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Yunfan Zhang

Publications and source records attributed to Yunfan Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Spontaneous spin splitting and tunable valley polarization in a two-dimensional fully compensated ferrimagnet

Materials with controllable valley polarization and anomalous valley Hall (AVH) effect are highly desired in valleytronic applications. While current AVH studies primarily focus on ferromagnetic materials, two-dimensional (2D) antiferromagnets are more attractive for valleytronics since they possess zero net magnetization, negligible stray fields, and ultrafast spin dynamics. Nevertheless, the joint space-inversion and time-reversal ($PT$) symmetry in conventional collinear antiferromagnets prohibits the occurrence of AVH response. The recently proposed fully compensated ferrimagnets break $PT$ symmetry, and the spin-opposite sublattices are not related by crystal symmetry, providing a natural platform for the coexistence of spontaneous spin splitting, valley polarization, and anomalous-Hall compatible symmetry. Herein, we demonstrate that such compensated ferrimagnetism can be realized in a Janus Mn$_{2}$BrI monolayer, with a Néel temperature above room temperature. Spontaneous spin splitting is observed due to the built-in layer-dependent electrostatic potential. When SOC is considered, valley polarization emerges for an out-of-plane Néel vector. Moreover, proper hole doping stabilizes the perpendicular magnetic anisotropy and the two valleys exhibit markedly different Berry curvatures, thereby making AHE responses allowed. Furthermore, the valence band extrema of Mn$_{2}$BrI monolayer can be effectively tuned by external biaxial strain and giant piezomagnetism can be achieved. Our results identify Janus Mn$_{2}$BrI monolayer as a promising fully compensated ferrimagnetic platform for 2D valleytronics and spintronics.

cond-mat.mtrl-sci↗
arXiv2026

How to Ask the AI: A User Perspective Survey for Large Language Model Prompting

AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc., which are also known as LLM prompts. Crafting clear and well-structured prompts leads to more appropriate LLM feedback, which effectively bridges human-LLM interaction. Although prompting appears accessible to non-expert users, precisely organizing effective prompts is a highly systematic and skillful process, presenting potential challenges even for experienced users. This survey explores the principles, taxonomy, and organization of prompts from a user-centered perspective. Differing from the existing surveys that primarily focus on technical principles and application scenarios of LLMs, this paper provides actionable guidelines for formulating effective LLM prompts across diverse real-world tasks and specifically contributes by: 1) developing an intuitive evaluation strategy for prompt efficacy, 2) providing prompting workflow demonstrations on representative applications, and 3) maintaining a dynamically updated open-source project to ensure the core takeaways remain up-to-date. These measures lower the threshold for users to correctly understand and craft prompts that align with evolving application scenarios. This work will be maintained as a living GitHub project \href{https://github.com/Yunfan-Zhang/TAI_Guideline-Table}{\textcolor{blue}{here}}.

cs.HC↗
arXiv2026

Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing

Selecting which products to display and at what prices is a central decision in retail and e-commerce operations. In many applications, these two choices must be made jointly under limited display capacity and uncertain customer demand. In this paper, we study the joint assortment and pricing problem under a price-based contextual multinomial logit model, where customer preferences depend on both product features and selling prices. Our analysis begins with the construction of a new confidence region for demand estimation under price-dependent features. Building on this result, we develop a pessimistic offline algorithm and SupCB-type online algorithms for joint assortment and pricing optimization. In the offline setting, we establish a suboptimality guarantee governed by local information around the optimal assortment-price pair, rather than by exact coverage of the optimal action. In the online setting, our SupCB-type algorithm improves the best previously known regret bound to $\widetilde{O}(W\sqrt{dT\log N}/L_0)$, and we also provide a computationally simpler Thompson-sampling alternative. When specialized to the assortment-only or pricing-only setting, our bounds recover the near-minimax-optimal rates established in those respective domains, thereby bridging the gap between the mature study on assortment optimization or dynamic pricing and the limited literature on their joint optimization.

math.OC↗
arXiv2026

Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering

Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level statistical co-occurrence to infer the latent metric space, often overlooking the intrinsic semantic knowledge encapsulated in feature names and values. As a result, semantically related concepts like `Flu' and `Cold' are often treated as symbolic tokens, causing conceptually related samples to be isolated. To bridge the gap between dataset-specific statistics and intrinsic semantic knowledge, this paper proposes Tabular-Augmented Contrastive Clustering (TagCC), a novel framework that anchors statistical tabular representations to open-world textual concepts. Specifically, TagCC utilizes Large Language Models (LLMs) to distill underlying data semantics into textual anchors via semantic-aware transformation. Through Contrastive Learning (CL), the framework enriches the statistical tabular representations with the open-world semantics encapsulated in these anchors. This CL framework is jointly optimized with a clustering objective, ensuring that the learned representations are both semantically coherent and clustering-friendly. Extensive experiments on benchmark datasets demonstrate that TagCC significantly outperforms its counterparts.

cs.AI↗
arXiv2026

Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration

Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchical multiscale structure-function coupling framework for connectome integration that jointly learns individualized modular organization and hierarchical coupling across structural connectivity (SC) and functional connectivity (FC). The framework includes: (i) Prototype-based Modular Pooling (PMPool), which learns modality-specific multiscale communities by selecting prototypical ROIs and optimizing a differentiable modularity-inspired objective; (ii) an Attention-based Hierarchical Coupling Module (AHCM) that models both within-hierarchy and cross-hierarchy SC-FC interactions to produce enriched hierarchical coupling representations; and (iii) a Coupling-guided Clustering loss (CgC-Loss) that regularizes SC and FC community assignments with coupling signals, allowing cross-modal interactions to shape community alignment across hierarchies. We evaluate the model's performance across four cohorts for predicting brain age, cognitive score, and disease classification. Our model consistently outperforms baselines and other state-of-the-art approaches across three tasks. Ablation and sensitivity analyses verify the contributions of key components. Finally, the visualizations of learned coupling reveal interpretable differences, suggesting that the framework captures biologically meaningful structure-function relationships.

q-bio.NC↗
arXiv2026

FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications

Instruction following is critical for LLMs deployed in enterprise and API-driven settings, where strict adherence to output formats, content constraints, and procedural requirements is essential for enabling reliable LLM-assisted workflows. However, existing instruction following benchmarks predominantly evaluate natural language generation constraints that reflect the needs of chat assistants rather than enterprise users. To bridge this gap, we introduce FireBench, an LLM instruction following benchmark grounded in real-world enterprise and API usage patterns. FireBench evaluates six core capability dimensions across diverse applications including information extraction, customer support, and coding agents, comprising over 2,400 samples. We evaluate 11 LLMs and present key findings on their instruction following behavior in enterprise scenarios. We open-source FireBench at fire-bench.com to help users assess model suitability, support model developers in diagnosing performance, and invite community contributions.

cs.CL↗
arXiv2026

LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News

Large Language Models (LLMs) with agentic web search capabilities show strong potential for tasks requiring real-time information access and complex fact retrieval, yet evaluating such systems remains challenging. We introduce \bench, a rigorous and regularly updated benchmark designed to assess the agentic web search abilities of LLMs. \bench automatically generates fresh question-answer pairs from recent news articles, ensuring that questions require information beyond an LLM's training data and enabling clear separation between internal knowledge and search capability. The benchmark features intentionally difficult questions requiring multi-hop search queries, page visits, and reasoning, making it well-suited for evaluating agentic search behavior. Our automated data curation and question generation pipeline enables frequent benchmark updates and supports construction of a large-scale training dataset for agentic web search models, addressing the scarcity of such data in the research community. To ensure reliable evaluation, we include a subset of human-verified samples in the test set. We evaluate a broad range of systems using \bench, including commercial and open-weight LLMs as well as LLM-based web search APIs. The leaderboard, datasets, and code are publicly available at livenewsbench.com.

cs.IR↗
arXiv2025

Deep Learning Waveform Channel Modeling for Wideband Optical Fiber Transmission: Model Comparisons, Challenges and Potential Solutions

Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical network configurations, and advancing the optical fiber transmission system towards wideband. Deep learning (DL) has emerged as a powerful tool for waveform modeling, offering high accuracy and low complexity compared to traditional split-step Fourier method (SSFM), due to its strong nonlinear fitting capabilities and efficient parallel computation. However, most DL methods are designed for few-channel and low-rate WDM systems, leaving their scalability to wideband systems uncertain. Moreover, the lack of a standardized accuracy evaluation method and the inconsistent results between waveform errors and transmission performance errors, hinders fair comparisons of various DL schemes. In this paper, we introduce a DSP-assisted accuracy evaluation method integrated with nonlinear DSP, providing a fair benchmark for evaluating the accuracy of DL models. Using this method, we conduct a comprehensive comparison of DL schemes, ranging from simple configurations to more complex wideband setups. The feature decoupled distributed method combining with bidirectional long short-term memory achieves the better performance compared to other DL schemes. Furthermore, in scenarios with more-channel and higher-rate, the performance advantages of FDD-BiLSTM will be further improved. However, as the number of channels and symbol rates increase, the performance of FDD-BiLSTM still gradually deteriorate. We analyze these challenges from three perspectives: the more intricate linear and nonlinear effects, the higher sampling rate required for SSFM. To address these challenges, we discuss potential solutions from two aspects: incorporating more prior physical knowledge and optimizing the structure of DL models.

eess.SP↗
arXiv2025

Forecasting Conversation Derailments Through Generation

Forecasting conversation derailment can be useful in real-world settings such as online content moderation, conflict resolution, and business negotiations. However, despite language models' success at identifying offensive speech present in conversations, they struggle to forecast future conversation derailments. In contrast to prior work that predicts conversation outcomes solely based on the past conversation history, our approach samples multiple future conversation trajectories conditioned on existing conversation history using a fine-tuned LLM. It predicts the conversation outcome based on the consensus of these trajectories. We also experimented with leveraging socio-linguistic attributes, which reflect turn-level conversation dynamics, as guidance when generating future conversations. Our method of future conversation trajectories surpasses state-of-the-art results on English conversation derailment prediction benchmarks and demonstrates significant accuracy gains in ablation studies.

cs.CL↗
arXiv2025

Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment

Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced human perspectives. Recent research has underscored the importance of enabling LLMs to support steerable pluralism -- the capacity to adopt a specific perspective and align generated outputs with it. In this work, we investigate whether Chain-of-Thought (CoT) reasoning techniques can be applied to building steerable pluralistic models. We explore several methods, including CoT prompting, fine-tuning on human-authored CoT, fine-tuning on synthetic explanations, and Reinforcement Learning with Verifiable Rewards (RLVR). We evaluate these approaches using the Value Kaleidoscope and OpinionQA datasets. Among the methods studied, RLVR consistently outperforms others and demonstrates strong training sample efficiency. We further analyze the generated CoT traces with respect to faithfulness and safety.

cs.CL↗
arXiv2025

On Decision-Dependent Uncertainties in Power Systems with High-Share Renewables

The continuously increasing renewable energy sources (RES) and demand response (DR) are becoming important sources of system flexibility. As a consequence, decision-dependent uncertainties (DDUs), interchangeably referred to as endogenous uncertainties, impose new characteristics to power system dispatch. The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR, while reserve providers encounter DDUs arising from the uncertain reserve deployment. This paper presents a systematic framework for addressing robust dispatch problems with DDUs. The main contributions include i) the robust characterization of DDUs with a dependency decomposition structure; ii) a generic DDU coping mechanism, manifested as the bilateral matching between uncertainty and flexibility; iii) analyses of the influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems; and iv) generic solution algorithms adaptive for DDUs. Under this framework, the inherent distinctions and correlations between DDUs and DIUs are revealed, providing a fundamental theoretical basis for the economic and reliable operation of RES-dominated power systems. Applications in the source and demand sides illustrate the importance of considering DDUs and verify the effectiveness of proposed algorithms for robust dispatch with DDUs.

eess.SY↗
arXiv2025

InsTex: Indoor Scenes Stylized Texture Synthesis

Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant challenges remain in achieving broad generalization and maintaining style consistency across multiple viewpoints. Current methods, such as 2D diffusion models adapted for 3D texturing, suffer from lengthy processing times and visual artifacts, while approaches driven by 3D data often fail to generalize effectively. To overcome these challenges, we introduce InsTex, a two-stage architecture designed to generate high-quality, style-consistent textures for 3D indoor scenes. InsTex utilizes depth-to-image diffusion priors in a coarse-to-fine pipeline, first generating multi-view images with a pre-trained 2D diffusion model and subsequently refining the textures for consistency. Our method supports both textual and visual prompts, achieving state-of-the-art results in visual quality and quantitative metrics, and demonstrates its effectiveness across various 3D texturing applications.

cs.CV↗
arXiv2024

Asynchronous Federated Clustering with Unknown Number of Clusters

Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, some tricky but common FC problems are still relatively unexplored, including the heterogeneity in terms of clients' communication capacity and the unknown number of proper clusters $k^*$. To further bridge the gap between FC and real application scenarios, this paper first shows that the clients' communication asynchrony and unknown $k^*$ are complex coupling problems, and then proposes an Asynchronous Federated Cluster Learning (AFCL) method accordingly. It spreads the excessive number of seed points to the clients as a learning medium and coordinates them across the clients to form a consensus. To alleviate the distribution imbalance cumulated due to the unforeseen asynchronous uploading from the heterogeneous clients, we also design a balancing mechanism for seeds updating. As a result, the seeds gradually adapt to each other to reveal a proper number of clusters. Extensive experiments demonstrate the efficacy of AFCL.

cs.LG↗
arXiv2024

SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing Values

Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem because it highly relies on domain knowledge. Large language models (LLMs) have emerged as a promising tool for data cleaning, including MVI for tabular data, offering advanced capabilities for understanding and generating content. However, despite their promise, existing LLM techniques such as in-context learning and Chain-of-Thought (CoT) often fall short in guiding LLMs to perform complex reasoning for MVI, particularly when imputing derived missing values, which require mathematical formulas and data relationships across rows and columns. This gap underscores the need for further advancements in LLM methodologies to enhance their reasoning capabilities for more reliable imputation outcomes. To fill this gap, we propose SketchFill, a novel sketch-based method to guide LLMs in generating accurate formulas to impute missing numerical values. Our experimental results demonstrate that SketchFill significantly outperforms state-of-the-art methods, achieving 56.2% higher accuracy than CoT-based methods and 78.8% higher accuracy than MetaGPT. This sets a new standard for automated data cleaning and advances the field of MVI for numerical values.

cs.CL↗
arXiv2024

Multi-modal Learnable Queries for Image Aesthetics Assessment

Image aesthetics assessment (IAA) is attracting wide interest with the prevalence of social media. The problem is challenging due to its subjective and ambiguous nature. Instead of directly extracting aesthetic features solely from the image, user comments associated with an image could potentially provide complementary knowledge that is useful for IAA. With existing large-scale pre-trained models demonstrating strong capabilities in extracting high-quality transferable visual and textual features, learnable queries are shown to be effective in extracting useful features from the pre-trained visual features. Therefore, in this paper, we propose MMLQ, which utilizes multi-modal learnable queries to extract aesthetics-related features from multi-modal pre-trained features. Extensive experimental results demonstrate that MMLQ achieves new state-of-the-art performance on multi-modal IAA, beating previous methods by 7.7% and 8.3% in terms of SRCC and PLCC, respectively.

cs.CV↗
arXiv2024

iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching

We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. Conventional pose estimation methods typically rely on the target's CAD model or necessitate specific network training tailored to particular object classes. Some existing methods have achieved promising results in mesh-free object and scene pose estimation by inverting the Neural Radiance Fields (NeRF). However, they still struggle with adverse initializations such as large rotations and translations. To address this issue, we propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS). Specifically, a gradient-based differentiable framework optimizes camera pose by minimizing the residual between the query image and the rendered image, requiring no training. An end-to-end matching module is designed to enhance the model's robustness against adverse initializations, while minimizing pixel-level comparing loss aids in precise pose estimation. Experimental results on synthetic and complex real-world data demonstrate the effectiveness of the proposed approach in challenging conditions and the accuracy of camera pose estimation.

cs.CV↗
arXiv2024

Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects

Controllable 3D indoor scene synthesis stands at the forefront of technological progress, offering various applications like gaming, film, and augmented/virtual reality. The capability to stylize and de-couple objects within these scenarios is a crucial factor, providing an advanced level of control throughout the editing process. This control extends not just to manipulating geometric attributes like translation and scaling but also includes managing appearances, such as stylization. Current methods for scene stylization are limited to applying styles to the entire scene, without the ability to separate and customize individual objects. Addressing the intricacies of this challenge, we introduce a unique pipeline designed for synthesis 3D indoor scenes. Our approach involves strategically placing objects within the scene, utilizing information from professionally designed bounding boxes. Significantly, our pipeline prioritizes maintaining style consistency across multiple objects within the scene, ensuring a cohesive and visually appealing result aligned with the desired aesthetic. The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity. These scenes are crafted in response to various natural language prompts, demonstrating the versatility and adaptability of our model.

cs.CV↗