arXiv subjects

Hao Hu

Publications and source records attributed to Hao Hu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Twin reflections from a moving space-time boundary

An interluminal interface, a space-time boundary propagating at a velocity between the group velocities of the surrounding media, enables extraordinary wave phenomena such as nonreciprocal amplification and analogues of Hawking radiation. A particularly intriguing prediction is that such an interface splits an incident wave into three outgoing waves: one transmitted and two reflected. Yet, despite decades of theoretical study, this triple-wave scattering has remained experimentally unobserved, owing to stringent requirements on interface velocity and modulation speed. Here, we introduce a programmable spatiotemporal microstrip transmission-line platform that realizes step-modulated interluminal interfaces with controlled velocity. Using this system, we report the direct observation of bi-reflection from an interluminal interface, confirming the emergence of two distinct reflected waves alongside a transmitted one. Measured frequencies and scattering coefficients show excellent agreement with longstanding theoretical predictions. Furthermore, we reveal that the two reflections possess fundamentally different causal symmetries: one is spatially inverted, while the other is time-reversed. These findings resolve a half-century-old puzzle in moving-boundary electrodynamics and establish a versatile experimental platform for studying wave interaction with dynamic interfaces. Our work opens pathways to velocity-independent scattering devices, broadband frequency conversion, and advanced spatiotemporal wave engineering.

physics.optics↗

arXiv2026

Face Structure in Partial Facial Reduction of Semidefinite Relaxations of Binary Programs

Semidefinite relaxations of binary optimization problems often fail Slater's condition. Full facial reduction restores strict feasibility but may require auxiliary problems as costly as the original relaxation. We analyze partial facial reduction based on tractable inner approximations of the positive semidefinite cone: the nonnegative diagonal, diagonally dominant (DD), and scaled diagonally dominant (SDD) cones. For this class of SDP relaxations, we prove that every terminal face identified by SDD-partial FR is described by variables fixed to zero or one and groups of variables forced to be equal. The face admits a full-column-rank $\{0,1\}$ basis matrix with disjoint column supports, yielding a natural reduced formulation that can preserve sparsity. For polyhedral inner approximations, we characterize the face exposed at each step through the inequalities active on the associated outer relaxation. We also establish sharp limitations. We construct a family with singularity degree two for which DD- and SDD-partial FR require the maximum possible number of steps. We further show that factor-width restrictions can prevent detection of dense valid affine equalities. These results characterize both the structure recovered by tractable partial FR and the cost of restricting the exposing-matrix search.

math.OC↗

arXiv2026

On the Singularity Degree of Shor Relaxations for 0-1 Programs

Shor relaxations are standard tools for nonconvex quadratic optimization, but they may fail Slater's condition. The singularity degree quantifies this degeneracy by the number of facial-reduction steps needed to reach the minimal face. We determine how the constraints defining a binary feasible set affect this quantity. For every nonempty binary set defined by linear equations and inequalities, the Shor relaxation has singularity degree at most one. It is zero precisely when the linear programming (LP) relaxation contains a point in the open cube that strictly satisfies every inequality, and is one otherwise. As an algorithmic consequence of this characterization, Slater's condition can be restored by solving an LP rather than an auxiliary semidefinite program (SDP). In sharp contrast, for every $n\geq1$, we construct a binary feasible set defined by quadratic equalities whose Shor relaxation has a positive semidefinite matrix variable of order $n+1$ and singularity degree $n$. Thus, singularity degree is uniformly bounded in the linearly constrained case but can grow linearly with the matrix order when quadratic defining constraints are used.

math.OC↗

arXiv2026

Is there ghost and tachyon free bounce in UV complete gravity theory?

Analytic infinite derivative gravity theories provide a renormalizable and ghost-free description of gravity around covariantly constant backgrounds. These theories can have non-singular bouncing Universe solutions. In this paper we aim to address a question whether it is possible to realize a bouncing solution without the presence of a ghost or a tachyon instability in this framework. We perform a detailed analysis of degrees of freedom in $(1+3)$ formalism around Minkowski and de Sitter space-times. As a result it becomes clear that on a very general basis one cannot construct an instability free bounce without a negative cosmological constant. An analysis of known bouncing solutions in this model shows that an analyticity of higher derivative form factors in combination with solutions parameters result in the presence of a ghost radiation. Being motivated by the idea of resolving the cosmological singularity problem we proceed by analyzing scalar and tensor modes anyway. Scalar modes appear to not influence Cosmic Microwave Background observations at all, while tensor modes spectrum is computed and the corresponding implications are discussed.

gr-qc↗

arXiv2026

Data Preservation in High Energy Physics: Global Report 2026

This document summarizes the contributions to the 5th DPHEP workshop March 5-6, 2026, CERN, and reflects the advancements since 2024, as well as future milestones and tendencies. Impressive progress in HEP data preservation is observed. Legacy data revival was showcased through successful reanalysis of archived data using contemporary methods, demonstrating the long-term scientific value of preservation. Sustainability challenges were noted, emphasizing the need for long-term funding and institutional support to maintain data preservation infrastructure, particularly for legacy experiments transitioning to archival modes. Innovative transverse projects display constant progress towards common technologies for a robust and transferrable DP. In particular, there is a clear shift toward automation, with increasing use of AI and machine learning for data curation, metadata extraction, and workflow optimization. Open science momentum is growing, with wider adoption of FAIR principles and open data policies, and experiments committing to public releases.

hep-ex↗

arXiv2026

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, strong exogenous-fusion and exogenous-adapted models can degrade far above the endogenous-only floor. We study whether such robustness requires specialized architectures, or whether it can be obtained through a simple training intervention. We propose exogenous dropout, a model-agnostic method that randomly zeros whole exogenous channels during training. Across electricity-price forecasting, reservoir hydrology, and meteorology, exogenous dropout substantially improves robustness under Gaussian noise, temporal misalignment, and fully missing channels, while preserving clean accuracy. Applied to a dual-correlation network, it yields the most robust model in our experiments, outperforming a deliberately strong bounded architectural foil, BoundEx, which combines a learnable gate, a fallback residual to the endogenous backbone, and per-channel exogenous FiLM modulation. Architecture-by-dropout ablations, gate-behavior diagnostics, and a representation-level bound show that explicit architectural boundedness is not necessary for this robustness: an unbounded model trained with exogenous dropout is more robust than the bounded model in every domain. We release a corruption-robustness benchmark and recommend exogenous dropout as a simple, strong baseline for future work on time series forecasting with covariates.

cs.LG↗

arXiv2026

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards. Constructing such data for CUAs requires consistent task instruction, executable environment, and verifiable reward. However, hand-curated benchmarks achieve high reward fidelity but cover few applications and LLM-as-judge-based datasets scale broadly but lack reliable verification. We present CUA-Gym, a scalable pipeline that co-generates task instructions, environment states, and reward functions. Concretely, a Generator agent constructs the initial and golden environment states, and a separate Discriminator agent writes the reward function from the task specification. An orchestrator agent drives the two through iterative rounds upon execution. Generated tuples then pass a final filter combining LLM majority voting and agent rollouts, ensuring quality beyond the per-task adversarial loop. To address the scarcity of training environments, we further synthesize CUA-Gym-Hub, a broad suite of high-fidelity mock web applications grounded in real-world software-use distributions, expanding the scale of CUA RLVR data by magnitude. Using this pipeline, we construct CUA-Gym, a dataset of 32,112 verified RLVR training tuples grounded in 110 environments. Trained with GSPO on CUA-Gym, our CUA-Gym-A3B and CUA-Gym-A17B achieve 62.1% and 72.6% on OSWorld-Verified, outperforming prior open-source CUAs at comparable scales, with performance scaling smoothly in both data volume and environment diversity. The same checkpoints also improve on the held-out WebArena benchmark, indicating transfer beyond the training environments. We will open-source the full synthesis pipeline, dataset, CUA-Gym-Hub environments, and models.

cs.AI↗

arXiv2026

Finding Maximum Determinant Principal Submatrices via Hadamard Bounds and Projection Methods

An important yet challenging problem in numerical linear algebra is finding a principal submatrix with maximum determinant from a given symmetric positive semidefinite matrix. This problem arises in experimental design, statistics, and machine learning. We study several exact and approximate approaches to this problem. We first derive an upper bound based on Hadamard's inequality, along with a projection scheme based on the Gram--Schmidt process without normalization. This combination yields a highly effective upper bound and leads to an exact branch-and-bound algorithm for moderate-sized instances. For larger scale problems we propose a continuous relaxation that facilitates reliable performance evaluation when the exact method returns only near-optimal solutions. We further prove that the projection scheme strengthens the upper bound derived from this relaxation. Numerical experiments demonstrate the effectiveness of the proposed methods across a broad range of datasets.

math.OC↗

arXiv2026

Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework

While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the attack surface and challenge cyber resilience. Reinforcement Learning (RL)-based defense strategies have been developed to optimize resource deployment and isolation policies under adversarial conditions, aiming to enhance system resilience by maintaining and restoring network availability. However, existing approaches lack robustness as they require retraining to adapt to dynamic changes in network structure, node scale, attack strategies, and attack intensity. Furthermore, the lack of Human-in-the-Loop (HITL) support limits interpretability and flexibility. To address these limitations, we propose CyberOps-Bots, a hierarchical multi-agent reinforcement learning framework empowered by Large Language Models (LLMs). Inspired by MITRE ATT&CK's Tactics-Techniques model, CyberOps-Bots features a two-layer architecture: (1) An upper-level LLM agent with four modules--ReAct planning, IPDRR-based perception, long-short term memory, and action/tool integration--performs global awareness, human intent recognition, and tactical planning; (2) Lower-level RL agents, developed via heterogeneous separated pre-training, execute atomic defense actions within localized network regions. This synergy preserves LLM adaptability and interpretability while ensuring reliable RL execution. Experiments on real cloud datasets show that, compared to state-of-the-art algorithms, CyberOps-Bots maintains network availability 68.5% higher and achieves a 34.7% jumpstart performance gain when shifting the scenarios without retraining. To our knowledge, this is the first study to establish a robust LLM-RL framework with HITL support for cloud defense.

cs.CR↗

arXiv2026

A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems

The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRPs often involve diverse objectives and complex constraints, such as time windows or backhaul requirements, motivating the development of a unified solution framework. Recent reinforcement learning (RL) approaches have shown promise in combinatorial optimization, yet they rely on end-to-end learning and lack explicit problem-solving knowledge, limiting solution quality. In this paper, we propose a knowledge-embedded framework inspired by the Route-First Cluster-Second heuristics. It incorporates knowledge at two levels: (1) decomposing CVRPs into the route-first and cluster-second subproblems, and (2) leveraging dynamic programming to solve the second subproblem, whose results guide the RL-based constructive solver to solve the first problem. To mitigate partial observability caused by problem decomposition, we introduce a unified history-enhanced context processing module. Extensive experiments show that this framework achieves superior solution quality compared with state-of-the-art learning-based methods, with a smaller gap to classical heuristics, demonstrating strong generalization across diverse CVRP variants.

cs.AI↗

arXiv2026

Rongzai agent: A Large Language Model-Based Autonomous Assistant for Rietveld Refinement of Neutron Diffraction Data

Neutron diffraction (ND) is an indispensable technique for determining atomic positions (especially light elements) and thus serves as a critical probe for revealing microscopic structures in materials science. However, traditional Rietveld refinement of ND data relies heavily on manual operation of specialized software, which is time-consuming, labor-intensive, and highly dependent on user expertise, severely hindering automated analysis. The automation of Rietveld refinement has long been a long-standing and challenging problem in crystallography. To address this challenge, this paper presents the Dr.Sai-Rongzai agent, an autonomous refinement assistant based on a large language model (LLM), a specialist knowledge base, and the GSAS-II refinement engine, achieving for the first time an intelligent refinement that integrates knowledge-driven decision-making. The agent accomplishes a fully automated workflow from natural language task parsing to autonomous decision-making, execution of refinement strategies, and report generation. Evaluation on five representative samples shows that the Rongzai agent achieves lower Rwp values than human specialists on three samples (2.88% vs. 4.42%, 5.06% vs. 5.40%, 7.60% vs. 9.00%), while on the other two samples its results are very close to those of the specialists. The agent is currently deployed at the China Spallation Neutron Source (CSNS) and is open for external user registration, providing an intelligent and user-friendly analytical tool for materials research. This work fully leverages the cutting-edge advantages of LLM, offers a new path to solve the long-standing problem of automated refinement, takes a key step toward intelligent and fully automated crystallographic analysis, and holds great potential to accelerate AI for Science discoveries in neutron-based materials characterization.

cond-mat.mtrl-sci↗

arXiv2026

ObjView-Bench: Rethinking Difficulty and Deployment for Object-Centric View Planning

Object-centric view planning is a core component of active geometric 3D reconstruction in robotics, yet existing evaluations often conflate object complexity, planning difficulty, budget assumptions, and physical reachability constraints. As a result, conclusions drawn from idealized view-planning evaluations may not reliably predict performance under realistic reconstruction settings. We introduce ObjView-Bench, an evaluation framework for rethinking difficulty and deployment in object-centric view planning. First, we disentangle three quantities underlying view-planning evaluation: omnidirectional self-occlusion as an object-side attribute, observation saturation difficulty, and protocol-dependent planning difficulty defined through a set-cover formulation. This separation supports controlled dataset construction, analysis of slow-saturation objects, and a case study showing that planning difficulty-aware sampling can improve learned view planners. Second, we design deployment-oriented evaluation protocols that reveal how budget regimes and reachable-view constraints alter method behavior. Across classical, learned, and hybrid planners, ObjView-Bench shows that difficulty, budget, and reachability constraints substantially change method rankings and failure modes.

cs.RO↗

arXiv2026

Beyond Accuracy: Evaluating Strategy Diversity in LLM Mathematical Reasoning

Large language models now achieve high final-answer accuracy on mathematical reasoning benchmarks, but accuracy alone does not capture reasoning flexibility. We introduce a strategy-level evaluation framework instantiated on 80 AMC 10/12 and AIME problems with 217 AoPS-derived reference strategy families. Model outputs are annotated for strategy identity, validity, and correctness using dual-AI coding with human adjudication. Across four frontier models, we find a pronounced decoupling between answer accuracy and strategy diversity. Under a single-solution prompt, all models achieve high accuracy (95%-100%), but under a multiple-strategy prompt they recover substantially fewer strategies than the human reference set. Gemini, DeepSeek, GPT, and Claude generate 184, 152, 151, and 110 distinct valid strategies, respectively, with the largest gaps in Geometry and Number Theory. The models collectively produce 50 benchmark-novel valid strategies, indicating both incomplete coverage of human strategies and some capacity for alternative reasoning. A repeated-run robustness check on 20 problems shows diminishing gains in discovered strategies, with the strongest model recovering only 39 of 55 AoPS-reference strategies (71%) after three runs. These findings position strategy diversity as a complementary dimension for evaluating mathematical reasoning beyond answer correctness.

cs.AI↗

arXiv2026

Fundamentals and Applications of Time-varying Media: A Review

Time-varying media, characterized by dynamic or spacetime-modulated constitutive parameters such as permittivity and permeability, have recently emerged as a transformative paradigm for advanced wave control, transcending the constraints imposed by temporal translation symmetry and energy conservation in static systems. By incorporating time as an active degree of freedom, such media unlock unique phenomena including broadband frequency conversion, temporal refraction, significant field enhancement, and magnet-free nonreciprocity. These capabilities are reshaping the landscape of photonic technologies, enabling groundbreaking applications such as broadband nonreciprocal amplifiers, non-resonant lasers, and highly efficient particle accelerators. This review systematically classifies time-varying media based on their modulation schemes and elucidates the underlying physical principles and distinctive wave-matter interactions. We comprehensively survey the latest advances in this rapidly evolving field, highlighting exotic wave behaviors and practical implementations across electromagnetic and photonic systems. Furthermore, we summarize experimental platforms that realize time-varying responses across different frequency regimes. Finally, we assess the current state of progress, identify key challenges, and offer a forward-looking perspective on future research directions in this dynamic and promising area.

physics.optics↗

arXiv2026

Hybrid functional calculation of electrical activity and complexing mechanism of Cu-related defects

Copper is a detrimental impurity in silicon with high diffusivity and a high tendency to precipitate. Interaction between Cu and other defects is essential for understanding the nature of Cu precipitation in silicon. Despite extensive experimental investigations of Cu-related defects in silicon, a comprehensive understanding remains elusive due to limitations of techniques in resolving defect configurations, as well as inconsistencies between theoretical and experimental results regarding transition levels. Moreover, the underlying formation mechanism of the well-known $\mathrm{Cu_{PL}}$ line is still unclear. In this work, configurations, formation energies, and transition levels of Cu-related defects in silicon are calculated using the HSE06 functional and finite-size correction. Defects involved in this study include $\mathrm{Cu_i}$, $\mathrm{Cu_{Si}}$, Cu-B, Cu-P, and Cu-H. A $\mathrm{Cu_{i4}V}$ model is proposed to explain the discrepancies between theory and experiment about $\mathrm{Cu_{PL}}$ defect. Our calculations may provide insight into the electrically active defects and the early states of Cu precipitation in silicon.

cond-mat.mtrl-sci↗

arXiv2026

High-efficiency graphene-silicon slot-waveguide microring modulator at 1.5 μm and 2 μm wavelength bands

Electro-optic (E/O) modulators are crucial for optical communication but face a trade-off between modulation bandwidth and efficiency. A small footprint could reduce the capacitance and increase the bandwidth, however, this usually results in a low modulation efficiency. Here, we present an integrated E/O modulator that simultaneously achieves wideband large bandwidth and high modu- lation efficiency operation by embedding a partially overlapped double-layer graphene on a compact silicon slot waveguide microring resonator. At 1550 nm, the graphene-silicon slot-waveguide demon- strates a high phase modulation efficiency of Vπ L = 220 V μm, and the corresponding microring modulator has a large bandwidth of over 70 GHz, a compact active length of 10 μm, and an optical modulation amplitude (OMA) of -1.97 dBm under a 3-V voltage swing. The modulator operates at a data rate of 50 Gbit/s with an open eye diagram under a 2-V Vpp RF drive voltage. The graphene modulator operation is broadband, and we also characterize its performance at 2 μm wavelength band. At 2 μm wavelength band, the microring modulator has a large bandwidth of over 20 GHz, an OMA of -3.36 dBm under a 6-V voltage swing, and an open eye diagram at 20 Gbit/s with a 2-V Vpp RF drive voltage. The difference in performance is caused by the bandwidth limit of the 2 μm wavelength band measurement setup. The broadband, large bandwidth, compact, highly effi- cient, and energy efficient graphene E/O modulator has the potential to enable large-scale graphene photonic integrated circuits, facilitating a broad range of applications such as optical interconnects, optical neural networks, and programmable photonic circuits.

physics.optics↗

arXiv2026

Liquid-Gas Criticality of Hyperuniform Fluids

In statistical physics, it is well established that the liquid-gas (LG) phase transition with divergent critical fluctuations belongs to the Ising universality class. Whether non-equilibrium effects can alter this universal behavior remains a fundamental open question. In this work, we theoretically prove that non-equilibrium hyperuniform (HU) fluids with additional center-of-mass conservation exhibit LG criticality different from the Ising universality class. As a specific case, we investigate a 2D HU fluid composed of active spinners, where phase separation is driven by dissipative collisions. Strikingly, at the critical point, the 2D HU fluid displays finite density fluctuations $S(q)\sim q^η$ with $η=0$, while the compressibility still diverges. The critical point is thus calm yet highly susceptible, in fundamental violation of the conventional fluctuation-dissipation relation. Consistently, we observe short-range pair correlation functions coexisting with quasi-long-range response functions at the critical point. Based on a generalized Model B and renormalization-group analysis, we prove that hyperuniformity reduces the upper critical dimension $d_c$ from $4$ to $2$. Moreover, the critical point exhibits Gaussian density fluctuations and non-divergent energy fluctuations. Furthermore, the HU fluid undergoes non-conventional spinodal decomposition. The origin of the above anomalies lies in the non-equilibrium nature of the system which obeys a generalized fluctuation-dissipation relation $2\mathrm{Im}~ χ(q,ω) ={ω}C(q,ω)/{k_B T_{\text{eff}}(q)}$ with a scale-dependent effective temperature $T_{\rm eff}(q) \propto q^2$. These findings establish a striking exception to conventional paradigms of critical phenomena and illustrate how non-equilibrium forces can fundamentally reshape universality classes.

cond-mat.stat-mech↗

arXiv2026

Wideband integrated high-speed graphene-silicon slot-waveguide electro-absorption modulator at 2 μm and 1.5 μm wavebands

The 2-μm waveband, emerging as a highly promising candidate for optical communication, offers an extended wavelength window for high-speed optical transmission. Despite its potential, the development of integrated electro-optic (E/O) modulators operating at this wavelength range has been limited. Such E/O modulators are crucial for high-speed optical communication systems at the 2-μm waveband. In this work, we propose and experimentally demonstrate high-performance E/O absorption modulators based on a graphene-silicon slot waveguide. Our approach enables wideband, high-speed, efficient, robust and compact modulators at both 2-μm and 1.5-μm wavebands. This work represents a significant advancement towards the realization of high-speed integrated E/O modulators for optical communication systems operating at the 2-μm wavelength range.

physics.optics↗