arXiv subjects

Peng Zou

Publications and source records attributed to Peng Zou.

At least 19 recordsLinked to original sources

arXiv2026

An investigation of magnetic energy and helicity thresholds at the onset of solar eruptions based on numerical simulations

Identifying universal, topology-independent thresholds in the coronal magnetic fields at onset of solar eruptions is crucial for physics-based prediction of eruptions. To this end, we systematically analyze the evolution of magnetic energy and helicity in twelve high-fidelity 3D magnetohydrodynamic simulations where eruptions are triggered by magnetic reconnection. The simulations encompass a comprehensive parameter space, including bipolar and quadrupolar configurations, sheared arcades and pre-existing flux ropes, and various photospheric driving motions. We find that the ratio of current-carrying helicity to total relative helicity $(H_j/H_r)$ exhibits a remarkably consistent threshold of $0.38 \pm 0.04$ at eruption onset across all cases, with a coefficient of variation of only $\sim 10$\%. This threshold specifically characterizes the critical conditions at eruption onset and is largely independent of the subsequent temporal evolution, making it the most robust eruptivity indicator identified. In contrast, other normalized helicity and energy metrics show greater scatter. Crucially, we further find that $H_j/H_r$ does not necessarily achieve its peak at the eruption onset time and its post-eruption evolution diverges based on magnetic topology: it continues to increase in bipolar configurations due to tether-cutting reconnection, which transforms sheared arcade into the erupting current-carrying magnetic flux, but decreases in quadrupolar configurations as breakout reconnection peels off the erupting flux. These results highlight the helicity ratio as a promising and consistent eruptivity indicator and provide new insights into its dynamic evolution due to different reconnections.

astro-ph.SR↗

arXiv2026

Age of Information Optimization in Distributed Sensor Networks with Half-Duplex Channels

Motivated by cooperative distributed networks in which users dynamically alternate between transmit and receive modes under half-duplex constraints, this paper studies the Age of Information (AoI) in a distributed multi-user network using an ALOHA-based protocol. We derive closed-form expressions for the average AoI and formulate an optimization problem over transmission probabilities. After proving the convexity of the problem, we leverage the derived optimality conditions to characterize optimal policies for general network graphs, obtain closed-form solutions for $d$-regular topologies, and derive tractable optimality conditions for star topologies. Numerical results confirm that the proposed mechanism can effectively and adaptively determine user-specific optimal transmission probabilities across varying network topologies. These findings contribute to the design of adaptive and efficient distributed networks with enhanced information freshness.

cs.IT↗

arXiv2026

Novel dynamical excitations and roton-based measurement of Cooper-pair momentum in a two-dimensional Fulde-Ferrell-Larkin-Ovchinnikov superfluid on optical lattices

Determining the center-of-mass (COM) momentum of Cooper pairs in unconventional superconductors or superfluids is a topic of great interest in condensed matter physics and ultracold atomic gases. Theoretically, we investigate the dynamical excitations of a two-dimensional spin-polarized attractive Hubbard model on a square optical lattice under an effective Zeeman field by computing the density and spin dynamical structure factors, focusing on phase transition from a Bardeen-Cooper-Schrieffer (BCS) superfluid to an Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superfluid. In the FFLO superfluid, besides the phonon mode in the density channel, a low-energy bogolon mode emerges in the spin channel, which is associated with Bogoliubov quasiparticles on a Bogoliubov Fermi surface. Moreover, the dynamical excitations exhibit pronounced anisotropy in momentum space due to the finite COM momentum. At half filling, the roton mode around $[\pi,\pi]$ evolves from a point-like minimum into a ring structure shifted by the COM momentum across the BCS-FFLO transition, providing a roton-based protocol to extract the COM momentum. These predictions provide key insights for confirming the existence of FFLO superfluids and understanding their dynamical excitation spectra.

cond-mat.supr-con↗

arXiv2025

MARINE: Theoretical Optimization and Design for Multi-Agent Recursive IN-context Enhancement

Large Language Model (LLM)-based agents demonstrate advanced reasoning capabilities, yet practical constraints frequently limit outputs to single responses, leaving significant performance potential unrealized. This paper introduces MARINE (Multi-Agent Recursive IN-context Enhancement), a theoretically grounded framework that reconceptualizes test-time reasoning as iterative refinement of a persistent reference trajectory, fundamentally departing from conventional one-shot or multi-sample paradigms. The MARINE refinement operator systematically converts a base model's pass@N capabilities into near-optimal pass@1 performance. Rigorous theoretical analysis establishes that minimal feasible batches maximize expected performance gains under fixed invocation budgets, while logarithmically growing batch schedules ensure continuous improvement without computational constraints. Comprehensive evaluation on the BrowserComp-ZH benchmark demonstrates state-of-the-art results, with a 685B-parameter implementation achieving 46.0% pass@1 accuracy. Meanwhile, MARINE establishes a new paradigm for parameter-efficient reasoning: an 80B-parameter model augmented with MARINE matches the performance of standalone 1000B-parameter agents, reducing parameter requirements by over an order of magnitude. Notably, within a fixed computational budget, the proposed MARINE delivers higher-quality samples to alignment and optimization processes than traditional sampling-and-ranking strategies. Consequently, it has great potential to boost post-training efficiency.

cs.MA↗

arXiv2025

SUPERChem: A Multimodal Reasoning Benchmark in Chemistry

Current benchmarks for evaluating the chemical reasoning capabilities of Large Language Models (LLMs) are limited by oversimplified tasks, lack of process-level evaluation, and misalignment with expert-level chemistry skills. To address these issues, we introduce SUPERChem, a benchmark of 500 expert-curated reasoning-intensive chemistry problems, covering diverse subfields and provided in both multimodal and text-only formats. Original content and an iterative curation pipeline eliminate flawed items and mitigate data contamination. Each problem is paired with an expert-authored solution path, enabling Reasoning Path Fidelity (RPF) scoring to evaluate reasoning quality beyond final-answer accuracy. Evaluations against a human baseline of 40.3% accuracy show that even the best-performing model, GPT-5 (High), reaches only 38.5%, followed closely by Gemini 2.5 Pro (37.9%) and DeepSeek-V3.1-Think (37.3%). SUPERChem elicits multi-step, multimodal reasoning, reveals model-dependent effects of visual information, and distinguishes high-fidelity reasoners from heuristic ones. By providing a challenging benchmark and a reliable evaluation framework, SUPERChem aims to facilitate the advancement of LLMs toward expert-level chemical intelligence. The dataset of the benchmark is available at https://huggingface.co/datasets/ZehuaZhao/SUPERChem.

cs.CL↗

arXiv2025

Finding the stable mechanism of ring solitons in two-dimensional Fermi superfluids

We theoretically investigate the stable mechanism of a ring soliton in two-dimensional Fermi superfluids by solving the Bogoliubov-de Gennes equations and their time-dependent counterparts. In the uniform situation, we discover that the ring soliton is always driven away from its initial location, and moves towards the edge due to a curvature-induced effective potential. The ring soliton is impossible to remain static at any location in the uniform system. To balance the density difference between the ring soliton's two sides, a harmonic trap is introduced, which can exert an effect to counterbalances the curvature-induced effective potential. This enables the ring dark soliton to become a stable state at a particular equilibrium position r_s, where the free energy of the ring dark soliton just reaches the maximum value. Once ring soliton is slightly deviated from r_s, some stable periodic oscillations of ring soliton around r_s will turn out. Some dissipation will possibly occur to ring soliton once its minimum radius is comparable to the healing length of soliton's Friedel oscillation. This dissipation will increase the oscillation amplitude and finally make the ring soliton decay into sound ripples. Our research lays the groundwork for a more in-depth understanding of the stable mechanism of a ring dark soliton in the future.

cond-mat.quant-gas↗

arXiv2025

Co-Sight: Enhancing LLM-Based Agents via Conflict-Aware Meta-Verification and Trustworthy Reasoning with Structured Facts

Long-horizon reasoning in LLM-based agents often fails not from generative weakness but from insufficient verification of intermediate reasoning. Co-Sight addresses this challenge by turning reasoning into a falsifiable and auditable process through two complementary mechanisms: Conflict-Aware Meta-Verification (CAMV) and Trustworthy Reasoning with Structured Facts (TRSF). CAMV reformulates verification as conflict identification and targeted falsification, allocating computation only to disagreement hotspots among expert agents rather than to full reasoning chains. This bounds verification cost to the number of inconsistencies and improves efficiency and reliability. TRSF continuously organizes, validates, and synchronizes evidence across agents through a structured facts module. By maintaining verified, traceable, and auditable knowledge, it ensures that all reasoning is grounded in consistent, source-verified information and supports transparent verification throughout the reasoning process. Together, TRSF and CAMV form a closed verification loop, where TRSF supplies structured facts and CAMV selectively falsifies or reinforces them, yielding transparent and trustworthy reasoning. Empirically, Co-Sight achieves state-of-the-art accuracy on GAIA (84.4%) and Humanity's Last Exam (35.5%), and strong results on Chinese-SimpleQA (93.8%). Ablation studies confirm that the synergy between structured factual grounding and conflict-aware verification drives these improvements. Co-Sight thus offers a scalable paradigm for reliable long-horizon reasoning in LLM-based agents. Code is available at https://github.com/ZTE-AICloud/Co-Sight/tree/cosight2.0_benchmarks.

cs.AI↗

arXiv2025

Nonreciprocal optical circuit switching

Directly switching optical signals outperforms conventional optoelectronic hardware in terms of cost, latency, and energy efficiency, and is expected to address the growing demand for data node capacity driven by the development of machine learning and artificial intelligence (AI) technologies. Therefore, optical circuit switching (OCS) technology has piqued widespread research interest in various technical solutions, including silicon photonics. However, silicon-based integrated OCS remains constrained by challenges such as network performance and port scalability. Here we propose a magneto-optical heterogeneous integrated nonreciprocal OCS (NOCS) network based on a silicon photonics platform, achieving bidirectional full-duplex nonreciprocal transmission by programming reciprocal and nonreciprocal phase shifters. We demonstrate that compared with the existing OCS architecture, NOCS has the advantages of ultra-high reconfiguration speed, large-scale integration compatibility, and bidirectional channel isolation reducing the number of required ports. NOCS could meet the programming speed requirements of the AI backend network, or supports nonreciprocal optical switching applications without multiplexing technology.

physics.optics↗

arXiv2025

The role of photospheric magnetic flux diffusion in initiation of solar eruptions

Solar eruptions may occur at different evolutionary stages of active regions, during which the photospheric motions manifest in various forms, including flux emergence, sunspot rotation, shearing, converging, and magnetic flux diffusion. However, it remains unclear what are the specific roles played by these different motions in leading to eruptions. Here, we employ high resolution magnetohydrodynamic simulations to demonstrate how solar eruptions can be initiated in a single bipolar configuration, driven by first shearing and then flux diffusion at the bottom surface. Flux diffusion disperses the photospheric magnetic flux, driving portions of it toward the polarity inversion line (PIL). This process leads to the expansion of core field, enhancing the pinching effect to form the current sheet. When magnetic reconnection occurs within this current sheet, the eruption is initiated, characterized by a rapid release of magnetic energy and accompanied by the formation of a erupting flux rope. Additionally, flux diffusion contributes to magnetic cancellation near the PIL, leading to the formation of a weakly twisted magnetic flux rope prior to the eruption. However, this pre-exist flux rope plays a limited role in eruption initiation, as its spatial position remains largely unchanged throughout the eruption. These findings demonstrate that the primary role of flux diffusion is to facilitate current sheet formation, highlighting the critical role of current sheet formation in eruption initiation.

astro-ph.SR↗

arXiv2025

Age of Information Optimization with Preemption Strategies for Correlated Systems

In this paper, we examine a multi-sensor system where each sensor monitors multiple dynamic information processes and transmits updates over a shared communication channel. These updates may include correlated information across the various processes. In this type of system, we analyze the impact of preemption, where ongoing transmissions are replaced by newer updates, on minimizing the Age of Information (AoI). While preemption is optimal in some scenarios, its effectiveness in multi-sensor correlated systems remains an open question. To address this, we introduce a probabilistic preemption policy, where the source sensor preemption decision is stochastic. We derive closed-form expressions for the AoI and frame its optimization as a sum of linear ratios problem, a well-known NP-hard problem. To navigate this complexity, we establish an upper bound on the iterations using a branch-and-bound algorithm by leveraging a reformulation of the problem. This analysis reveals linear scalability with the number of processes and a logarithmic dependency on the reciprocal of the error that shows the optimal solution can be efficiently found. Building on these findings, we show how different correlation matrices can lead to distinct optimal preemption strategies. Interestingly, we demonstrate that the diversity of processes within the sensors' packets, as captured by the correlation matrix, plays a more significant role in preemption priority than the number of updates.

cs.IT↗

arXiv2025

Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition

In this paper, we present our solution to the Cross-View Isolated Sign Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a critical issue in traditional Isolated Sign Language Recognition (ISLR), where existing datasets predominantly capture sign language videos from a frontal perspective, while real-world camera angles often vary. To accurately recognize sign language from different viewpoints, models must be capable of understanding gestures from multiple angles, making cross-view recognition challenging. To address this, we explore the advantages of ensemble learning, which enhances model robustness and generalization across diverse views. Our approach, built on a multi-dimensional Video Swin Transformer model, leverages this ensemble strategy to achieve competitive performance. Finally, our solution ranked 3rd in both the RGB-based ISLR and RGB-D-based ISLR tracks, demonstrating the effectiveness in handling the challenges of cross-view recognition. The code is available at: https://github.com/Jiafei127/CV_ISLR_WWW2025.

cs.CV↗

arXiv2024

A New Approach of Data-driven Simulation and Its Application to Solar Active Region 12673

The solar coronal magnetic field is a pivotal element in the study of eruptive phenomena, and understanding its dynamic evolution has long been a focal point in solar physics. Numerical models, driven directly by observation data, serve as indispensable tools in investigating the dynamics of the coronal magnetic field. This paper presents a new approach to electric field inversion, which involves modifying the electric field derived from the DAVE4VM velocity field using ideal Ohm's law. The time series of the modified electric field is used as a boundary condition to drive a MHD model, which is applied to simulate the magnetic field evolution of active region 12673. The simulation results demonstrate that our method enhances the magnetic energy injection through the bottom boundary, as compared with energy injection calculated directly from the DAVE4VM code, and reproduce of the evolution of the photospheric magnetic flux. The coronal magnetic field structure is also in morphological similarity to the coronal loops. This new approach will be applied to the high-accuracy simulation of eruption phenomena and provide more details on the dynamical evolution of the coronal magnetic field.

astro-ph.SR↗

arXiv2024

PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation

Mental health has attracted substantial attention in recent years and LLM can be an effective technology for alleviating this problem owing to its capability in text understanding and dialogue. However, existing research in this domain often suffers from limitations, such as training on datasets lacking crucial prior knowledge and evidence, and the absence of comprehensive evaluation methods. In this paper, we propose a specialized psychological large language model (LLM), named PsycoLLM, trained on a proposed high-quality psychological dataset, including single-turn QA, multi-turn dialogues and knowledge-based QA. Specifically, we construct multi-turn dialogues through a three-step pipeline comprising multi-turn QA generation, evidence judgment, and dialogue refinement. We augment this process with real-world psychological case backgrounds extracted from online platforms, enhancing the relevance and applicability of the generated data. Additionally, to compare the performance of PsycoLLM with other LLMs, we develop a comprehensive psychological benchmark based on authoritative psychological counseling examinations in China, which includes assessments of professional ethics, theoretical proficiency, and case analysis. The experimental results on the benchmark illustrate the effectiveness of PsycoLLM, which demonstrates superior performance compared to other LLMs.

cs.CL↗

arXiv2024

Age of Information Optimization and State Error Analysis for Correlated Multi-Process Multi-Sensor Systems

In this paper, we examine a multi-sensor system where each sensor may monitor more than one time-varying information process and send status updates to a remote monitor over a common channel. We consider that each sensor's status update may contain information about more than one information process in the system subject to the system's constraints. To investigate the impact of this correlation on the overall system's performance, we conduct an analysis of both the average Age of Information (AoI) and source state estimation error at the monitor. Building upon this analysis, we subsequently explore the impact of the packet arrivals, correlation probabilities, and rate of processes' state change on the system's performance. Next, we consider the case where sensors have limited sensing abilities and distribute a portion of their sensing abilities across the different processes. We optimize this distribution to minimize the total AoI of the system. Interestingly, we show that monitoring multiple processes from a single source may not always be beneficial. Our results also reveal that the optimal sensing distribution for diverse arrival rates may exhibit a rapid regime switch, rather than smooth transitions, after crossing critical system values. This highlights the importance of identifying these critical thresholds to ensure effective system performance.

cs.IT↗

arXiv2024

A universal pairing gap measurement proposal by dynamical excitations in 2D doped attractive Fermi-Hubbard model with spin-orbit coupling

By calculating dynamical structure factor of two-dimensional doped attractive Fermi-Hubbard model with Rashba spin-orbit coupling, we not only investigate collective modes and single-particle excitations of the system during the phase transition between Bardeen-Cooper-Schrieffer superfluid and topological superfluid, but also propose a universal method to measure pairing gap measurement in an optical lattice system. Our numerical results show that the area of the molecular excitation peak at the transferred momentum ${\bf q}=\left[\pi,\pi\right]$ is proportional to the square of the pairing gap in the system with Rashba SOC. In particular, this method is very sensitive to the pairing gap. This goes on verifying that this method is universal to measure the pairing gap in a doped optical lattice with Rashba SOC. These theoretical results are important for experimentally measuring the pairing gap and studying the topological superfluid in an optical lattice.

cond-mat.quant-gas↗

arXiv2024

Dynamical excitations of one-dimensional Fulde-Ferrell pairing Fermi superfluid

We theoretically investigate a one-dimensional Fulde-Ferrell Fermi superfluid at a finite effective Zeeman field $h$, and study entire dynamical excitations related to density perturbation. By calculating the density dynamic structure factor, we find anisotropic dynamical excitations in both collective modes and single-particle excitations. Along the direction of centre-of-mass momentum $p$, there are two obvious gapless collective modes with different speed. The lower collective modes is from the usual gauge symmetry breaking and has a larger speed than the one in the negative direction of $p$. The higher one is due to the direction spontaneous symmetry breaking of centre-of-mass momentum $p$, and separates two kinds of single-particle excitations in the positive $p$ direction. However, this higher mode disappears in the opposite direction of $p$, where two single-particle excitations overlap with each other. These signals of dynamical excitations can do help to distinguish Fulde-Ferrell superfluid from the conventional Bardeen-Cooper-Schrieffer superfluid in the future experiment.

cond-mat.quant-gas↗

arXiv2023

Magnetic Reconnection as the Key Mechanism in Sunspot Rotation Leading to Solar Eruption

The rotation of sunspots around their umbral center has long been considered as an important process in leading to solar eruptions, but the underlying mechanism remains unclear. A prevailing physical picture on how sunspot rotation leads to eruption is that, by twisting the coronal magnetic field lines from their footpoints, the rotation can build up a magnetic flux rope and drive it into some kinds of ideal magnetohydrodynamics (MHD) instabilities which initiate eruptions. Here with a data-inspired MHD simulation we studied the rotation of a large sunspot in solar active region NOAA 12158 leading to a major eruption, and found that it is distinct from prevailing theories based on ideal instabilities of twisted flux rope. The simulation suggests that, through successive rotation of the sunspot, the coronal magnetic field is sheared with a central current sheet created progressively within the sheared arcade before the eruption, but without forming a flux rope. Then the eruption is instantly triggered once fast reconnection sets in at the current sheet, while a highly twisted flux rope is created during the eruption. Furthermore, the simulation reveals an intermediate evolution stage between the quasi-static energy-storage phase and the impulsive eruption-acceleration phase. This stage may correspond to the slow-rise phase in observation and it enhances building up of the current sheet.

astro-ph.SR↗

arXiv2023

Data-driven MHD simulation of a sunspot rotating active region leading to solar eruption

Solar eruptions are the leading driver of space weather, and it is vital for space weather forecast to understand in what conditions the solar eruptions can be produced and how they are initiated. The rotation of sunspots around their umbral center has long been considered as an important condition in causing solar eruptions. To unveil the underlying mechanisms, here we carried out a data-driven magnetohydrodynamics simulation for the event of a large sunspot with rotation for days in solar active region NOAA 12158 leading to a major eruption. The photospheric velocity as recovered from the time sequence of vector magnetograms are inputted directly at the bottom boundary of the numerical model as the driving flow. Our simulation successfully follows the long-term quasi-static evolution of the active region until the fast eruption, with magnetic field structure consistent with the observed coronal emission and onset time of simulated eruption matches rather well with the observations. Analysis of the process suggests that through the successive rotation of the sunspot the coronal magnetic field is sheared with a vertical current sheet created progressively, and once fast reconnection sets in at the current sheet, the eruption is instantly triggered, with a highly twisted flux rope originating from the eruption. This data-driven simulation stresses magnetic reconnection as the key mechanism in sunspot rotation leading to eruption.

astro-ph.SR↗