arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2025

SpatialSplat: Efficient Semantic 3D from Sparse Unposed Images

A major breakthrough in 3D reconstruction is the feedforward paradigm to generate pixel-wise 3D points or Gaussian primitives from sparse, unposed images. To further incorporate semantics while avoiding the significant memory and storage costs of high-dimensional semantic features, existing methods extend this paradigm by associating each primitive with a compressed semantic feature vector. However, these methods have two major limitations: (a) the naively compressed feature compromises expressiveness, affecting the model's ability to capture fine-grained semantics, and (b) the pixel-wise primitive prediction introduces redundancy in overlapping areas, causing unnecessary memory overhead. To this end, we introduce \textbf{SpatialSplat}, a feedforward framework that produces redundancy-aware Gaussians and capitalizes on a dual-field semantic representation. Particularly, with the insight that primitives within the same instance exhibit high semantic consistency, we decompose the semantic representation into a coarse feature field that encodes uncompressed semantics with minimal primitives, and a fine-grained yet low-dimensional feature field that captures detailed inter-instance relationships. Moreover, we propose a selective Gaussian mechanism, which retains only essential Gaussians in the scene, effectively eliminating redundant primitives. Our proposed Spatialsplat learns accurate semantic information and detailed instances prior with more compact 3D Gaussians, making semantic 3D reconstruction more applicable. We conduct extensive experiments to evaluate our method, demonstrating a remarkable 60\% reduction in scene representation parameters while achieving superior performance over state-of-the-art methods. The code is available at https://github.com/shengyuuu/SpatialSplat.git

cs.CV↗

arXiv2025

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks

A B testing serves as the gold standard for large scale, data driven decision making in online businesses. To mitigate metric variability and enhance testing sensitivity, control variates and regression adjustment have emerged as prominent variance reduction techniques, leveraging pre experiment data to improve estimator performance. Over the past decade, these methods have spawned numerous derivatives, yet their theoretical connections and comparative properties remain underexplored. In this paper, we conduct a comprehensive analysis of their statistical properties, establish a formal bridge between the two frameworks in practical implementations, and extend the investigation from design based to model-based frameworks. Through simulation studies and real world experiments at ByteDance, we validate our theoretical insights across both frameworks. Our work aims to provide rigorous guidance for practitioners in online controlled experiments, addressing critical considerations of internal and external validity. The recommended method control variates with group specific coefficient estimates has been fully implemented and deployed on ByteDance's experimental platform.

stat.ME↗

arXiv2025

Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations

In the age of AI-powered educational (AIED) innovation, evaluating the developmental consequences of novel designs before they are exposed to students has become both essential and challenging. Since such interventions may carry irreversible effects, it is critical to anticipate not only potential benefits but also possible harms. This study proposes a student development agent framework based on large language models (LLMs), designed to simulate how students with diverse characteristics may evolve under different educational settings without administering them to real students. By validating the approach through a case study on a multi-agent learning environment (MAIC), we demonstrate that the agent's predictions align with real student outcomes in non-cognitive developments. The results suggest that LLM-based simulations hold promise for evaluating AIED innovations efficiently and ethically. Future directions include enhancing profile structures, incorporating fine-tuned or small task-specific models, validating effects of empirical findings, interpreting simulated data and optimizing evaluation methods.

cs.CY↗

arXiv2025

Observation of multiple time crystals in a driven-dissipative system with Rydberg gas

Time crystals, as temporal analogs of space crystals, manifest as stable and periodic behavior that breaks time translation symmetry. In an open quantum system, many-body interaction subjected to dissipation allows one to develop the time crystalline order in an unprecedented way, as refer to dissipative time crystals. Here we report the observation of multiple time crystals in the continuously driven-dissipative and strongly interacting Rydberg thermal gases, in which continuous time crystals, sub-harmonic time crystals, and high-harmonic time crystals are observed in the same system by manipulating the Rydberg excitation. Our work provides new ways to explore the nonequilibrium phases of matter in open systems. Such time crystals with persistent oscillation rooted in emergent quantum correlations, may emerge as a ubiquitous tool in quantum metrology, for instance, continuous sensing and parameter estimation surpassing the standard quantum limit.

physics.atom-ph↗

arXiv2025

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning

Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-correction, especially compared to their unimodal text-based counterparts. Existing reflection methods are simplistic and struggle to generate meaningful and instructive feedback, as the reasoning ability and knowledge limits of pre-trained models are largely fixed during initial training. To overcome these challenges, we propose Multimodal Self-Reflection enhanced reasoning with Group Relative Policy Optimization (SRPO), a two-stage reflection-aware reinforcement learning (RL) framework explicitly designed to enhance multimodal LLM reasoning. In the first stage, we construct a high-quality, reflection-focused dataset under the guidance of an advanced MLLM, which generates reflections based on initial responses to help the policy model learn both reasoning and self-reflection. In the second stage, we introduce a novel reward mechanism within the GRPO framework that encourages concise and cognitively meaningful reflection while avoiding redundancy. Extensive experiments across multiple multimodal reasoning benchmarks, including MathVista, MathVision, MathVerse, and MMMU-Pro, using Qwen-2.5-VL-7B and Qwen-2.5-VL-32B demonstrate that SRPO significantly outperforms state-of-the-art models, achieving notable improvements in both reasoning accuracy and reflection quality.

cs.CL↗

arXiv2025

ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models

Mixture-of-Experts (MoE) models promise efficient scaling of large language models (LLMs) by activating only a small subset of experts per token, but their parallelized inference pipelines make elastic serving challenging. Existing strategies fall short: horizontal scaling provisions entire replicas of the current configuration, often tens to hundreds of accelerators, leading to coarse granularity, long provisioning delays, and costly overprovisioning. Vertical scaling offers finer adjustments but typically requires instance restarts, incurring downtime. These limitations make current approaches ill-suited for the bursty, short-lived traffic patterns common in cloud deployments. We present ElasticMoE, an elastic scaling framework for MoE LLMs that achieves fine-grained, low-latency, and zero-downtime scaling. ElasticMoE decouples inference execution from memory operations, enabling scaling steps to proceed concurrently with serving. An HBM Management Module (HMM) reuses weights and KV caches via zero-copy remapping, while high-bandwidth peer-to-peer transfers bring newly added accelerators online without interrupting service. A virtual memory based expert redistribution mechanism migrates MoE experts without costly buffer reallocations, reducing peak memory usage during expert parallelism reconfiguration. Our evaluation on Ascend NPUs with three popular MoE LLMs shows that ElasticMoE achieves up to 9x lower scale-up latency, up to 2x better throughput during scaling, and significantly improves SLO attainment compared to baselines. By enabling fine-grained, concurrent scaling with minimal disruption, ElasticMoE advances the practicality of deploying massive MoE LLMs in dynamic cloud environments.

cs.DC↗

arXiv2025

Alternating Training-based Label Smoothing Enhances Prompt Generalization

Recent advances in pre-trained vision-language models have demonstrated remarkable zero-shot generalization capabilities. To further enhance these models' adaptability to various downstream tasks, prompt tuning has emerged as a parameter-efficient fine-tuning method. However, despite its efficiency, the generalization ability of prompt remains limited. In contrast, label smoothing (LS) has been widely recognized as an effective regularization technique that prevents models from becoming over-confident and improves their generalization. This inspires us to explore the integration of LS with prompt tuning. However, we have observed that the vanilla LS even weakens the generalization ability of prompt tuning. To address this issue, we propose the Alternating Training-based Label Smoothing (ATLaS) method, which alternately trains with standard one-hot labels and soft labels generated by LS to supervise the prompt tuning. Moreover, we introduce two types of efficient offline soft labels, including Class-wise Soft Labels (CSL) and Instance-wise Soft Labels (ISL), to provide inter-class or instance-class relationships for prompt tuning. The theoretical properties of the proposed ATLaS method are analyzed. Extensive experiments demonstrate that the proposed ATLaS method, combined with CSL and ISL, consistently enhances the generalization performance of prompt tuning. Moreover, the proposed ATLaS method exhibits high compatibility with prevalent prompt tuning methods, enabling seamless integration into existing methods.

cs.CV↗

arXiv2025

Strongly Entangled Kondo and Kagome Lattices and the Emergent Magnetic Ground State in Heavy-Fermion Kagome Metal YbV$_6$Sn$_6$

Applying angle-resolved photoemission spectroscopy and density functional theory calculations, we present compelling spectroscopic evidence demonstrating the intertwining and mutual interaction between the Kondo and kagome sublattices in heavy-fermion intermetallic compound YbV$_6$Sn$_6$. We reveal the Yb 4$f$-derived states near the Fermi level, along with the presence of bulk kagome bands and topological surface states. We unveil strong interactions between the 4$f$ and itinerant electrons, where the kagome bands hosting the Dirac fermions and van Hove singularities predominate. Such findings are well described using a $c$-$f$ hybridization model. On the other hand, our systematic characterization of magnetic properties demonstrates an unusually enhanced antiferromagnetic ordering, where the kagome-derived van Hove singularities near $E_F$ play a vital role in determining the unconventional nature of the Ruderman-Kittel-Kasuya-Yosida interaction and Kondo coupling. These unique kagome-state-mediated exchange interactions have never been reported before and could lead to a novel phase diagram and various quantum critical behaviors in YbV$_6$Sn$_6$ and its siblings. Our results not only expand the family of exotic quantum phases entangled with kagome structure to the strongly correlated regime, but also establish YbV$_6$Sn$_6$ as an unprecedented platform to explore unconventional many-body physics beyond the standard Kondo picture.

cond-mat.str-el↗

arXiv2025

Active Shadowing (ASD): Manipulating Perception of Robotic Behaviors via Implicit Virtual Communication

Explicit communication is often valued for its directness in presenting information but requires attention during exchange, resulting in cognitive interruptions. On the other hand, implicit communication contributes to tacit and smooth interaction, making it more suitable for teaming, but requires inference for interpretation. This paper studies a novel type of implicit visual communication (IVC) using shadows via visual projection with augmented reality, referred to as active shadowing (ASD). Prior IVC methods, such as legible motion, are often used to influence the perception of robot behavior to make it more understandable. They often require changing the physical robot behavior, resulting in suboptimality. In our work, we investigate how ASD can be used to achieve similar effects without losing optimality. Our evaluations with user studies demonstrates that ASD can effectively creates ''illusions'' that maintain optimal physical behavior without compromising its understandability. We also show that ASD can be more informative than other explicit communication methods, and examine the conditions under which ASD becomes less effective.

cs.RO↗

arXiv2025

Sim2Field: End-to-End Development of AI RANs for 6G

Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), the wireless industry is now broadly pushing for the adoption of AI in 5G and future 6G technology. Despite this enthusiasm, AI-based wireless systems still remain largely untested in the field. Common simulation methods for generating datasets for AI model training suffer from "reality gap" and, as a result, the performance of these simulation-trained models may not carry over to practical cellular systems. Additionally, the cost and complexity of developing high-performance proof-of-concept implementations present major hurdles for evaluating AI wireless systems in the field. In this work, we introduce a methodology which aims to address the challenges of bringing AI to real networks. We discuss how detailed Digital Twin simulations may be employed for training site-specific AI Physical (PHY) layer functions. We further present a powerful testbed for AI-RAN research and demonstrate how it enables rapid prototyping, field testing and data collection. Finally, we evaluate an AI channel estimation algorithm over-the-air with a commercial UE, demonstrating that real-world throughput gains of up to 40% are achievable by incorporating AI in the physical layer.

cs.NI↗

arXiv2025

Fingerprinting Organic Molecules for the Inverse Design of Two-Dimensional Hybrid Perovskites with Target Energetics

Artificial intelligence (AI)-assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified new organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.

cond-mat.mtrl-sci↗

arXiv2025

A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25--100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, for instance, multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

quant-ph↗

arXiv2025

CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models

Watermarking algorithms for Large Language Models (LLMs) effectively identify machine-generated content by embedding and detecting hidden statistical features in text. However, such embedding leads to a decline in text quality, especially in low-entropy scenarios where performance needs improvement. Existing methods that rely on entropy thresholds often require significant computational resources for tuning and demonstrate poor adaptability to unknown or cross-task generation scenarios. We propose \textbf{C}ontext-\textbf{A}ware \textbf{T}hreshold watermarking ($\myalgo$), a novel framework that dynamically adjusts watermarking intensity based on real-time semantic context. $\myalgo$ partitions text generation into semantic states using logits clustering, establishing context-aware entropy thresholds that preserve fidelity in structured content while embedding robust watermarks. Crucially, it requires no pre-defined thresholds or task-specific tuning. Experiments show $\myalgo$ improves text quality in cross-tasks without sacrificing detection accuracy.

cs.CR↗

arXiv2025

Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation

Identifying reaction conditions that are broadly applicable across diverse substrates is a longstanding challenge in chemical and pharmaceutical research. While many methods are available to generate conditions with acceptable performance, a universal approach for reliably discovering effective conditions during reaction exploration is rare. Consequently, current reaction optimization processes are often labor-intensive, time-consuming, and costly, relying heavily on trial-and-error experimentation. Nowadays, large language models (LLMs) are capable of tackling chemistry-related problems, such as molecule design and chemical reasoning tasks. Here, we report the design, implementation and application of Chemma-RC, a text-augmented multimodal LLM to identify effective conditions through task-specific dialogue and condition generation. Chemma-RC learns a unified representation of chemical reactions by aligning multiple modalities-including text corpus, reaction SMILES, and reaction graphs-within a shared embedding module. Performance benchmarking on datasets showed high precision in identifying optimal conditions, with up to 17% improvement over the current state-of-the-art methods. A palladium-catalysed imidazole C-H arylation reaction was investigated experimentally to evaluate the functionalities of the Chemma-RC in practice. Our findings suggest that Chemma-RC holds significant potential to accelerate high-throughput condition screening in chemical synthesis.

cs.AI↗

arXiv2025

Safe Explicable Policy Search

When users work with AI agents, they form conscious or subconscious expectations of them. Meeting user expectations is crucial for such agents to engage in successful interactions and teaming. However, users may form expectations of an agent that differ from the agent's planned behaviors. These differences lead to the consideration of two separate decision models in the planning process to generate explicable behaviors. However, little has been done to incorporate safety considerations, especially in a learning setting. We present Safe Explicable Policy Search (SEPS), which aims to provide a learning approach to explicable behavior generation while minimizing the safety risk, both during and after learning. We formulate SEPS as a constrained optimization problem where the agent aims to maximize an explicability score subject to constraints on safety and a suboptimality criterion based on the agent's model. SEPS innovatively combines the capabilities of Constrained Policy Optimization and Explicable Policy Search to introduce the capability of generating safe explicable behaviors to domains with continuous state and action spaces, which is critical for robotic applications. We evaluate SEPS in safety-gym environments and with a physical robot experiment to show its efficacy and relevance in human-AI teaming.

cs.AI↗

arXiv2025

Extensions of a Line-Graph-Based Method for Token Routing in Decentralized Exchanges

Decentralized exchanges (DEXs) form a cornerstone of the decentralized finance (DeFi) ecosystem, processing token trades worth billions of dollars daily. Yet, a significant fraction of these trades are suboptimal: alternative routing paths could yield more target tokens. Addressing this inefficiency is both practically urgent and theoretically compelling. Building on the linear line-graph-based routing method of Zhang et al. (2025), we propose three key extensions that better capture real-world trading complexity. First, we introduce a breadth-first search (BFS) link iteration rule that reduces computational cost and average execution time without sacrificing profitability. Second, we design a route-splitting strategy that divides large trades into smaller ones, alleviating price slippage and increasing average trader profits, albeit at the cost of higher computational overhead. Third, we generalize the method beyond a single DEX to a multi-DEX aggregator setting, reflecting actual trading environments. Using empirical data from Uniswap V2 and Sushiswap V2, we demonstrate that these extensions substantially improve both computational efficiency and profitability, establishing a foundation for future routing enhancements.

cs.CE↗

arXiv2025

Emergent Inductance from Chiral Orbital Currents in a Bulk Ferrimagnet

We report the discovery of a new form of inductance in the bulk ferrimagnet Mn3Si2Te6, which features strong spin-orbit coupling, large magnetic anisotropy, and pronounced magnetoelastic interactions. Below its Curie temperature, Mn3Si2Te6 hosts chiral orbital currents (COC) that circulate within the crystal lattice and give rise to collective electronic behavior [1]. By applying a magnetic field along the hard c axis and driving the system with low-frequency currents, we uncover a giant inductive response up to millihenry scale, originating from first-order reconfigurations of COC domains. These domains act as coherent mesoscopic inductive elements that resist reversal upon current reduction, producing a large electromotive force and sharply increasing voltage. This emergent inductance defies classical models, occurs without superconductivity or engineered nanostructures, and opens a new frontier in orbital-based quantum functionality and device concepts.

cond-mat.str-el↗

arXiv2025

Survivors, Complainers, and Borderliners: Upward Bias in Online Discussions of Academic Conference Reviews

Online discussion platforms, such as community Q&A sites and forums, have become important hubs where academic conference authors share and seek information about the peer review process and outcomes. However, these discussions involve only a subset of all submissions, raising concerns about the representativeness of the self-reported review scores. In this paper, we conduct a systematic study comparing the review score distributions of self-reported submissions in online discussions (based on data collected from Zhihu and Reddit) with those of all submissions. We reveal a consistent upward bias: the score distribution of self-reported samples is shifted upward relative to the population score distribution, with this difference statistically significant in most cases. Our analysis identifies three distinct contributors to this bias: (1) survivors, authors of accepted papers who are more likely to share good results than those of rejected papers who tend to conceal bad ones; (2) complainers, authors of high-scoring rejected papers who are more likely to voice complaints about the peer review process or outcomes than those of low scores; and (3) borderliners, authors with borderline scores who face greater uncertainty prior to decision announcements and are more likely to seek advice during the rebuttal period. These findings have important implications for how information seekers should interpret online discussions of academic conference reviews.

cs.SI↗