arXiv subjects

Cheng Liu

Publications and source records attributed to Cheng Liu.

At least 19 recordsLinked to original sources

arXiv2026

PingpongDuo: Agile Humanoid Table Tennis Doubles with Safe Team Coordination

Unlike singles, humanoid table tennis doubles requires teammates to strike in strict alternation while sharing a confined workspace. Rapid repositioning can compromise striking accuracy, while uncoordinated teammate movements introduce collision risks. We present PingpongDuo, a hierarchical framework for agile humanoid doubles with safe team coordination. At the team level, a planner jointly selects positioning targets and uses learned risk estimates to filter risky proposals, preferentially preserving the current striker's target. At the individual level, separate footwork and striking skills combine agile movement with precise racket control. The skills are guided by human motion references, including footwork collected from human doubles play, and trained from states produced by one another to support rapid transitions. A racket-arm residual further refines strikes after movement using return feedback from a simulator calibrated with real ball-flight and contact measurements. In simulation, PingpongDuo improves the ball-return success rate by 13.2% over the state-of-the-art baseline, while achieving approximately 2 cm positioning error during clearance. In real-world experiments, a robot completes a 2.15 m clearance-and-recovery cycle in 2.4 s with a peak speed of 2.23 m/s, while two robots sustain more than 10 consecutive strikes in strict alternation.

cs.RO↗

arXiv2026

Survivor Set of Gauss Map with a Hole at $1$

Let $T$ be the Gauss map. For each $α\in [0,1]$, let $E(α)$ be the survivor set consisting of all $x\in [0,1]$ whose orbit $\{T^k(x):k\ge 0\}$ never enters the interval $(α,1]$. We prove that the set-valued function $α\mapsto E(α)$ is increasing and locally constant almost everywhere. Moreover, we explicitly characterize all intervals where the function $α\mapsto E(α)$ remains constant. We also describe the points at which the map $α\mapsto E(α)$ is not locally constant. Further, we obtain the Hausdorff dimension of $E(α)$ for all $α\in [0,1]$. We prove that the function $α\mapsto \dim_H(E(α))$ forms a devil's staircase on $(0,1]$, but is not continuous at $0$. This discontinuity implies that the result of Raith [Acta Math. Univ. Comenian. 63 (1994), 39-53] fails for piecewise monotone interval maps with infinitely many branches. Finally, we give an application quantifying the set of points sharing identical $α$-continued fraction expansion and regular continued fraction expansion.

math.DS↗

arXiv2026

NAS Timers for Non-Terrestrial Networks: Current Standards and Directions

Response-supervision timers in the 5G Non-Access Stratum (NAS) bound the time allowed for signaling exchanges before retransmission or abnormal-case handling begins. In non-terrestrial networks, that interval can include satellite propagation, terrestrial transport, and processing by multiple core-network functions. Under current 3GPP specifications, the applicable timer value for satellite access is selected according to the satellite NG-RAN radio access technology (RAT) type. However, control paths within the same low Earth orbit (LEO) network can differ in propagation delay and processing load. This article reviews the relevant 3GPP timer semantics and traces the delay components covered by different timers through 5G registration. It organizes candidate refinements along two dimensions: configuration granularity and information scope. A registration case study illustrates the trade-off between avoiding premature timer expiry and recovering promptly from message loss. The article identifies the information and coordination that future specifications would need to address to support interoperable timer configuration.

cs.NI↗

arXiv2026

UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy

Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extraction, enabling the policy and skillbank to co-evolve. However, as the actor continues learning, rewarding skill proposals through their reuse in subsequent training steps may conflate skill benefits with actor improvement, while directly testing each proposed skill requires costly additional actor rollouts. In this paper, we introduce UniSkill, which uses a shared policy to interact with the environment and propose skillbank edits (Add, Update, or No Edit) from the resulting trajectories. Specifically, the actor learns from environment rewards, while contrastive action feedback guides skill proposal learning. This feedback provides an actor-alignment signal by measuring how replacing the retrieved skill with a proposed skill changes the current actor's action log-likelihood gap between previously collected successful and failed trajectories from the same task, thereby avoiding new rollouts for each proposal. Since proposal-level feedback may suppress an otherwise appropriate edit operation when the proposed skill content scores poorly, we further apply skill-edit support regularization to preserve exploration. Empirically, UniSkill achieves strong performance, reaching 98.4% success on ALFWorld and 84.7% on WebShop while maintaining stable joint training. Further ALFWorld experiments show that UniSkill remains effective when the shared policy uses a smaller backbone. Our implementation is available at https://github.com/LimOkii/UniSKill.

cs.AI↗

arXiv2026

CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection

Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful attempts without useful correction. We introduce CoEvoke, a cross-domain framework that evokes these interactions during trajectory collection. Controlled perturbations create recoverable difficulties, while restricted reference-based guidance directs investigation. The resulting dataset contains 4,567 reconstructable trajectories over 4,130 software-engineering and reasoning tasks, linking public interactions to evidence, decisions, and task-native outcomes. Separate experiments over 2,287 same-task pairs show feedback increases of 47.72-100 percentage points across seven perturbation groups; guidance has smaller, model-dependent effects. With identical training tasks, decision-record counts, and updates, perturbation-enriched judge training improves ACPBench-Hard success from 35.38% to 42.12% (+6.73 points). SWE verifier training gains 7.60-9.40 points over interface-matched untrained references. Workflow and post-training evaluations preserve task-solving topology with unperturbed inputs and no private-reference guidance. These results support designing collection conditions to obtain corrective supervision that improves collaborative decisions with frozen solving agents.

cs.LG↗

arXiv2026

Incommensurate structural and magnetic modulations in potassium-rich cryptomelane, K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$)

Cryptomelane is a hollandite-like material consisting of K$^+$ cations in an $α$-MnO$_2$ tunnel-like crystallographic motif. Cryptomelane with stoichiometry K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$) has been synthesized and its magnetic properties investigated using variable-temperature magnetic susceptibility, heat capacity, and neutron powder diffraction. Three distinct transitions at $T_1=184$\,K, $T_2=54.5$\,K, and $T_3=24$\,K are observed. At $T_1$ there is a subtle tetragonal$\rightarrow$monoclinic transition associated with emergence of a set of non-magnetic superstructure peaks indexable to a $\vec{k}_\mathrm{struc}\approx0.74\vec{c^*}$ incommensurate modulation parallel to the $α$-MnO$_2$ tunnels. Our findings are consistent with a relation previously reported in titanate hollandites, that $x\approx2|\vec{k}_\mathrm{struc}|$. Magnetic Bragg peaks emerge below $T_2=54.5$\,K, and their positions indicate an incommensurate modulated magnetic structure. The model consistent with the data is a dual-$\vec{k}_\mathrm{mag}$ structure with a ferromagnetic $|\vec{k}_\mathrm{mag}|=0$ component and an incommensurate $\vec{k}_\mathrm{mag}\approx0.37\vec{c^*}$, with the latter most likely to be helical. The period of oscillation of the incommensurate magnetic component is in line with predictions based on a Heisenberg spin Hamiltonian [Mandal \textit{et al}. Phys. Rev. B 90, 104420 (2014)]. Below $T_3=24$\,K, there is a magnetic transition, which gives rise to a different set of magnetic Bragg peaks indicative of a highly complex magnetic structure.

cond-mat.mtrl-sci↗

arXiv2026

Mass Distribution of M31 from its Rotation Curve up to 167 kpc

The mass distribution of the Andromeda galaxy (M31) is fundamental for understanding its dark matter content, assembly history, and dynamical role within the Local Group. However, estimates of M31's total mass remain quite uncertain, partly because of limited dynamical constraints at large galactocentric radii. Moreover, M31's recent merger history has produced abundant tidal substructures and may have left its outer halo out of dynamical equilibrium, complicating equilibrium-based mass estimates. We use over 8,500 tracers with line-of-sight velocities to constrain the rotation curve (RC) of M31 over R=2--167 kpc. In the disk, we derive the RC from a clean tracer sample using the Jeans equation and find good agreement with previous H I measurements. In the halo, we use Gaussian mixture modeling to account for tidal substructures and Galactic foreground contamination and constrain the kinematics of the residual dynamically hot halo. Assuming this halo population can be treated as an equilibrium tracer, we infer the outer RC and constrain M31's mass distribution for different velocity anisotropies beta and dark matter halo profiles. The NFW and Dekel--Zhao (DZ) models yield broadly consistent total masses ranging from 1.12(+0.11/-0.10) to 1.63(+0.21/-0.17)x10^12 Msun for beta=0.3--0.7. In contrast, the Einasto profile favors substantially lower masses of 0.41(+0.09/-0.06) to 0.86(+0.21/-0.16)x10^12 Msun, highlighting significant dependence on the adopted halo profile, although Einasto solutions are more in tension with independent mass estimates based on M31 satellite galaxies than the NFW and DZ models at fixed beta. Overall, our analysis provides a comprehensive measurement of M31's mass distribution under the assumption of dynamical equilibrium, while also quantifying the systematic uncertainties associated with halo velocity anisotropy and the choice of dark matter density profile.

astro-ph.GA↗

arXiv2026

$P$-adic $s$-energy and Fourier dimension

We extend the theory of $s$-energy to $\mathbb{Q}_p^n$. We give an exact representation for the $s$-energy in terms of the Fourier coefficients of a finite Borel measure with support in $\mathbb{Z}_p^n$, which is stronger than the approximate representation in $\mathbb{R}^n$. We also compute the Fourier dimension of the $p$-adic Riesz product measure, even in the case $p=2$, which has never been studied before.

math.CA↗

arXiv2026

GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND)

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses.

astro-ph.IM↗

arXiv2026

Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents

Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on live websites while recovering from mistakes and navigating complex UIs. We argue that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone. We present Wuying-Browser-Agent, a unified framework that addresses each of these levels. A structured browser harness provides stable execution primitives and decision-oriented context management. Reflection and UI-specialized Curriculum SFT (RUIC-SFT) explicitly trains on recovery trajectories and complex-UI interactions. Divergence-Aware Online GRPO (DAO-GRPO) improves long-horizon credit assignment through potential-based reward shaping and divergence-aware step weighting. Finally, we introduce BrowserBench, a bilingual real-web benchmark of 350 tasks averaging 37.9 steps, because most existing benchmarks are too short to expose long-horizon failure modes. Wuying-Browser-Agent-27B achieves 80.6\% on WebVoyager, 66.7\% on Online-Mind2Web, and 65.1\% on BrowserBench, establishing a new open-source state of the art on browser-use benchmarks. The same pipeline also transfers beyond browser use, demonstrating strong general agentic ability and reaching an average score of 73.8 on Tau2-Bench, Claw-Eval, and BFCL-v4.

cs.AI↗

arXiv2026

Reshaping coupled bosonic networks: A bipartite-graph framework for optimal quantum excitation transfer

Highly efficient transfer of quantum resources including quantum excitations, states, and information on coupled bosonic networks is an important task in quantum physics and quantum information science. Here we propose a bipartite-graph framework to characterize quantum excitation transfer in coupled bosonic networks. This is achieved by diagonalizing the intermediate subnetwork between the sender and the receiver to construct a bipartite-graph configuration, and hence this treatment can be understood from the viewpoint of network deformation. We examine the covariance matrix of the coupled bosonic networks in both the original and bipartite-graph representations. In particular, we investigate quantum excitation transfer in both the finite and infinite intermediate-normal-mode cases and show the dependence of the transfer efficiency on the network configurations and system parameters. We find the bounds of maximally transferred excitations for various network configurations and reveal the underlying physical mechanisms. We also discover that the dark-mode effect will degrade the excitation transfer efficiency. Our findings provide a new insight for the design and optimization of networks in physics, information theory, and complex system science.

quant-ph↗

arXiv2026

Knowing When to Ask: Resolving Uncertainty in Human-Robot Joint Planning via Explicit Dialogue and Implicit Intent Cues

Effective human-robot collaboration in open-world environments requires joint planning under uncertainty about the task, the environment, and the human teammate. Communication is the most direct means of resolving such uncertainty, yet most existing systems support only one-way communication: robots listen and act, treating humans as passive supervisors rather than conversational teammates capable of two-way dialogue. We propose a unified human-robot joint planning system in which the robot actively resolves uncertainty through two complementary communication channels. When uncertainty is decision-critical, an uncertainty-mitigation joint planning module engages the human in clarification dialogue: it grounds ambiguous instructions via an LLM-assisted active elicitation mechanism, enumerates traversability hypotheses through a hypothesis-augmented A* search, and computes a cost-optimal querying policy via dynamic programming, so that the robot asks only the questions whose answers actually matter for the plan. When explicit dialogue is unnecessary or impractical, a real-time intent-aware collaboration module instead reads implicit, nonverbal cues, maintaining a probabilistic belief over the human's latent task intent from spatial and directional signals to enable coordination-aware task selection without any communication overhead. We validate the proposed system in both Gazebo simulations and real-world UAV deployments, integrated with a voice dialogue interface and a Vision-Language Model (VLM)-based 3D semantic perception pipeline. Experimental results show that cost-optimal clarification dialogue cuts the interaction cost by 51.9% while maintaining a 100% task success rate, and implicit intent reading reduces the cooperative task execution time by 25.4% compared to the baselines.

cs.RO↗

arXiv2026

Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogeneous nodes through their relative relationships, facilitating the accurate capture of structural patterns and implicit connections. Previous studies derive node positional information as distances to single-granularity landmarks, defined as the centers of homophilic regions, while neglecting the multi-granularity nature of homophilic structures and their hierarchical interrelations. We propose the Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction (MGLP) method to obtain multi-granularity position embedding of graphs. Specifically, MGLP introduces an Adaptive Granular-Ball Graph Refinement mechanism to adaptively refine the graph into homophilic subdomains with optimal levels of granularity. The central nodes within subdomains are treated as landmarks, which form a Hierarchical Central Graph. Moreover, a novel Multi-granularity Hierarchical Distance encoding mechanism is proposed to capture both the homophilic structures within a graph and their hierarchical correlations, improving the discriminative power of nodes. Experimental results demonstrate that the multi-granularity position embedding generated by our method exhibits excellent performance and strong competitiveness compared to baseline algorithms for link prediction. Our codes are available in https://anonymous.4open.science/r/MGLP-D3C5/.

cs.SI↗

arXiv2026

Location-Aware NAS Timer Optimization in NTN-TN Integrated Networks

Efficient Non-Access Stratum (NAS) timer configuration is critical for reliable and energy-efficient Fifth Generation (5G) registration in Non-Terrestrial Network (NTN)-Terrestrial Network (TN) integrated systems, where Low Earth Orbit (LEO) satellite access introduces large registration bursts, heterogeneous propagation paths, and multi-hop satellite routing. Existing 3GPP NAS timers use fixed values, while prior closed-form timer models compute a global timer under network-level assumptions; both fail to capture user equipment (UE)-level differences in propagation delay, Access and Mobility Management Function (AMF) arrival position, and path reliability. In this paper, we propose a location-aware, UE-specific NAS timer optimization method for LEO NTN-TN integrated networks. The proposed method models the path-delay component using service-link geometry, ground-station distance, and Inter-Satellite Link (ISL) hop count, and adapts the endpoint-delay component according to each UE's expected AMF queue exposure and path reliability. Simulation results show that our method reduces registration latency, UE energy consumption, and avoidable registration attempts compared with fixed and global timer configurations, especially when timer over-provisioning causes unnecessary waiting.

cs.NI↗

arXiv2026

Robust logical Bell nonlocality based on quantum error correction codes

Quantum nonlocality based on the violation of Bell-like inequalities constitutes a fundamental feature of quantum physics and drives the development of device-independent (DI) quantum information technologies. Existing studies of Bell nonlocality have mainly focused on physical qubit systems, where the observed nonlocal correlations are directly encoded in physical degrees of freedom. The decoherence sensitivity of Bell nonlocality largely limits the performance and security of its DI applications. Here, we investigate the robust logical Bell nonlocality based on quantum error correction codes. We construct the general logical Bell inequality in the stabilizer coding subspace and prove its violation indicates the global nonlocal feature of the logical system. Then, we indicate that the logical Bell nonlocality is robust against decoherence. Comparing with the physical qubit system, the fidelity thresholds for the logical Bell inequality violation based on the [[3,1,1]] and [[7,1,1]] repetition codes under the bit-flip error model can be reduced from 82.8% to 73.10% and 66.35%, increasing DI QKD's bit-flip noise threshold from 10.64% to 14.42% and 23.36%, respectively. Such stabilizer-based framework can be also used to characterize the multipartite logical Bell nonlocality in principle. Finally, a logical Bell test implementation circuit based on the [[3,1,1]] repetition code is presented. This work provides a feasible avenue for unlocking robust Bell nonlocality in scalable logical quantum systems and facilitates its applications in future scalable quantum network.

quant-ph↗

arXiv2026

Harvesting AI Computation at the Edge via Generic Approximation

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands. However, they are often underutilized and suffer from considerable computational waste due to temporal or spatial redundancy in processing. Conversely, general-purpose processing engines at the edge may struggle with compute-intensive tasks such as signal processing and complex numerical operations because of stringent resource constraints. To address this imbalance, we propose a framework that harvests unused AI computation resources using general-purpose approximation techniques. The core idea is to automatically convert traditional computing tasks into neural network models via a representative neural architecture search (NAS) method. These approximate versions of general-purpose tasks are then deployed on AI engines during their idle periods. Specifically, we introduce a runtime scheduler that offloads these tasks to AI chips without compromising the performance of primary AI workloads, thereby alleviating the burden on general-purpose processors. Experiments on a representative AIoT processor show that our proposed AI computation harvesting strategy delivers substantial performance improvements across a set of edge processing tasks.

cs.AR↗

arXiv2026

VeriPilot: An LLM-Powered Verilog Debugging Framework

Verilog debugging remains one of the most time-consuming stages in digital circuit design. Recent advances in Large Language Models (LLMs) have enabled automated debugging; however, most existing approaches rely solely on test outputs and compiler feedback in an end-to-end manner, limiting their effectiveness on complex bugs. A key challenge is that the root cause of an error may be far removed from its observable outputs, making it difficult for LLMs to trace long dependency chains in code. This challenge is further exacerbated in large codebases, where long context lengths hinder efficient reasoning. To address these limitations, we propose VeriPilot, an LLM-powered debugging framework that leverages golden reference models to enable fine-grained bug localization and repair. VeriPilot goes beyond output-level comparison by aligning internal variable semantics between the Verilog design and its corresponding golden model through LLM-based analysis. It then performs step-by-step signal tracing using Control-Data-Flow Graphs (CDFGs) derived from static analysis, identifying a minimal set of suspicious code regions along with their correct counterparts from the golden model. These structured insights are subsequently provided to the LLM to guide reasoning and automated code repair. Experimental results on the Comprehensive Verilog Design Problems (CVDP) benchmark from NVIDIA demonstrate that VeriPilot improves the repair success rate of GPT-4o from 54.3\% to 85.71\%, significantly enhancing both bug localization accuracy and repair effectiveness for complex Verilog designs. The source code and benchmark are publicly available at Github https://github.com/YihanWn/VeriPilot.git.

cs.AR↗

arXiv2026

ASC-SW: A Lightweight Atrous Strip Convolution Network for DLOs Segmentation on Edge mobile Robots

Detecting deformable linear objects (DLOs), such as floor cables, is essential for safe mobile robot navigation but remains challenging due to oblique viewpoints, thin structures, and limited edge-device resources. Existing DLO segmentation methods are primarily designed for manipulator platforms with fixed top-down views and often require heavy models, limiting their deployment on mobile robots. We formulate a cross-view DLO segmentation problem, where models trained on manipulator-view data must generalize to mobile robot perspectives. To address this, we propose ASC-SW, a lightweight and geometry-aware segmentation framework. The core network, ASCNet, introduces Atrous Strip Convolution, combining directional strip filtering with dilated receptive fields to enhance sensitivity to elongated structures at low computational cost. An Atrous Strip Convolution Spatial Pyramid Pooling module enables multi-scale anisotropic feature aggregation, while a temporal Sliding Window refinement suppresses viewpoint-induced false positives. Evaluated on real-world mobile robot data, ASC-SW achieves 74.1% mIoU at 261 FPS and remains deployable on edge devices.

cs.RO↗