arXiv subjects

Mingyi Liu

Publications and source records attributed to Mingyi Liu.

At least 19 recordsLinked to original sources

arXiv2026

Testing Agentic Service Systems: An Agentic Living Lab Methodology Across the Physical-Digital Continuum

Task success is insufficient for testing agents that act for principals, change persistent business state, and affect physical processes: a useful result may be unauthorized, leave obligations unresolved, or depend on the execution substrate. We propose an Agentic Living Lab methodology organized around three questions: what is the test unit, who and what participate, and under what reality is the test executed? The unit is a service episode containing delegated roles, obligations, world state, and evidence. The method separates agents under test from environment actors and distinguishes real humans, human traces, rule surrogates, and agent-based proxies. Realization Profiles, capability-declared Reality Substrate bindings, Grounding Profiles, and Semantic Delta disclosures make changes across software, enterprise systems, simulation, and physical settings explicit. A four-plane reference architecture separates experiment control, participants, world realization, and evidence evaluation, with cross-cutting authority and governance controls. Runtime semantics distinguish intent, normative admission, and factual reconciliation. Real stakeholder grounding, participant calibration or validation, and governed feedback are required for the Living Lab claim, but not synchronous human participation in every run. Released JSON Schemas, deterministic fixtures, and a limited reference runner make selected rules machine-checkable; they are not a complete platform or empirical validation. Constructed travel and industrial designs illustrate authorization and fulfillment failures, proxy sensitivity, and substrate changes. Agent quality, proxy validity, and cross-substrate effect stability remain empirical questions.

cs.SE↗

arXiv2026

Detecting photons in radiative Bhabha interaction with Si-strip and LYSO crystals at future e+e- colliders

Next-generation e+e-colliders have been proposed for the Super Tau Charm Facility covering center-of-mass energies sqrt(s) = 2-7 GeV, and for the Higgs Factories optimized for Z-pole physics and for HZ production. Luminosity can be determined by measuring forward Bhabha scattering. The QED cross-section calculation has reached a precision of better than 10^-3. Detecting radiative photons in Bhabha events provides a tool for validating higher-order corrections in theory. We propose the Forward Detector for detecting Bhabha scattering events at the STCF. The design uses high-resolution silicon detectors and finely segmented LYSO crystals to discriminate radiative photons from closely associated electrons. Bhabha scattering events are simulated with the BHLUMI and BHWIDE programs, and the detector is modeled using GEANT. Systematic uncertainties due to multiple scattering and bremsstrahlung in the beam pipe were evaluated. The potential for achieving precise measurements of radiative processes in Bhabha events is investigated.

hep-ex↗

arXiv2026

Entanglement-sensitive observables in $e^+e^- \to τ^+τ^-$ at STCF: a detector-level feasibility study

Quantum entanglement in $τ^+τ^-$ production provides a direct probe of non-classical spin correlations in a relativistic quantum system, with the $τ$ decay products serving as spin analyzers. We investigate the detector-level feasibility of such measurements in $e^+e^-\toτ^+τ^-$ at the Super Tau-Charm Facility (STCF) at $\sqrt{s}=7$ GeV, using the $ρρ$ and $πρ$ decay channels. Signal and dominant background processes are simulated with the full STCF detector and reconstruction framework. Dedicated reconstruction and event-selection procedures yield overall signal efficiencies of approximately $4.5\%$ and $4.8\%$, with corresponding purities of about $87.6\%$ and $93.2\%$ for the $ρρ$ and $πρ$ channels, respectively. From the reconstructed decay kinematics, the $τ^+τ^-$ spin-density matrix is inferred and the concurrence $C[ρ]$ together with the Bell-sensitive quantity $m_{12}[C]$ are evaluated in a selected fiducial region. For the benchmark requirement $|\cosθ|<0.2$ and an integrated luminosity of $1~\mathrm{ab}^{-1}$, the projected relative precision on $C[ρ]$ is about $2.2\%$ in both channels, while that on $m_{12}[C]$ is approximately $1.4\%$ for $ρρ$ and $1.5\%$ for $πρ$, including statistical and detector-resolution uncertainties. The reconstructed $m_{12}[C]$ shows a larger separation from its reference threshold in the $ρρ$ channel, making this topology particularly sensitive to Bell-type spin correlations. These results demonstrate the detector-level feasibility of probing quantum entanglement and non-classical spin correlations in hadronic $τ$ decays at STCF.

hep-ex↗

arXiv2026

Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter

Although aerial-legged robots offer combined agility and efficiency, controlling high-speed hopping under complex hybrid dynamics is challenging. Reinforcement Learning (RL) is promising but prone to energy-inefficient "reward hacking". We propose a Dynamics-Informed RL framework for a monopedal hopping quadcopter. By embedding a target Specific Energy into the reward, we constrain the optimization to a physically viable energy manifold, ensuring stable hopping behaviour. By rewarding the phase-consistent behavior, it can encourage bio-inspired stance-phase impulse. Furthermore, penalizing the electro-mechanical power waste induces the motors generate an efficient impulse. This enables the policy to inject energy strictly during spring restitution without heuristic state machines. MuJoCo simulations validate robust height regulation and forward velocity tracking up to 2.0 m/s despite severe attitude-contact coupling. Ultimately, our approach yields a highly agile hopping gait, reducing energy consumption by 82% and 73% compared to hovering baselines and inefficiency baseline, respectively.

cs.RO↗

arXiv2026

Learning to Exploit Passive Dynamics for Energy-Efficient Target Hopping of a Spring-Legged Quadcopter

Combining aerial thrust with spring-loaded hopping makes monopedal quadcopters promising for locomotion over complex terrain, but heuristic proportional-integral-derivative (PID) tuning limits coordination between active thrust and passive contact dynamics. We present a direct estimated-state-to-motor Proximal Policy Optimization (PPO) policy that commands four motors without an explicit hopping state machine or low-level attitude PID. Its reward combines Energy-Manifold Shaping for mass-normalized vertical-energy tracking and apex-state anchoring with Efficiency Shaping, which uses a history-aware power estimator to penalize general power use, impose an additional airborne-power cost, and penalize airborne near-stationarity. In representative hardware runs, the PPO-based control stack reduced cycle-averaged measured electrical power by 30.7% and mean total normalized thrust by 49.8% relative to the tuned PID-based control stack, while retaining repeatable commanded-height hopping and more concentrated landings. These observations are consistent with improved use of passive dynamics and reduced measured electrical demand.

cs.RO↗

arXiv2026

Software Engineering in the Agent Era From Trustworthy Change to Human Agent Software Organizations

Software agents make digital execution elastic: repository analysis, code generation, testing, migration, tool use, and operations can be replicated and parallelized without proportional human headcount. Problem framing, semantic commitment, verification, integration, attention, and residual-risk acceptance remain bounded by human cognition, organizational authority, and economic capacity. How should scalable execution be governed so organizations can accept and sustain its changes? Our testable framework has two constructs and one execution abstraction. Trustworthy Change (TC) is the engineering object moving from intent through delegated execution, verification, integration, acceptance, and operation. Responsibility Topology classifies organizations by the distribution of independent residual-risk acceptance authority. A single-center topology has one final baseline responsibility anchor; a multi-anchor topology requires joint acceptance across independently governed domains. The Human-Agent Cell (HAC) produces candidates, proposals, and evidence; execution grants no acceptance authority. As execution and authority scale differently, distributed HACs create context-coherence and invalidation pressures, while multi-anchor governance adds joint acceptance and explicit responsibility closure. Responsibility, accountability, change management, specification, verification, and human oversight predate this work; our claim is only that agent-scaled execution changes how they fit together. We make that authority an explicit classification axis and derive consequences for change state, shared engineering facts, verification, and flow control. Progressive Specification and bounded-capacity analysis remain hypotheses to test, not laws. We contribute theory construction and operationalization; empirical validity remains open to controlled, longitudinal, and field studies.

cs.SE↗

arXiv2026

Prediction-Robust Service Deployment with Capacity-Aware Edge Admission

Edge platforms instantiate executable services close to users to reduce request-serving cost, but each instance incurs a one-time deployment cost and remains useful only for a finite time-to-live (TTL). The resulting online decision is both prediction-sensitive and capacity-coupled: an optimistic forecast can waste deployment cost, whereas a delayed decision misses the burst it is intended to serve. We study this problem under a common TTL cost model and propose CAPSUM, a capacity-aware admission policy with an elastic specialization, CAPSUM-E. In the local elastic setting, every node-service trace is exactly a variable-price Bahncard instance. This reduction lets CAPSUM-E inherit PFSUM's tight prediction-error-dependent ratio, including $2/(1+β)$ consistency and $1/β$ robustness for $β>0$. A redirect-aware variant preserves the same local deployment schedule. For finite-capacity nodes, CAPSUM combines size-scaled break-even tests, a utilization-dependent shadow price, and evidence-density eviction; we prove capacity feasibility, scale invariance, and exact agreement with CAPSUM-E under an elastic configuration. We implement an exact local offline dynamic program and compare against direct common-model baselines and documented source-derived adapters for EDP-A, OREO, and uEDC-L. Experiments cover controlled prediction error, three synthetic demand regimes, a causal predictor on a public Globus Compute trace, and joint scaling to 1,024 nodes and 10,000 services. Under the common model, CAPSUM reduces normalized cost by 33.7-42.9% relative to the best source-derived adapter across the synthetic regimes and by 45.5% on the sampled trace.

cs.DC↗

arXiv2026

Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence

The convergence of artificial intelligence, cyber-physical systems, and distributed networking has accelerated the evolution of industrial intelligence across edge, cloud, and cross-organizational communication environments. However, existing governance mechanisms remain fragmented across data management, service orchestration, and knowledge-based decision-making, making it difficult to ensure reliability, accountability, compliance, and explainability throughout the industrial intelligence stack. To address this gap, we present TRISK (TRusted Industrial Data-Service-Knowledge governance), a conceptual and taxonomic framework for trustworthy industrial intelligence. TRISK is grounded in a five-dimensional trust model covering quality, security, privacy, fairness, and explainability, and formalizes how trust is constructed, propagated, aggregated, and fed back across data, service, and knowledge layers in networked industrial systems. Through a structured synthesis of more than 100 representative studies, standards, and technical reports, we examine data governance as the foundation of trust construction, service governance as the mediation layer for trustworthy execution, and knowledge governance as the semantic anchor for reasoning, validation, and feedback adaptation. We further discuss industrial implementation patterns, cross-industry implications, and the role of emerging communication and computing technologies. Finally, we outline a future research roadmap toward adaptive, verifiable, and human-aligned industrial governance for Industry 5.0.

cs.CE↗

arXiv2026

Dynamics, stability, and energy efficiency of an energy-recycling rimless wheel with spring-clutch legs

This paper proposes an energy-recycling rimless wheel with spring-clutch legs. The proposed mechanism uses a lockable clutch to store part of the impact-induced elastic energy after foot contact and reinject it in the next gait cycle. First, we develop a hybrid dynamic model of the energy-recycling rimless wheel. Second, numerical simulations are used to examine the dynamics, local stability of periodic gaits, and the Cost of Transport (CoT) of the proposed mechanism. The simulation results show that the proposed mechanism reduces the CoT by up to 16.13% compared with a benchmark viscoelastic-legged rimless wheel with telescopic spring-damper legs. Compared with the rigid rimless wheel, the viscoelastic-legged and energy-recycling models reduce the CoT by more than 50%. The energy-recycling model also maintains locally stable periodic gaits over the tested slope and stiffness ranges. Finally, prototype experiments on an inclined plane are conducted to examine the feasibility of the proposed mechanism. The experimental results show that the proposed rimless wheel achieves passive walking on a shallow 1° slope, corresponding to a CoT of approximately 0.02. These results suggest that the proposed spring-clutch mechanism can improve the simulated walking efficiency of the energy-recycling rimless wheel, while the prototype experiments support the feasibility of passive walking with the mechanism.

cs.RO↗

arXiv2026

Propeller-Assisted Robust 3D Hopping Robot with Hierarchical Force Allocation

Monopedal hopping robots are conceptually simple but highly dynamic and inherently unstable. Achieving robust 3D hopping is still difficult because ground reaction forces are available only during the short stance phase, while the robot is underactuated in flight. A key unresolved issue is how to improve flight-phase control authority. Propeller assistance provides a promising solution, but it requires careful coordination of leg-generated contact forces and propeller thrusts across stance and flight. This paper presents Pro-OMEGA2, a propeller-assisted 3D monopedal hopping robot with an active 3-RSR parallel leg and a trunk-mounted tri-rotor for auxiliary attitude regulation. To address the force coordination challenge, we propose a Hierarchical Force Allocation (HFA) framework based on a single rigid body (SRB) model. The leg generates the main stance contact wrench, while the tri-rotor provides auxiliary attitude regulation, compensating the residual attitude moment in stance and maintaining attitude during flight. Real-robot experiments in indoor and outdoor scenarios demonstrate sustained 3D hopping, including terrain transitions and impulsive push recovery, validating robustness under unmodeled contact and external disturbances.

cs.RO↗

arXiv2026

Probing Quantum Entanglement in $τ^+τ^-$ Pairs via the $ππ$ Channel at STCF

Quantum entanglement and Bell-inequality violation in $τ^+τ^-$ pairs provide a sensitive probe of quantum correlations in high-energy interactions. We present a feasibility study of $e^+e^- \to τ^+τ^-$ at the proposed Super Tau-Charm Facility based on full Monte Carlo simulation at $\sqrt{s} = 7$ GeV, focusing on the $ππ$ channel ($τ^\pm \to π^\pmν$), which offers the maximal spin-analyzing power $|κ| = 1$ and the simplest final-state topology for validating the quantum-tomography framework. We establish a consistency chain from the tree-level QED prediction through truth-level and detector-level reconstruction, yielding a reconstructed concurrence of $0.279 \pm 0.007$ with the good-solution approach. A complementary full-simulation study of the $ρρ$ channel is also briefly reported. These results demonstrate that the STCF can provide a competitive platform for precision studies of quantum correlations in $τ$-lepton pairs.

hep-ex↗

arXiv2026

The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation

RLHF-aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40-79% of questions produce a single semantic cluster across 10 i.i.d. samples. On affected questions, sampling-based uncertainty methods have zero discriminative power (AUROC=0.500), while free token entropy retains signal (0.603). This alignment tax is task-dependent: on GSM8K (n=500), token entropy achieves 0.724 (Cohen's d=0.81). A base-vs-instruct ablation confirms the causal role of alignment: the base model shows 1.0% single-cluster rate vs. 28.5% for the instruct model (p < 10^{-6}). A training stage ablation (Base 0.0% -> SFT 1.5% -> DPO 4.0% SCR) localizes the cause to DPO, not SFT. Cross-family replication on four model families reveals alignment tax severity varies by family and scale. We validate across 22 experiments, 5 benchmarks, 4 model families, and 3 model scales (3B-14B), with Jaccard, embedding, and NLI-based baselines at three DeBERTa scales (all ~0.51 AUROC). Cross-embedder validation with two independent embedding families rules out coupling bias. Cross-dataset validation on WebQuestions (58.0% SCR) confirms generalization beyond TruthfulQA. The central finding -- response homogenization -- is implementation-independent and label-free. Motivated by this diagnosis, we explore a cheapest-first cascade (UCBD) over orthogonal uncertainty signals. Selective prediction raises GSM8K accuracy from 84.4% to 93.2% at 50% coverage; weakly dependent boundaries (|r| <= 0.12) enable 57% cost savings.

cs.LG↗

arXiv2025

OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance

Accurate and timely prediction of tool conditions is critical for intelligent manufacturing systems, where unplanned tool failures can lead to quality degradation and production downtime. In modern industrial environments, predictive maintenance is increasingly implemented as an intelligent service that integrates sensing, analysis, and decision support across production processes. To meet the demand for reliable and service-oriented operation, we present OmniFuser, a multimodal learning framework for predictive maintenance of milling tools that leverages both visual and sensor data. It performs parallel feature extraction from high-resolution tool images and cutting-force signals, capturing complementary spatiotemporal patterns across modalities. To effectively integrate heterogeneous features, OmniFuser employs a contamination-free cross-modal fusion mechanism that disentangles shared and modality-specific components, allowing for efficient cross-modal interaction. Furthermore, a recursive refinement pathway functions as an anchor mechanism, consistently retaining residual information to stabilize fusion dynamics. The learned representations can be encapsulated as reusable maintenance service modules, supporting both tool-state classification (e.g., Sharp, Used, Dulled) and multi-step force signal forecasting. Experiments on real-world milling datasets demonstrate that OmniFuser consistently outperforms state-of-the-art baselines, providing a dependable foundation for building intelligent industrial maintenance services.

cs.AI↗

arXiv2024

A Hybrid BPMN-DMN Framework for Secure Inter-organizational Processes and Decisions Collaboration on Permissioned Blockchain

In the rapidly evolving digital business landscape, organizations increasingly need to collaborate across boundaries to achieve complex business objectives, requiring both efficient process coordination and flexible decision-making capabilities. Traditional collaboration approaches face significant challenges in transparency, trust, and decision flexibility, while existing blockchain-based solutions primarily focus on process execution without addressing the integrated decision-making needs of collaborative enterprises. This paper proposes BlockCollab, a novel model-driven framework that seamlessly integrates Business Process Model and Notation (BPMN) with Decision Model and Notation (DMN) to standardize and implement collaborative business processes and decisions on permissioned blockchain platforms. Our approach automatically translates integrated BPMN-DMN models into smart contracts(SCs) compatible with Hyperledger Fabric, enabling privacy-aware multi-organizational process execution through blockchain-based Attribute-Based Access Control (ABAC). The framework introduces three key innovations: (1) a standardized method for modeling collaborative processes and decisions using integrated BPMN-DMN model, (2) an automated SC generator that preserves both process logic and decision rules while maintaining privacy constraints, and (3) a hybrid on-chain/off-chain execution environment that optimizes collaborative workflows through secure data transfer and external system integration. Experimental evaluation across 11 real-world collaboration scenarios demonstrates that our approach achieves 100\% accuracy in process execution. Furthermore, an analysis of various execution processes highlights the strong practical applicability and reliability of our approach. The proposed framework includes an open-source third-party collaboration platform based on blockchain.

cs.SE↗

arXiv2024

Design and Experimental Study of Vacuum Suction Grabbing Technology to Grasp Fabric Piece

Vacuum Suction Grabbing Technology. The primary objective of this study was to design the grabbing technique used to determine the vacuum suction gripper and its design parameters for the pocket welting operation in apparel manufacturing. It presents the application of vacuum suction in grabbing technology, a technique that has revolutionized the handling and manipulation to grasp the various fabric materials in a range of garment industries. Vacuum suction, being non-intrusive and non-invasive, offers several advantages compared to traditional grabbing methods. It is particularly useful in scenarios where soft woven fabric and air-impermeable fabric items need to be handled with utmost care. The paper delves into the working principles of vacuum suction, its various components, and the underlying physics involved. Furthermore, it explores the various applications of vacuum suction in the garment industry into the automation exploration. The paper also highlights the challenges and limitations of vacuum suction technology and suggests potential areas for further research and development.

cs.RO↗

arXiv2024

I Experienced More than 10 DeFi Scams: On DeFi Users' Perception of Security Breaches and Countermeasures

Decentralized Finance (DeFi) offers a whole new investment experience and has quickly emerged as an enticing alternative to Centralized Finance (CeFi). Rapidly growing market size and active users, however, have also made DeFi a lucrative target for scams and hacks, with 1.95 billion USD lost in 2023. Unfortunately, no prior research thoroughly investigates DeFi users' security risk awareness levels and the adequacy of their risk mitigation strategies. Based on a semi-structured interview study (N = 14) and a follow-up survey (N = 493), this paper investigates DeFi users' security perceptions and commonly adopted practices, and how those affected by previous scams or hacks (DeFi victims) respond and try to recover their losses. Our analysis shows that users often prefer DeFi over CeFi due to their decentralized nature and strong profitability. Despite being aware that DeFi, compared to CeFi, is prone to more severe attacks, users are willing to take those risks to explore new investment opportunities. Worryingly, most victims do not learn from previous experiences; unlike victims studied through traditional systems, DeFi victims tend to find new services, without revising their security practices, to recover their losses quickly. The abundance of various DeFi services and opportunities allows victims to continuously explore new financial opportunities, and this reality seems to cloud their security priorities. Indeed, our results indicate that DeFi users' strong financial motivations outweigh their security concerns - much like those who are addicted to gambling. Our observations about victims' post-incident behaviors suggest that stronger control in the form of industry regulations would be necessary to protect DeFi users from future breaches.

cs.CR↗

arXiv2023

Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network

Temporal graph neural network has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal graph neural networks that achieve remarkable results. However, these works focus on future event prediction and are performed under the assumption that all historical events are observable. In real-world applications, events are not always observable, and estimating event time is as important as predicting future events. In this paper, we propose MTGN, a missing event-aware temporal graph neural network, which uniformly models evolving graph structure and timing of events to support predicting what will happen in the future and when it will happen.MTGN models the dynamic of both observed and missing events as two coupled temporal point processes, thereby incorporating the effects of missing events into the network. Experimental results on several real-world temporal graphs demonstrate that MTGN significantly outperforms existing methods with up to 89% and 112% more accurate time and link prediction. Code can be found on https://github.com/HIT-ICES/TNNLS-MTGN.

cs.LG↗

arXiv2021

Data Correction and Evolution Analysis of the ProgrammableWeb Service Ecosystem

The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.

cs.SE↗