arXiv subjects

Ning Tang

Publications and source records attributed to Ning Tang.

15 recordsLinked to original sources

arXiv2026

The good commutator approach to global asymptotics for the Schr\"odinger equation with variable coefficients

We present a robust physical-space approach to establish time decay and global asymptotics of solutions to variable-coefficient Schr\"odinger equations in (3+1)-dimensions. As an immediate nonlinear application, we obtain new small data global existence and asymptotics results for quasilinear Schr\"odinger equations with cubic, Hamiltonian nonlinearity, variable coefficients in their linear part, and possibly outside obstacles, even in the presence of trapped bicharacteristics (provided that they are suitably unstable). Our approach relies on three primary ingredients. First is the concept of a good commutator, which extends Klainerman's classical commuting vector field method, and develops upon earlier approaches of Cuccagna-Georgiev-Visciglia and Rodnianski-Tao in the variable-coefficient case, and of Ifrim-Tataru, Ifrim-Koch-Tataru in the nonlinear case. Second, we apply Ifrim and Tataru's testing-by-wave-packets method, which is key for obtaining global asymptotics and handling sharp decay assumptions on the coefficients. Finally, we introduce a systematic technique for analyzing the control provided by the good commutator, termed two-scale elliptic analysis. Together, these techniques significantly broaden the applicability of physical-space methods to a wider class of variable-coefficient nonlinear problems.

math.AP↗

arXiv2026

ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks either focus on tables, which are well-structured and textualized, or generate cross-chart questions by simply extracting key points, which often induces lexical overlap between queries and evidence and yields logically inconsistent reasoning chains. To address this, we introduce ChartWalker, a novel framework for constructing challenging cross-chart RAG tasks. ChartWalker features a hierarchical knowledge graph construction method tailored to charts, which organizes entities and relations by granularity to preserve analytical structure. We then propose a structure-aware sampling algorithm that synthesizes semantically coherent, multi-hop reasoning paths, enabling explicit control over query difficulty and granularity for QA generation. Built with this framework, we release ChartWalker-Bench, a comprehensive benchmark spanning diverse domains and cross-chart query types. Extensive evaluations across major RAG paradigms reveal significant performance gaps, underscoring the benchmark's difficulty and utility. Furthermore, we provide ChartWalker-Agent, an agentic baseline to facilitate analysis and inspire future system design.

cs.IR↗

arXiv2026

PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization

Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recommendation and content filtering, can unintentionally amplify echo chambers and polarization, posing significant societal risks. Proactively evaluating the impact of such policies is therefore crucial. Existing approaches primarily rely on reactive online A/B testing, where risks are identified only after deployment, making risk identification delayed and costly. LLM-based social simulations offer a promising pre-deployment alternative, but current methods fall short in realistically modeling platform interventions and incorporating feedback from the platform. Bridging these gaps is essential for building actionable frameworks to assess and optimize platform policies. To this end, we propose PolicySim, an LLM-based social simulation sandbox for the proactive assessment and optimization of intervention policies. PolicySim models the bidirectional dynamics between user behavior and platform interventions through two key components: (1) a user agent module refined via supervised fine-tuning (SFT) and direct preference optimization (DPO) to achieve platform-specific behavioral realism; and (2) an adaptive intervention module that employs a contextual bandit with message passing to capture dynamic network structures. Experiments show that PolicySim can accurately simulate platform ecosystems at both micro and macro levels and support effective intervention policy.

cs.SI↗

arXiv2025

OxygenREC: An Instruction-Following Generative Framework for E-commerce Recommendation

Traditional recommendation systems suffer from inconsistency in multi-stage optimization objectives. Generative Recommendation (GR) mitigates them through an end-to-end framework; however, existing methods still rely on matching mechanisms based on inductive patterns. Although responsive, they lack the ability to uncover complex user intents that require deductive reasoning based on world knowledge. Meanwhile, LLMs show strong deep reasoning capabilities, but their latency and computational costs remain challenging for industrial applications. More critically, there are performance bottlenecks in multi-scenario scalability: as shown in Figure 1, existing solutions require independent training and deployment for each scenario, leading to low resource utilization and high maintenance costs-a challenge unaddressed in GR literature. To address these, we present OxygenREC, an industrial recommendation system that leverages Fast-Slow Thinking to deliver deep reasoning with strict latency and multi-scenario requirements of real-world environments. First, we adopt a Fast-Slow Thinking architecture. Slow thinking uses a near-line LLM pipeline to synthesize Contextual Reasoning Instructions, while fast thinking employs a high-efficiency encoder-decoder backbone for real-time generation. Second, to ensure reasoning instructions effectively enhance recommendation generation, we introduce a semantic alignment mechanism with Instruction-Guided Retrieval (IGR) to filter intent-relevant historical behaviors and use a Query-to-Item (Q2I) loss for instruction-item consistency. Finally, to resolve multi-scenario scalability, we transform scenario information into controllable instructions, using unified reward mapping and Soft Adaptive Group Clip Policy Optimization (SA-GCPO) to align policies with diverse business objectives, realizing a train-once-deploy-everywhere paradigm.

cs.IR↗

arXiv2025

Tree of Preferences for Diversified Recommendation

Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback. Nonetheless, due to inherent data biases, the observed data may not fully reflect user interests, where underexplored preferences can be overwhelmed or remain unmanifested. Failing to capture these preferences can lead to suboptimal diversity in recommendations. To fill this gap, this work aims to study diversified recommendation from a data-bias perspective. Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach that utilizes LLMs' expertise to uncover underexplored user preferences from observed behavior, ultimately providing diverse and relevant recommendations. To achieve this, we first introduce Tree of Preferences (ToP), an innovative structure constructed to model user preferences from coarse to fine. ToP enables LLMs to systematically reason over the user's rationale behind their behavior, thereby uncovering their underexplored preferences. To guide diversified recommendations using uncovered preferences, we adopt a data-centric approach, identifying candidate items that match user preferences and generating synthetic interactions that reflect underexplored preferences. These interactions are integrated to train a general recommender for diversification. Moreover, we scale up overall efficiency by dynamically selecting influential users during optimization. Extensive evaluations of both diversity and relevance show that our approach outperforms existing methods in most cases and achieves near-optimal performance in others, with reasonable inference latency.

cs.IR↗

arXiv2025

Synthetic Acceleration Preconditioners for Parametric Radiative Transfer Equations based on Trajectory-Aware Reduced Order Models

The parametric radiative transfer equation (RTE) arises in multi-query applications, such as design optimization, inverse problems, and uncertainty quantification, which require solving the RTE multiple times for various parameters. Classical synthetic acceleration (SA) preconditioners are designed based on low-order approximations of a kinetic correction equation, e.g., its diffusion limit in diffusion synthetic acceleration (DSA). Despite their widespread success, these methods rely on empirical physical assumptions and do not leverage low-rank structures across parameters of the parametric problem. To address these limitations, our previous work introduced a reduced-order model (ROM) enhanced preconditioner called ROMSAD, which exploits low-rank structures across parameters and the original kinetic description of the correction equation. While ROMSAD improves overall efficiency compared with DSA, its efficiency reduces after the first iteration, because the construction of the underlying ROM ignores the preconditioner-dependence of the residual trajectory, leading to a mismatch between the offline and online residual trajectories. To overcome this issue, we introduce a trajectory-aware framework that iteratively constructs ROMs to eliminate the mismatch between offline and online residual trajectories. Numerical tests demonstrate superior efficiency over DSA, and substantial gains in both efficiency and robustness over ROMSAD. For a parametric lattice problem, trajectory-aware ROM preconditioners achieve rapid convergence within only $2$-$3$ iterations online.

math.NA↗

arXiv2025

SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation

The rapid growth of scientific literature demands robust tools for automated survey-generation. However, current large language model (LLM)-based methods often lack in-depth analysis, structural coherence, and reliable citations. To address these limitations, we introduce SciSage, a multi-agent framework employing a reflect-when-you-write paradigm. SciSage features a hierarchical Reflector agent that critically evaluates drafts at outline, section, and document levels, collaborating with specialized agents for query interpretation, content retrieval, and refinement. We also release SurveyScope, a rigorously curated benchmark of 46 high-impact papers (2020-2025) across 11 computer science domains, with strict recency and citation-based quality controls. Evaluations demonstrate that SciSage outperforms state-of-the-art baselines (LLM x MapReduce-V2, AutoSurvey), achieving +1.73 points in document coherence and +32% in citation F1 scores. Human evaluations reveal mixed outcomes (3 wins vs. 7 losses against human-written surveys), but highlight SciSage's strengths in topical breadth and retrieval efficiency. Overall, SciSage offers a promising foundation for research-assistive writing tools.

cs.AI↗

arXiv2024

Stability of the catenoid for the hyperbolic vanishing mean curvature equation in 4 spatial dimensions

We establish the asymptotic stability of the catenoid, as a nonflat stationary solution to the hyperbolic vanishing mean curvature (HVMC) equation in Minkowski space $\mathbb{R}^{1 + (n + 1)}$ for $n = 4$. Our main result is under a ``codimension-$1$'' assumption on initial perturbation, modulo suitable translation and boost (i.e. modulation), without any symmetry assumptions. In comparison to the $n \geq 5$ case addressed by L\"{u}hrmann-Oh-Shahshahani arxiv:2212.05620, proving catenoid stability in $4$ dimensions shares additional difficulties with its $3$ dimensional analog, namely the slower spatial decay of the catenoid and slower temporal decay of waves. To overcome these difficulties in the $n = 3$ case, the strong Huygens principle, as well as a miraculous cancellation in the source term, plays an important role in arxiv:2409.05968 to obtain strong late time tails. In $n = 4$ dimensions, without these special structural advantages, our novelty is to introduce an appropriate commutator vector field to derive a new hierarchy of estimates with higher $r^p$-weights so that an improved pointwise decay can be established. We expect this to be applicable for proving improved late time tails of other quasilinear wave equations in even dimensions or wave equations with inverse square potential.

math.AP↗

arXiv2024

Unambiguous identification of the indirect band nature of atomically thin hexagonal boron nitride

Atomically thin hexagonal boron nitride (h-BN), especially monolayer, has garnered increasing attention due to its intriguing optical and light-matter-interaction properties. However, its intrinsic optical properties and electronic band structure, have long remained elusive. In this study, near-resonance excited deep-UV photoluminescence/Raman spectroscopy and deep-UV reflectance contrast spectroscopy are utilized to experimentally investigate the optical properties of atomically thin h-BN across various layer numbers. It is revealed that the absence of luminescence in 1-3 layers h-BN is indicative of their indirect band gap nature, rectifying previously adopted identification of a direct band gap in monolayer BN. Notably, band-edge luminescence signals and indirect bandgap absorption start to appear in 4-layer, and the luminescence intensity increases with the number of layers, suggesting that interlayer interactions and periodicity along the z-axis enhance phonon-assisted indirect bandgap transition, even in the 4-layer case, and furthermore indicating the formation process of flat bands at the K and M valleys as the periodicity along the z direction increases. Additionally, the prominent resonance Raman signals in atomically thin h-BN underscore strong electron-phonon coupling in this material.

cond-mat.mtrl-sci↗

arXiv2023

Observation of Fluctuation Spin Hall Effect in Antiferromagnet

The spin Hall effect (SHE) can generate a pure spin current by an electric current, which is promisingly used to electrically control magnetization. To reduce power consumption of this control, a giant spin Hall angle (SHA) in the SHE is desired in low-resistivity systems for practical applications. Here, critical spin fluctuation near the antiferromagnetic (AFM) phase-transition is proved as an effective mechanism to create an additional part of SHE, named as fluctuation spin Hall effect (FSHE). This FSHE enhances the SHA due to the AFM spin fluctuation between conduction electrons and local spins. We detect the FSHE with the inverse and direct spin Hall effect (ISHE and DSHE) set-up and their temperature (T) dependences in the Cr/MgO/Fe magnetic tunnel junctions (MTJs). The SHA is significantly enhanced when temperature is approached to the N\'eel temperature (T_N) and has a peak value of -0.34 at 200 K near T_N. This value is higher than the room-temperature value by 240% and comparable to that of heavy metals Ta and W. Furthermore, the spin Hall resistivity of Cr well fits the modeled T-dependence when T approaches T_N from low temperatures, implying the AFM spin fluctuation nature of strong SHA enhancement. Thus, this study demonstrates the critical spin fluctuation as a prospective way of increasing SHA and enriches the AFM material candidates for spin-orbitronic devices.

cond-mat.mes-hall↗

arXiv2021

Well-posedness and regularity for the fractional Navier-Stokes equations

We consider the wellposedness of the fractional Navier-Stokes as a generalization of the wellposedness result in Koch-Tataru's paper. An interesting remark is that our result does not contradict to the well-known ill-posedness result for Navier-Stokes with initial data in $\dot B^{-1, \infty}_\infty$ by Bourgain-Pavlovic. In the end, we also discuss the regularity and analyticity in the space variable.

math.AP↗

arXiv2013

Correlation quantum beats induced by non-Markovian effect

For two qubits independently coupled to their respective structured reservoirs (Lorentzian spectrum), quantum beats for entanglement and discord are found which are the result of quantum interference between correlation oscillations induced by local non-Markovian environments. We also discuss the preservation of quantum correlations by the effective suppression of the spontaneous emission.

quant-ph↗

arXiv2013

Improving the quality of spatial quantum channels

We show, for the non-Markovian or time-dependent Markovian process, by breaking up a spatial quantum channel (SQC) into a series of periodically arranged sub-components, that the quality of information transmission described by the purity, fidelity and concurrence of the output states can be improved. The relation to the quantum zero effect and possible applications of the idea have been discussed.

quant-ph↗

arXiv2012

Non-Markovian dynamics for an open two-level system without rotating wave approximation: Indivisibility versus backflow of information

By use of the two measures presented recently, the indivisibility and the backflow of information, we study the non-Markovianity of the dynamics for a two-level system interacting with a zero-temperature structured environment without using rotating wave approximation (RWA). In the limit of weak coupling between the system and the reservoir, and by expanding the time-convolutionless (TCL) generator to the forth order with respect to the coupling strength, the time-local non-Markovian master equation for the reduced state of the system is derived. Under the secular approximation, the exact analytic solution is obtained and the sufficient and necessary conditions for the indivisibility and the backflow of information for the system dynamics are presented. In the more general case, we investigate numerically the properties of the two measures for the case of Lorentzian reservoir. Our results show the importance of the counter-rotating terms to the short-time-scale non-Markovian behavior of the system dynamics, further expose the relations between the two measures and their rationality as non-Markovian measures. Finally, the complete positivity of the dynamics of the considered system is discussed.

quant-ph↗

arXiv2011

Equivalence of the measures of non-Markovianty for open two-level systems

In order to depict the deviation of quantum time evolution in open systems from Markovian processes, different measures have been presented. We demonstrate that the measure proposed by Breuer, Laine and Piilo [Phys. Rev. Lett. 103, 210401 (2009)] and the two measures proposed by Rivas, Huelga and Plenio [Phys. Rev. Lett. 105, 050403 (2010)] have exactly the same non-Markovian time-evolution intervals and thus are really equivalent each other when they apply to open two-level systems coupled to environments via Jaynes-Cummings or dephasing models. This equivalence implies that the three measures in different ways capture the intrinsical characters of non-Markovianty of quantum evolutional processes. We also show that the maximization in the definition of the first measure can be actually removed for the considered models without influencing the sensibility of the measure to detect non-Markovianty.

quant-ph↗