TMD analyses.

hep-lat↗

arXiv2025

Scalar-hairy AdS Black Hole in the Einstein-Maxwell-Scalar Theory: first-order phase transition with a critical point

In asymptotically anti-de Sitter (AdS) spacetime, we consider a real massiver scalar field in the Einstein-Maxwell-scalar (EMS) model and examine both scalar-hairy black hole solutions induced by the nonminimal coupling to the Maxwell field and tachyonic-hairy solutions driven by the scalar potential. When the scalar potential vanishes, scalar-hairy black holes emerge with profiles and properties similar to those observed in flat spacetime. The presence of the scalar potential additionally induces tachyonic-hairy solutions, leading to the coexistence of these two distinct hairy phases in different regions of the parameter space. The phase diagram reveals a first-order phase transition line between the tachyonic-hairy and scalar-hairy phases, originating at a critical point in the extreme temperature and chemical potential regime. Our detailed analysis shows that this phase transition is directly associated with the self-overlap region of the scalar-hairy phase and its start point. Moreover, increasing the coupling strength $λ$ shifts the critical point to higher temperature and chemical potential.

gr-qc↗

arXiv2025

Multidimensional Stochastic Dominance Test Based on Center-outward Quantiles

Stochastic dominance (SD) provides a quantile-based partial ordering of random variables and has broad applications. Its extension to multivariate settings, however, is challenging due to the lack of a canonical ordering in $\mathbb{R}^d$ ($d \ge 2$) and the set-valued character of multivariate quantiles. Based on the multivariate center-outward quantile function in Hallin et al. (2021), this paper proposes new first- and second-order multivariate stochastic dominance (MSD) concepts through comparing contribution functions defined over quantile contours and regions. To address computational and inferential challenges, we incorporate entropy-regularized optimal transport, which ensures faster convergence rate and tractable estimation. We further develop consistent Kolmogorov-Smirnov and Cramér- von Mises type test statistics for MSD, establish bootstrap validity, and demonstrate through extensive simulations good finite-sample performance of the tests. Our approach offers a theoretically rigorous, and computationally feasible solution for comparing multivariate distributions.

stat.ME↗

arXiv2025

CHyLL: Learning Continuous Neural Representations of Hybrid Systems

Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffers from the combination of mode switching and discontinuities in the flows. In this work, we propose CHyLL (Continuous Hybrid System Learning in Latent Space), which learns a continuous neural representation of a hybrid system without trajectory segmentation, event functions, or mode switching. The key insight of CHyLL is that the reset map glues the state space at the guard surface, reformulating the state space as a piecewise smooth quotient manifold where the flow becomes spatially continuous. Building upon these insights and the embedding theorems grounded in differential topology, CHyLL concurrently learns a singularity-free neural embedding in a higher-dimensional space and the continuous flow in it. We showcase that CHyLL can accurately predict the flow of hybrid systems with superior accuracy and identify the topological invariants of the hybrid systems. Finally, we apply CHyLL to the stochastic optimal control problem.

cs.LG↗

arXiv2025

Super-robust telecommunications enabled by topological half-supermodes

Topological photonics offers transformative potential for robust integrated waveguide devices due to their backscattering-immune properties. However, their integration faces two fundamental challenges: mode symmetry mismatch with conventional waveguides and prohibitive dimensions. We successfully overcome these two critical challenges by introducing a novel valley-ridge gap waveguide based on topological half-supermode engineering. By strategically hybridizing ridge waveguide modes and valley kink states, we create an exotic odd-symmetric supermode enabling robust propagation and ultra-compact operation. The further implementation of a perfect electric conductor boundary halves lateral dimensions while eliminating radiation loss. Crucially, our proposed valley-ridge interface achieves direct transverse electric mode matching with standard waveguides without transition structures, enabling seamless integration. Experimental results demonstrate reflection losses lower than -15 dB in realistic telecommunication scenarios with super-robust signal propagation through sharp bends. This work innovatively conceptualizes topological half-supermodes and pioneers their practical applications for integrated waveguide devices, establishing a completely new waveguide class that uniquely combines robust backscattering immunity with deep subwavelength compactness.

physics.optics↗