Showing 1–50 of 79 results for author: Zou, G

DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models

Authors: Yixin Bu, Runze Xia, Guanyun Zou, Yupeng Ji, Haodong Liu, Piji Li

Abstract: Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model's true epistemic state. While recent mechanistic approaches leverage hidden state dynamics, they typically aggregate residual stream updates, conflating the distinct roles of parametric memory (Feed-Forward Networks)… ▽ More Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model's true epistemic state. While recent mechanistic approaches leverage hidden state dynamics, they typically aggregate residual stream updates, conflating the distinct roles of parametric memory (Feed-Forward Networks) and contextual processing (Attention). We argue that this aggregation obscures fine-grained mechanistic conflicts, such as memory-context misalignment, that are fundamental indicators of uncertainty. To address this, we introduce \textbf{D}ecoupled \textbf{U}pdate \textbf{D}ynamics \textbf{(DUD)}, a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions. By quantifying the independent restoration capabilities of each module, we construct a dual-stream dynamic profile that captures the model's internal fragility. Extensive experiments demonstrate that DUD significantly outperforms state-of-the-art baselines in both uncertainty estimation and calibration, while exhibiting superior cross-dataset generalization, validating decoupled dynamics as a robust proxy for model faithfulness. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: ACL 2026 Main Conference

HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents

Authors: Zian Zhai, Xingyu Tan, Gaowang Zou, Xiaoyang Wang, Wenjie Zhang

Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments. Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficie… ▽ More Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments. Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficient exploration and unreliable execution in complex tasks. To address these challenges, we model tool relations at the schema level and construct a directed Tool--Schema Hypergraph, in which tools are represented as hyperedges from their required input-schema nodes to their output-schema nodes. Furthermore, we propose HyperAgent, a Tool--Schema Hypergraph-guided framework for dynamic planning and execution. Given a task, HyperAgent first extracts a task-relevant tool context graph and uses it to guide the construction of a schema-aware Task DAG. During execution, HyperAgent dynamically realizes each subtask by constructing a state-conditioned tool support graph through deficit-oriented expansion, which identifies unresolved requirements and retrieves supporting producer tools according to the current agent state. Experiments on AppWorld demonstrate that HyperAgent improves task completion performance while reducing redundant API calls, LLM interactions, and token consumption compared with existing agent baselines. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Surface code logical operations on a superconducting quantum processor

Authors: Weiping Lin, Shaojun Guo, Yuwei Ma, Zhengzhong Yi, Kai Zhang, Jiahao Bei, Jianbin Cai, Sirui Cao, Danning Chen, Guoben Chen, Jianguo Chen, Kefu Chen, Xiawei Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Xun Ding, Zhuzhengqi Ding, Yajie Du, Bo Fan, Daojin Fan, Yuanhao Fu, Dongxin Gao , et al. (122 additional authors not shown)

Abstract: Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit super… ▽ More Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit superconducting quantum processor. We first implement a reusable primitive layer comprising merge and split, patch expansion and shrinkage, and deformations mediated by domain walls and twist defects. We then compose these primitives to realize logical state routing, the logical controlled-NOT gate, and the single-qubit Hadamard and phase gates, which together form a Clifford-generating set. All operations are implemented on distance-three rotated surface-code patches with multi-round syndrome extraction and neural-network decoding, without post-selection. Our results advance superconducting surface-code experiments from protected logical memory to active, patch-based fault-tolerant logical operations. △ Less

Submitted 1 July, 2026; originally announced July 2026.

Large-scale multimode entangling-gate synthesis in trapped-ion systems

Authors: YingYe Huang, Wentao Chen, Guoyu Zou, Xuan Fan, Jing-Ning Zhang, Kihwan Kim

Abstract: Trapped-ion systems have emerged as a leading platform for scalable quantum information processing owing to their high-fidelity operations and long-range entangling capabilities. As the number of ions in a trap increases, the growing density of collective motional modes makes the synthesis of multimode entangling gates increasingly challenging. Designing large-scale gates requires simultaneously r… ▽ More Trapped-ion systems have emerged as a leading platform for scalable quantum information processing owing to their high-fidelity operations and long-range entangling capabilities. As the number of ions in a trap increases, the growing density of collective motional modes makes the synthesis of multimode entangling gates increasingly challenging. Designing large-scale gates requires simultaneously realizing the desired spin-spin interactions, suppressing residual spin-motion entanglement, and limiting experimental control resources, leading to a high-dimensional non-convex optimization problem. Here we develop a numerical framework for multi-tone gate synthesis that directly searches for control fields satisfying these competing requirements. By employing an alternating-minimization strategy, the framework improves numerical stability and remains effective for large systems with many motional modes and target interactions. As representative demonstrations, we synthesize gates implementing all-to-all and nearest-neighbor interaction patterns in ion chains of up to N = 1000, using only global laser control. Across the parameter regimes explored here, the control resources required to maintain high-fidelity interactions do not exhibit rapid growth with system size. We extend the framework to individual addressing using a structured qLDPC target at N = 512 as an example. These results identify multimode gate synthesis as a viable route toward programmable interaction engineering in large-scale trapped-ion quantum processors. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration

Authors: Beiwen Zhang, Yongheng Liang, Guowei Zou, Haitao Wang, Hejun Wu

Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow re… ▽ More Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow responses and high inference costs. We propose Collaboration Policy Tree (Co-pi-tree), a closed-loop method that learns an executable policy tree consisting of a partner-behavior prediction tree and an agent-action selection tree. Co-pi-tree constructs a policy by distilling LLM reasoning into policy tree code. It then evaluates the policy through partner interaction, obtains feedback, and uses natural language to summarize the interaction feedback to improve problematic branches. Experiments in Overcooked-AI show that Co-pi-tree improves average reward by 35.4% over the baseline average, while reducing the number of LLM queries by 77.7% and test-time latency by 97.1%. Project page: https://beiwenzhang.github.io/Co-pi-tree/ △ Less

Submitted 7 June, 2026; originally announced June 2026.

RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration

Authors: Ganyu Zou, Linhan Wang, Chen Dai, Siji Chen, Chang-Tien Lu

Abstract: Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying solely on local sensing and communication. However, existing decentralized control methods struggle to maintain strict inter-agent distance constraints in cluttered environments, often suffering from local minima deadlocks, high frequency control os… ▽ More Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying solely on local sensing and communication. However, existing decentralized control methods struggle to maintain strict inter-agent distance constraints in cluttered environments, often suffering from local minima deadlocks, high frequency control oscillations, or limited flexibility during obstacle navigation, resulting in low success rate. To address these limitations, we propose Rigid Swarm Control (RSC), a decentralized control framework for large-scale rigid formation flocking. To escape local minima via robust long-term planning while ensuring short-term safety, RSC integrates finite-horizon trajectory predictions with a reactive artificial potential field (APF) safety controller within a hybrid architecture. Furthermore, to accelerate formation reassembly after obstacle traversal without interrupting task execution, RSC introduces an online leader-follower reconfiguration mechanism based on stable role exchange. Extensive evaluations in challenging cluttered environments with 25 UAVs demonstrate that RSC reliably unifies rigid formation maintenance, obstacle avoidance, and target tracking. Under strict success criteria - collision-free operation with a maximum relative edge-length error below 10%, RSC achieves an 83% success rate, significantly outperforming existing heuristic and learning-based baselines that fall below 5%. △ Less

Submitted 2 June, 2026; originally announced June 2026.

Comments: 8 pages, 4 figures, two-column format

MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation

Authors: Wenhao Wang, Peizhi Niu, Gongyi Zou, Xiyuan Yang, Jingxing Wang, Haoting Shi, Yaxin Du, Jingyi Chai, Xianghe Pang, Shuo Tang, Yanfeng Wang, Siheng Chen

Abstract: The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms. However, existing benchmarks predominantly focus on generic information-seeking tools and fail to capture the practical challenges posed by personal social app… ▽ More The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms. However, existing benchmarks predominantly focus on generic information-seeking tools and fail to capture the practical challenges posed by personal social applications, where tools interact with individual accounts or local databases. To bridge this critical gap, we introduce MCP-Persona, the first benchmark specifically designed for evaluating agent performance on real-world, personalized MCP tools. MCP-Persona encompasses a diverse set of widely-used applications, ranging from social media platforms like Reddit and Xiaohongshu (Rednote) to enterprise collaboration suites such as Lark (Feishu) and Slack. Our extensive experiments on various state-of-the-art (SOTA) agents demonstrate their significant struggles with personalized tool use, thereby highlighting the benchmark's crucial role in identifying and addressing these limitations. MCP-Persona is publicly available at https://github.com/wwh0411/MCP-Persona}{https://github.com/wwh0411/MCP-Persona. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Comments: ICML 2026 Camera Ready

Locating a shortest vector in certain $2$-dimensional lattices

Authors: Guixian Zou

Abstract: Let $a$, $m$ be positive integers, $1<a<m$, $\gcd(a,m)=1$. We determine the location of a shortest vector in the $2$-dimensional lattices $$ Λ(a,m) = \{(x, y)\in\mathbb{Z}\times\mathbb{Z}\mid ax + y\equiv 0~(\bmod\,m)\}. $$ This confirms a conjecture of Han Wu and Guangwu Xu. Let $a$, $m$ be positive integers, $1<a<m$, $\gcd(a,m)=1$. We determine the location of a shortest vector in the $2$-dimensional lattices $$ Λ(a,m) = \{(x, y)\in\mathbb{Z}\times\mathbb{Z}\mid ax + y\equiv 0~(\bmod\,m)\}. $$ This confirms a conjecture of Han Wu and Guangwu Xu. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 7 pages

MSC Class: 11H06; 11H55; 11Y16

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

Authors: Bin Wu, Guanyun Zou, Bingbing Wang, Huan Zhao, Chuan Shi

Abstract: A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep what a user volunteers but rarely ask for what stays unspoken, leaving a proactivity gap in long-lived LLM agents: an agent cannot act on a preference it never obtained. As users delegate more of their affairs to agents,… ▽ More A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep what a user volunteers but rarely ask for what stays unspoken, leaving a proactivity gap in long-lived LLM agents: an agent cannot act on a preference it never obtained. As users delegate more of their affairs to agents, the impact of this gap grows. We isolate one concrete, controllable slice of this gap as Ask-to-Remember (ATR): the agent decides whether to ask now for a reusable user preference that the current task does not need but a later session with the same user will. ATR is hard even to evaluate: the right question is underdetermined and its payoff deferred to tasks that may never arise. ATRBench, to the best of our knowledge the first ATR benchmark, makes it measurable by fixing each user's preferences as hidden ground truth, so success demands asking, not recall. Across eight frontier LLM agents, defaults fall at least 62 points below an oracle handed the relevant preference, and prompting closes little of it. Diagnostics identify acquisition as the bottleneck. ATRBench surfaces this proactivity gap in current agents and offers a diagnostic testbed for closing it. △ Less

Submitted 28 May, 2026; v1 submitted 27 May, 2026; originally announced May 2026.

On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note

Authors: Guangyi Zou, Roman Vershynin

Abstract: We prove an elementary bounded-differences inequality for functions of non-isotropic Gaussian vectors. Specifically, if $f$ has bounded coordinate differences and $X\sim\mathcal N(μ,Σ)$, then the resulting concentration bound depends on the condition number $κ(Σ)$. As an application, we answer a question of Simone Bombari concerning the subgaussianity of sign-quantized linear maps… ▽ More We prove an elementary bounded-differences inequality for functions of non-isotropic Gaussian vectors. Specifically, if $f$ has bounded coordinate differences and $X\sim\mathcal N(μ,Σ)$, then the resulting concentration bound depends on the condition number $κ(Σ)$. As an application, we answer a question of Simone Bombari concerning the subgaussianity of sign-quantized linear maps $Y=\mathrm{sgn}(Wx)$. In the special case where $f$ is the coordinatewise sign function, an argument was initially suggested to us by Gemini 3.5 Flash without attribution. We subsequently discovered that it closely resembles an earlier argument of Barber and Kolar [Ann. Statist. 46 (2018), Lemma 4.5]. This revision corrects the attribution and documents the episode as an instance of AI-assisted mathematical discovery. △ Less

Submitted 17 August, 2026; v1 submitted 26 May, 2026; originally announced May 2026.

Comments: 5 pages; attribution updated

MSC Class: 60G15 (Primary); 60B20; 60E15 (Secondary)

Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation

Authors: Zhiquan Chen, Haitao Wang, Guowei Zou, Hejun Wu

Abstract: Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse… ▽ More Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse during encoding and insufficient fine grained multi scale decoding. To address these issues, we propose Med DisSeg, a dispersion driven medical image segmentation framework that jointly improves representation learning and anatomical delineation. Med DisSeg combines a lightweight Dispersive Loss with adaptive attention for fine grained structure segmentation. The Dispersive Loss enlarges inter sample margins by treating in batch hidden representations as negative pairs, producing well dispersed and boundary aware embeddings with negligible overhead. Based on these enhanced representations, the encoder strengthens structure sensitive responses, while the decoder performs adaptive multi scale calibration to preserve complementary local texture and global shape information. Extensive experiments on five datasets spanning three imaging modalities demonstrate consistent state of the art performance. Moreover, Med DisSeg achieves competitive results on multi organ CT segmentation, supporting its robustness and cross task applicability. △ Less

Submitted 14 May, 2026; originally announced May 2026.

SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation

Authors: Zhiquan Chen, Haitao Wang, Guowei Zou, Hejun Wu

Abstract: Medical image segmentation remains challenging in low-data regimes, where scarce annotations often yield poor generalization and ambiguous boundaries with missing fine structures. Recent self-supervised pretraining has improved transferability, but it often exhibits a texture bias. In contrast, accurate segmentation is inherently geometry-aware and depends on both topological consistency and preci… ▽ More Medical image segmentation remains challenging in low-data regimes, where scarce annotations often yield poor generalization and ambiguous boundaries with missing fine structures. Recent self-supervised pretraining has improved transferability, but it often exhibits a texture bias. In contrast, accurate segmentation is inherently geometry-aware and depends on both topological consistency and precise boundary preservation. To address this problem, we propose a two-stage framework that couples structure-aware encoder pretraining with boundary-oriented decoding. In Stage-1, we aim to learn structure-aware representations for downstream segmentation in low-data regimes. To this end, we propose Mixed-Domain MeanFlow Pretraining, which aligns images and binary masks in a shared latent space through latent transport regression, where masks act as conditional structural guidance rather than prediction targets, making the pretraining task-agnostic. To further improve training stability under scarce supervision, we incorporate a lightweight Dispersive Loss to prevent representation collapse. In Stage-2, we fine-tune the pretrained encoder with a lightweight decoder that combines Direct Attentional Fusion for adaptive cross-scale gating and Frequency-Directional Dynamic Convolution for high-frequency boundary refinement under appearance variation. Experiments on ISIC-2016, Kvasir-SEG, and GlaS demonstrate consistent gains over state-of-the-art methods, with improved robustness in low-data settings and sharper boundary delineation. △ Less

Submitted 14 May, 2026; originally announced May 2026.

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making

Authors: Guowei Zou, Haitao Wang, Beiwen Zhang, Boning Zhang, Hejun Wu

Abstract: Generative models have emerged as a promising paradigm for offline multi-agent reinforcement learning (MARL), but existing approaches require many iterative sampling steps. Recent few-step acceleration methods either distill a joint teacher into independent students or apply averaged velocity fields independently to each agent. Unfortunately, these few-step approaches hurt inter-agent coordination… ▽ More Generative models have emerged as a promising paradigm for offline multi-agent reinforcement learning (MARL), but existing approaches require many iterative sampling steps. Recent few-step acceleration methods either distill a joint teacher into independent students or apply averaged velocity fields independently to each agent. Unfortunately, these few-step approaches hurt inter-agent coordination. We show that the efficiency-coordination trade-off is not inherent: single-pass multi-agent generation can preserve coordination when the velocity field is natively joint-coupled. We propose Coordinated few-step Flow (CoFlow), an architecture that combines Coordinated Velocity Attention (CVA) with Adaptive Coordination Gating. A finite-difference consistency surrogate further replaces memory-prohibitive Jacobian-vector product backpropagation through the averaged velocity field with two stop-gradient forward passes. Across 60 configurations spanning MPE, MA-MuJoCo, and SMAC, CoFlow matches or surpasses Gaussian policies, value-based methods, transformer policies, diffusion models, and prior flow baselines on episodic return. Three independent coordination probes confirm that CoFlow's improvements arise from inter-agent coordination rather than per-agent capacity. A denoising-step sweep shows that single-pass inference suffices on every configuration. CoFlow reaches state-of-the-art coordination quality in 1-3 denoising steps under both centralized and decentralized execution. Project Page: https://guowei-zou.github.io/coflow/ △ Less

Submitted 12 May, 2026; v1 submitted 2 May, 2026; originally announced May 2026.

Comments: 34 pages, 15 figures, 10 tables. Project page: https://guowei-zou.github.io/coflow/

SVOM/VT: Flight Model Verification and Pre-launch Testing

Authors: Jian Zhang, Xue-Wu Fan, Gang-Yi Zou, Yu-Lei Qiu, Wei Gao, Wei Wang, Chen-Jie Wang, Ning Qi, Jin-Song Deng, Li-Jun Dan, Yue Pan, Chao Huang, Yun-Fei Du, Guo-Rui Ren, Zhong-Han Sun, Feng-Tao Wang, Wei Li, Bao-Peng Li, Chao Shen, Peng-Fei Chen, Kun Chen, Hui Zhao, Ming Chang, Tao Wang, Li-Pin Xin , et al. (1 additional authors not shown)

Abstract: This paper presents pre-launch testing and calibration results for the SVOM/VT (Space-based Variable Objects Monitor, Visible Telescope) Flight Model (FM), validating its performance under simulated space conditions through thermal vacuum cycling, energy concentration analysis, stray light suppression, and CCD/electronics calibrations (gain, noise, quantum efficiency). The results confirm full com… ▽ More This paper presents pre-launch testing and calibration results for the SVOM/VT (Space-based Variable Objects Monitor, Visible Telescope) Flight Model (FM), validating its performance under simulated space conditions through thermal vacuum cycling, energy concentration analysis, stray light suppression, and CCD/electronics calibrations (gain, noise, quantum efficiency). The results confirm full compliance with design requirements: stray light suppression achieves point-source transmittance $<10^{-7}$ at $30^\circ$ off-axis, thermal control maintains stable CCD temperatures ($-75^\circ$C for the red channel, $-65^\circ$C for the blue channel), and detection sensitivity meets the limiting magnitude of 22.50 (SNR $>$ 3 with 300 seconds exposure). Early in-orbit tests further validate performance, yielding limiting magnitudes of 22.70 (V-band, red) and 22.78 (blue), consistent with pre-launch specifications. △ Less

Submitted 27 April, 2026; originally announced April 2026.

Comments: Accepted for publication in the SVOM special issue in Reaearch in Astronomy and Astrophysics: 16pages, 14 figures, 3 tables

SVOM/VT: Instrument Overview, Science Objectives, and First-Year Performance

Authors: Yu-Lei Qiu, Li-Ping Xin, Jin-Song Deng, Jian Zhang, Xue-Wu Fan, Hong-Bo Cai, Chao Wu, Hua-Li Li, Rui-Feng Su, Qing-Yun Mao, Wei Gao, Gang-Yi Zou, Wei Wang, Zhu-Heng Yao, Dong Li, Kun Chen, Wen Chen, Yong-He Zhang, Xu-Hui Han, Jing Wang, Da-Wei Xu, Jesse T. Palmerio, Susanna. D. Vergani, Jian-Yan Wei, Bertrand Cordier

Abstract: The 44-cm Visible Telescope (VT) aboard the Space-based Variable Objects Monitor (SVOM) is a dual-band (400-650 nm and 650-1000 nm) instrument designed to detect and characterize the optical counterparts of gamma-ray bursts (GRBs) and other high-energy transients. This paper presents the VT's design, scientific objectives, observing strategies, and both space- and ground-based data processing pipe… ▽ More The 44-cm Visible Telescope (VT) aboard the Space-based Variable Objects Monitor (SVOM) is a dual-band (400-650 nm and 650-1000 nm) instrument designed to detect and characterize the optical counterparts of gamma-ray bursts (GRBs) and other high-energy transients. This paper presents the VT's design, scientific objectives, observing strategies, and both space- and ground-based data processing pipelines, along with its first-year in-orbit performance. In-orbit commissioning tests confirm a sensitivity of 22.5 AB mag (300 s exposure), extendable to $\sim\!24$ AB mag through stacking. This performance enables the VT to monitor over 100 GRBs in its first year with an exceptional $\sim\!80\%$ detection rate for \textit{SVOM}/ECLAIRS-triggered bursts and ToO-observed bursts from other missions (e.g., \textit{Swift, Fermi, Einstein Probe (EP)}), outperforming \textit{Swift}/UVOT's $\sim\!40\%$ detection rate. Beyond its exceptional detection efficiency, the VT played a key role in identifying high-redshift GRBs-most notably GRB 250314A (z = 7.3). Its deep upper limits at long wavelengths (up to 1 $μ$m) were pivotal in guiding follow-up observations with large ground-based telescopes, enabling crucial near-infrared (NIR) detections. With its rapid response, deep sensitivity, and real-time processing capabilities, the VT is a key instrument for GRB research in \textit{SVOM}-era, enabling critical studies of GRB optical afterglows, circumburst environments, relativistic jet dynamics, and the origins of optically dark bursts. △ Less

Submitted 27 April, 2026; originally announced April 2026.

Comments: Accepted for publication in the SVOM special issue in Research in Astronomy and Astrophysics; 30 pages, 23 figures

Edge Universality for Inhomogeneous Random Matrices II: Markov Chain Comparison and Critical Statistics

Authors: Dang-Zheng Liu, Guangyi Zou

Abstract: The first paper in this series introduced a \emph{short-to-long mixing} condition that captures mean-field GOE/GUE edge universality in the supercritical sparsity regime, for symmetric/Hermitian random matrices with independent entries and a Markov variance profile. This condition reduces the universality problem to the mixing properties of the underlying Markov chains. In this paper, we develop… ▽ More The first paper in this series introduced a \emph{short-to-long mixing} condition that captures mean-field GOE/GUE edge universality in the supercritical sparsity regime, for symmetric/Hermitian random matrices with independent entries and a Markov variance profile. This condition reduces the universality problem to the mixing properties of the underlying Markov chains. In this paper, we develop new \emph{short-to-long comparison} conditions that extend the analysis to the subcritical and critical sparsity regimes. Specifically, we prove that two inhomogeneous random matrices exhibit the same universal edge statistics whenever their variance-profile Markov chains are comparable, regardless of the fine details of the matrix entries. To illustrate the power of our Markov chain comparison theorem, we derive the spectral edge statistics for several prototypical models: random band matrices, the Wegner orbital model, and Hankel-profile random matrices. These comparisons uncover a rich landscape of both universal and non-universal phenomena -- shaped by geometric structure, spike patterns, and domains of stable attraction -- features that lie fundamentally beyond the reach of classical random matrix theory. △ Less

Submitted 22 April, 2026; originally announced April 2026.

Comments: 52 pages, 5 figures, 1 table

MSC Class: 60B20; 60F05

Concept-wise Attention for Fine-grained Concept Bottleneck Models

Authors: Minghong Zhong, Guoshuai Zou, Kanghao Chen, Dexia Chen, Ruixuan Wang

Abstract: Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-trained vision-language model (i.e. CLIP). However, there exist two key limitations in concept modeling. Existing methods often suffer from pre-training biases, manifested as granularity misalignment or reliance on structural priors. Moreover, fine-tunin… ▽ More Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-trained vision-language model (i.e. CLIP). However, there exist two key limitations in concept modeling. Existing methods often suffer from pre-training biases, manifested as granularity misalignment or reliance on structural priors. Moreover, fine-tuning with Binary Cross-Entropy (BCE) loss treats each concept independently, which ignores mutual exclusivity among concepts, leading to suboptimal alignment. To address these limitations, we propose Concept-wise Attention for Fine-grained Concept Bottleneck Models (CoAt-CBM), a novel framework that achieves adaptive fine-grained image-concept alignment and high interpretability. Specifically, CoAt-CBM employs learnable concept-wise visual queries to adaptively obtain fine-grained concept-wise visual embeddings, which are then used to produce a concept score vector. Then, a novel concept contrastive optimization guides the model to handle the relative importance of the concept scores, enabling concept predictions to faithfully reflect the image content and improved alignment. Extensive experiments demonstrate that CoAt-CBM consistently outperforms state-of-the-art methods. The codes will be available upon acceptance. △ Less

Submitted 1 June, 2026; v1 submitted 17 April, 2026; originally announced April 2026.

Comments: Withdrawn by authors for revision and improvement

Rank-based methods for estimating landmark win probability in longitudinal randomized controlled trials with missing data

Authors: Guangyong Zou, Shi-Fang Qui, Joshua Zou, Emma Davies Smith, Yun-Hee Choi, Yuhan Bi

Abstract: The primary analysis for longitudinal randomized controlled trials (RCTs) often compares treatment groups at the last timepoint, referred to as the landmark time. Assuming data are normally distributed and missing at random, the mixed model for repeated measures (MMRM) is widely used to conduct inference in terms of a mean difference. When outcomes violate normality assumption and/or the mean diff… ▽ More The primary analysis for longitudinal randomized controlled trials (RCTs) often compares treatment groups at the last timepoint, referred to as the landmark time. Assuming data are normally distributed and missing at random, the mixed model for repeated measures (MMRM) is widely used to conduct inference in terms of a mean difference. When outcomes violate normality assumption and/or the mean difference lacks a clear interpretation, we may quantify treatment effects using the probability that a treated participant would have a better outcome than (or win over) a control participant. For RCTs with missing data, one may apply the generalized pairwise comparison (GPC) procedure, which carries forward the results of a pairwise comparison from a previous timepoint. We propose first using ranks to converts each observation at a timepoint into a win fraction, reflecting the proportion of times that the observation is better than every observation in the comparison group. Then, we conduct inference for the win probability based on the win fractions using the MMRM to obtain the point and variance estimates. Simulation results suggest that our method performed much better than the GPC procedure. We illustrate our proposed procedure in SAS and R using data from two published trials. △ Less

Submitted 17 March, 2026; v1 submitted 12 March, 2026; originally announced March 2026.

Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems

Authors: Wentao Wang, Lifeng Han, Guangyu Zou

Abstract: The Pickup and Delivery Problem (PDP) is a fundamental and challenging variant of the Vehicle Routing Problem, characterized by tightly coupled pickup--delivery pairs, precedence constraints, and spatial layouts that often exhibit clustering. Existing deep reinforcement learning (DRL) approaches either model all nodes on a flat graph, relying on implicit learning to enforce constraints, or achieve… ▽ More The Pickup and Delivery Problem (PDP) is a fundamental and challenging variant of the Vehicle Routing Problem, characterized by tightly coupled pickup--delivery pairs, precedence constraints, and spatial layouts that often exhibit clustering. Existing deep reinforcement learning (DRL) approaches either model all nodes on a flat graph, relying on implicit learning to enforce constraints, or achieve strong performance through inference-time collaborative search at the cost of substantial latency. In this paper, we propose \emph{CAADRL} (Cluster-Aware Attention-based Deep Reinforcement Learning), a DRL framework that explicitly exploits the multi-scale structure of PDP instances via cluster-aware encoding and hierarchical decoding. The encoder builds on a Transformer and combines global self-attention with intra-cluster attention over depot, pickup, and delivery nodes, producing embeddings that are both globally informative and locally role-aware. Based on these embeddings, we introduce a Dynamic Dual-Decoder with a learnable gate that balances intra-cluster routing and inter-cluster transitions at each step. The policy is trained end-to-end with a POMO-style policy gradient scheme using multiple symmetric rollouts per instance. Experiments on synthetic clustered and uniform PDP benchmarks show that CAADRL matches or improves upon strong state-of-the-art baselines on clustered instances and remains highly competitive on uniform instances, particularly as problem size increases. Crucially, our method achieves these results with substantially lower inference time than neural collaborative-search baselines, suggesting that explicitly modeling cluster structure provides an effective and efficient inductive bias for neural PDP solvers. △ Less

Submitted 9 March, 2026; originally announced March 2026.

One Step Is Enough: Dispersive MeanFlow Policy Optimization

Authors: Guowei Zou, Haitao Wang, Hejun Wu, Yukun Qian, Yuhang Wang, Weibing Li

Abstract: Real-time robotic control demands fast action generation. However, existing generative policies based on diffusion and flow matching require multi-step sampling, fundamentally limiting deployment in time-critical scenarios. We propose Dispersive MeanFlow Policy Optimization (DMPO), a unified framework that enables true one-step generation through three key components: MeanFlow for mathematical… ▽ More Real-time robotic control demands fast action generation. However, existing generative policies based on diffusion and flow matching require multi-step sampling, fundamentally limiting deployment in time-critical scenarios. We propose Dispersive MeanFlow Policy Optimization (DMPO), a unified framework that enables true one-step generation through three key components: MeanFlow for mathematically-derived single-step inference without knowledge distillation, dispersive regularization to prevent representation collapse, and reinforcement learning (RL) fine-tuning to surpass expert demonstrations. Experiments across RoboMimic manipulation and OpenAI Gym locomotion benchmarks demonstrate competitive or superior performance compared to multi-step baselines. With our lightweight model architecture and the three key algorithmic components working in synergy, DMPO exceeds real-time control requirements (>120Hz) with 5-20x inference speedup, reaching hundreds of Hertz on high-performance GPUs. Physical deployment on a Franka-Emika-Panda robot validates real-world applicability. △ Less

Submitted 28 January, 2026; originally announced January 2026.

Comments: Code and project page: https://guowei-zou.github.io/dmpo-page/

ODMA: On-Demand Memory Allocation Strategy for LLM Serving on LPDDR-Class Accelerators

Authors: Guoqiang Zou, Wanyu Wang, Hao Zheng, Longxiang Yin, Yinhe Han

Abstract: Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access bandwidth.While static pre-allocation preserves memory contiguity,it incurs significant overhead due to worst-case provisioning.Conversely,fine-grained paging mitigates this overhead but relies on HBM's high random-access tolerance, making it unsuitable for… ▽ More Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access bandwidth.While static pre-allocation preserves memory contiguity,it incurs significant overhead due to worst-case provisioning.Conversely,fine-grained paging mitigates this overhead but relies on HBM's high random-access tolerance, making it unsuitable for LPDDR systems where non-sequential access rapidly degrades bandwidth. Furthermore, prior works typically assume static distributions and HBM characteristics, thereby failing to resolve the critical fragmentation and bandwidth constraints inherent to LPDDR hardware. We present ODMA, an on-demand memory allocation strategy tailored for random-access-constrained accelerators, such as the Cambricon MLU series.ODMA advances generation-length prediction by addressing two critical limitations in production workloads: (i) distribution drift that invalidates static bucket boundaries, and (ii) performance fragility under heavy-tailed request patterns. ODMA integrates a lightweight length predictor with adaptive bucket partitioning and a fallback safety pool. Bucket boundaries are dynamically recalibrated via online histograms to maximize utilization, while the safety pool ensures robustness against prediction errors. On Alpaca and Google-NQ benchmarks, ODMA improves S3's prediction accuracy from 98.60% to 99.55% and 82.68% to 93.36%, respectively. Deployment with DeepSeek-R1-Distill-Qwen-7B on Cambricon MLU370-X4 accelerators demonstrates that ODMA increases KV-cache utilization by up to 19.25% (absolute) and throughput (TPS) by 23-27% over static baselines, validating the efficacy of predictor-driven contiguous allocation for LPDDR-class devices. △ Less

Submitted 21 April, 2026; v1 submitted 10 December, 2025; originally announced December 2025.

Comments: 4 pages, 6 figures

ZeroOS: A Universal Modular Library OS for zkVMs

Authors: Guangxian Zou, Isaac Zhang, Ryan Zarick, Kelvin Wong, Thomas Kim, Daniel L. -K. Wong, Saeid Yazdinejad, Dan Boneh

Abstract: zkVMs promise general-purpose verifiable computation through ISA-level compatibility with modern programs and toolchains. However, compatibility extends further than just the ISA; modern programs often cannot run or even compile without an operating system and libc. zkVMs attempt to address this by maintaining forks of language-specific runtimes and statically linking them into applications to cre… ▽ More zkVMs promise general-purpose verifiable computation through ISA-level compatibility with modern programs and toolchains. However, compatibility extends further than just the ISA; modern programs often cannot run or even compile without an operating system and libc. zkVMs attempt to address this by maintaining forks of language-specific runtimes and statically linking them into applications to create self-contained unikernels, but this ad-hoc approach leads to version hell and burdens verifiable applications (vApps) with an unnecessarily large trusted computing base. We solve this problem with ZeroOS, a modular library operating system (libOS) for vApp unikernels; vApp developers can use off-the-shelf toolchains to compile and link only the exact subset of the Linux ABI their vApp needs. Any zkVM team can easily leverage the ZeroOS ecosystem by writing a ZeroOS bootloader for their platform, resulting in a reduced maintainence burden and unifying the entire zkVM ecosystem with consolidated development and audit resources. ZeroOS is free and open-sourced at https://github.com/LayerZero-Labs/ZeroOS. △ Less

Submitted 9 December, 2025; originally announced December 2025.

Self-Reinforced Deep Priors for Reparameterized Full Waveform Inversion

Authors: Guangyuan Zou, Junlun Li, Feng Liu, Xuejing Zheng, Jianjian Xie, Guoyi Chen

Abstract: Full waveform inversion (FWI) has become a widely adopted technique for high-resolution subsurface imaging. However, its inherent strong nonlinearity often results in convergence toward local minima. Recently, deep image prior-based reparameterized FWI (DIP-FWI) has been proposed to alleviate the dependence on massive training data. By exploiting the spectral bias and implicit regularization in th… ▽ More Full waveform inversion (FWI) has become a widely adopted technique for high-resolution subsurface imaging. However, its inherent strong nonlinearity often results in convergence toward local minima. Recently, deep image prior-based reparameterized FWI (DIP-FWI) has been proposed to alleviate the dependence on massive training data. By exploiting the spectral bias and implicit regularization in the neural network architecture, DIP-FWI can effectively avoid local minima and reconstruct more geologically plausible velocity models. Nevertheless, existing DIP-FWI typically use a fixed random input throughout the inversion process, which fails to utilize the mapping and correlation between the input and output of the network. Moreover, under complex geological conditions, the lack of informative prior in the input can exacerbate the ill-posedness of the inverse problem, leading to artifacts and unstable reconstructions. To address these limitations, we propose a self-reinforced DIP-FWI (SRDIP-FWI) framework, in which a steering algorithm alternately updates both the network parameters and the input at each iteration using feedback from the current network output. This design allows adaptive structural enhancement and improved regularization, thereby effectively mitigating the ill-posedness in FWI. Additionally, we analyze the spectral bias of the network in SRDIP-FWI and quantify its role in multiscale velocity model building. Synthetic tests and field land data application demonstrate that SRDIP-FWI achieves superior resolution, improved accuracy and greater depth penetration compared to multiscale FWI. More importantly, SRDIP-FWI eliminates the need for manual frequency-band selection and time-window picking, substantially simplifying the inversion workflow. Overall, the proposed method provides a novel, adaptive and robust framework for accurate subsurface velocity model reconstruction. △ Less

Submitted 9 December, 2025; originally announced December 2025.

Comments: Submitted to GEOPHYSICS

PACT: Phenotype-Aware Contrastive Team Representation for Multi-Phenotype Grouped Ad Hoc Teamwork

Authors: Beiwen Zhang, Yongheng Liang, Guowei Zou, Haitao Wang, Liu Cong, Hejun Wu

Abstract: Learning to collaborate with various unfamiliar teammates poses a great challenge in the domain of multi-agent systems. Existing ad hoc teamwork methods typically drive controlled agents to collaborate with a group of teammates exhibiting a single coordination phenotype shaped by the same reward function. However, in real-world applications, controlled agents should collaborate with unfamiliar tea… ▽ More Learning to collaborate with various unfamiliar teammates poses a great challenge in the domain of multi-agent systems. Existing ad hoc teamwork methods typically drive controlled agents to collaborate with a group of teammates exhibiting a single coordination phenotype shaped by the same reward function. However, in real-world applications, controlled agents should collaborate with unfamiliar teammates of diverse coordination phenotypes among groups that have never worked together. We formalize this as the Multi-Phenotype Grouped Ad Hoc Teamwork (MPG-AHT) problem, and propose Phenotype-Aware Contrastive Team Representation (PACT) to solve this problem. PACT is empowered with phenotype-aware contrastive learning and relational reasoning to accurately distinguish coordination phenotypes and capture inter-agent interactions. Extensive experiments on multi-phenotype collaboration tasks show that PACT outperforms state-of-the-art baselines on average, achieving a mean 21.0% gain in out-of-distribution evaluation and a mean 36.5% gain in sample efficiency. △ Less

Submitted 20 August, 2026; v1 submitted 29 October, 2025; originally announced October 2025.

DM1: MeanFlow with Dispersive Regularization for 1-Step Robotic Manipulation

Authors: Guowei Zou, Haitao Wang, Hejun Wu, Yukun Qian, Yuhang Wang, Weibing Li

Abstract: The ability to learn multi-modal action distributions is indispensable for robotic manipulation policies to perform precise and robust control. Flow-based generative models have recently emerged as a promising solution to learning distributions of actions, offering one-step action generation and thus achieving much higher sampling efficiency compared to diffusion-based methods. However, existing f… ▽ More The ability to learn multi-modal action distributions is indispensable for robotic manipulation policies to perform precise and robust control. Flow-based generative models have recently emerged as a promising solution to learning distributions of actions, offering one-step action generation and thus achieving much higher sampling efficiency compared to diffusion-based methods. However, existing flow-based policies suffer from representation collapse, the inability to distinguish similar visual representations, leading to failures in precise manipulation tasks. We propose DM1 (MeanFlow with Dispersive Regularization for One-Step Robotic Manipulation), a novel flow matching framework that integrates dispersive regularization into MeanFlow to prevent collapse while maintaining one-step efficiency. DM1 employs multiple dispersive regularization variants across different intermediate embedding layers, encouraging diverse representations across training batches without introducing additional network modules or specialized training procedures. Experiments on RoboMimic benchmarks show that DM1 achieves 20-40 times faster inference (0.07s vs. 2-3.5s) and improves success rates by 10-20 percentage points, with the Lift task reaching 99% success over 85% of the baseline. Real-robot deployment on a Franka Panda further validates that DM1 transfers effectively from simulation to the physical world. To the best of our knowledge, this is the first work to leverage representation regularization to enable flow-based policies to achieve strong performance in robotic manipulation, establishing a simple yet powerful approach for efficient and robust manipulation. △ Less

Submitted 9 October, 2025; originally announced October 2025.

Comments: Website with code: https://guowei-zou.github.io/dm1/

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents

Authors: Yaxin Du, Yuanshuo Zhang, Xiyuan Yang, Yifan Zhou, Cheng Wang, Gongyi Zou, Xianghe Pang, Wenhao Wang, Menglan Chen, Shuo Tang, Zhiyu Li, Feiyu Xiong, Siheng Chen

Abstract: Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content is noisy and unreliable, and many real-world tasks require precise, domain-specific knowledge unavailable from the web. The emergence of the Model Context Protocol (MCP) now allows agents to interface with thousands of s… ▽ More Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content is noisy and unreliable, and many real-world tasks require precise, domain-specific knowledge unavailable from the web. The emergence of the Model Context Protocol (MCP) now allows agents to interface with thousands of specialized tools, seemingly resolving this limitation. Yet it remains unclear whether agents can effectively leverage such tools -- and more importantly, whether they can integrate them with general-purpose search to solve complex tasks. Therefore, we introduce InfoMosaic-Bench, the first benchmark dedicated to multi-source information seeking in tool-augmented agents. Covering six representative domains (medicine, finance, maps, video, web, and multi-domain integration), InfoMosaic-Bench requires agents to combine general-purpose search with domain-specific tools. Tasks are synthesized with InfoMosaic-Flow, a scalable pipeline that grounds task conditions in verified tool outputs, enforces cross-source dependencies, and filters out shortcut cases solvable by trivial lookup. This design guarantees both reliability and non-triviality. Experiments with 14 state-of-the-art LLM agents reveal three findings: (i) web information alone is insufficient, with GPT-5 achieving only 38.2% accuracy and 67.5% pass rate; (ii) domain tools provide selective but inconsistent benefits, improving some domains while degrading others; and (iii) 22.4% of failures arise from incorrect tool usage or selection, highlighting that current LLMs still struggle with even basic tool handling. △ Less

Submitted 4 October, 2025; v1 submitted 2 October, 2025; originally announced October 2025.

Text2Mem: A Unified Memory Operation Language for Memory Operating System

Authors: Yi Wang, Lihai Yang, Boyu Chen, Gongyi Zou, Kerun Xu, Bo Tang, Feiyu Xiong, Siheng Chen, Zhiyu Li

Abstract: Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such as encode, retrieve, and delete, while higher order operations like merge, promote, demote, split, lock, and expire are missing or inconsistently supported. Moreover, there is no formal and executable specification for m… ▽ More Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such as encode, retrieve, and delete, while higher order operations like merge, promote, demote, split, lock, and expire are missing or inconsistently supported. Moreover, there is no formal and executable specification for memory commands, leaving scope and lifecycle rules implicit and causing unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that provides a standardized pathway from natural language to reliable execution. Text2Mem defines a compact yet expressive operation set aligned with encoding, storage, and retrieval. Each instruction is represented as a JSON based schema instance with required fields and semantic invariants, which a parser transforms into typed operation objects with normalized parameters. A validator ensures correctness before execution, while adapters map typed objects either to a SQL prototype backend or to real memory frameworks. Model based services such as embeddings or summarization are integrated when required. All results are returned through a unified execution contract. This design ensures safety, determinism, and portability across heterogeneous backends. We also outline Text2Mem Bench, a planned benchmark that separates schema generation from backend execution to enable systematic evaluation. Together, these components establish the first standardized foundation for memory control in agents. △ Less

Submitted 23 October, 2025; v1 submitted 14 September, 2025; originally announced September 2025.

Comments: 12 pages, 3 figures, 2 tables

Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving

Authors: Fujiang Yuan, Zhen Tian, Yangfan He, Guojian Zou, Chunhong Yuan, Yanhong Peng, Zhihao Lin

Abstract: In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theoretic methods and robust control, are often overly conservative or computationally intensive. Conversely, learning-based approaches typically require extensive training data and frequently exhibit limited interpretability… ▽ More In recent years, ensuring safety, efficiency, and comfort in interactive autonomous driving has become a critical challenge. Traditional model-based techniques, such as game-theoretic methods and robust control, are often overly conservative or computationally intensive. Conversely, learning-based approaches typically require extensive training data and frequently exhibit limited interpretability and generalizability. Simpler strategies, such as Risk Potential Fields (RPF), provide lightweight alternatives with minimal data demands but are inherently static and struggle to adapt effectively to dynamic traffic conditions. To overcome these limitations, we propose the Evolutionary Risk Potential Field (ERPF), a novel approach that dynamically updates risk assessments in dynamical scenarios based on historical obstacle proximity data. We introduce a Risk-Ellipse construct that combines longitudinal reach and lateral uncertainty into a unified spatial temporal collision envelope. Additionally, we define an adaptive Evolution Factor metric, computed through sigmoid normalization of Time to Collision (TTC) and Time-Window-of-Hazard (TWH), which dynamically adjusts the dimensions of the ellipse axes in real time. This adaptive risk metric is integrated seamlessly into a Model Predictive Control (MPC) framework, enabling autonomous vehicles to proactively address complex interactive driving scenarios in terms of uncertain driving of surrounding vehicles. Comprehensive comparative experiments demonstrate that our ERPF-MPC approach consistently achieves smoother trajectories, higher average speeds, and collision-free navigation, offering a robust and adaptive solution suitable for complex interactive driving environments. △ Less

Submitted 8 September, 2025; originally announced September 2025.

Realization of an untrusted intermediate relay architecture using a quantum dot single-photon source

Authors: Mi Zou, Yu-Ming He, Yizhi Huang, Jun-Yi Zhao, Bin-Chen Li, Yong-Peng Guo, Xing Ding, Mo-Chi Xu, Run-Ze Liu, Geng-Yan Zou, Zhen Ning, Xiang You, Hui Wang, Wen-Xin Pan, Hao-Tao Zhu, Ming-Yang Zheng, Xiu-Ping Xie, Dandan Qin, Xiao Jiang, Yong-Heng Huo, Qiang Zhang, Chao-Yang Lu, Xiongfeng Ma, Teng-Yun Chen, Jian-Wei Pan

Abstract: To fully exploit the potential of quantum technologies, quantum networks are needed to link different systems, significantly enhancing applications in computing, cryptography, and metrology. Central to these networks are quantum relays that can facilitate long-distance entanglement distribution and quantum communication. In this work, we present a modular and scalable quantum relay architecture us… ▽ More To fully exploit the potential of quantum technologies, quantum networks are needed to link different systems, significantly enhancing applications in computing, cryptography, and metrology. Central to these networks are quantum relays that can facilitate long-distance entanglement distribution and quantum communication. In this work, we present a modular and scalable quantum relay architecture using a high-quality single-photon source. The proposed network incorporates three untrusted intermediate nodes and is capable of a repetition rate of 304.52 MHz. We use a measurement-device-independent protocol to demonstrate secure key establishment over fibers covering up to 300 kilometers. This study highlights the potential of single-photon sources in quantum relays to enhance information transmission, expand network coverage, and improve deployment flexibility, with promising applications in future quantum networks. △ Less

Submitted 29 August, 2025; originally announced August 2025.

Comments: 29 pages,17 figures, 2 tables

Journal ref: Nat. Phys. 21, 1670-1677 (2025)

Edge Universality for Inhomogeneous Random Matrices

Authors: Dang-Zheng Liu, Guangyi Zou

Abstract: We consider symmetric and Hermitian random matrices whose entries are independent and symmetric random variables with an arbitrary variance pattern. Under a novel Short-to-Long Mixing condition, which is sharp in the sense that it precludes a corrected shift at the spectral edge, we establish GOE/GUE edge universality for such inhomogeneous random matrices. This condition effectively reduces the u… ▽ More We consider symmetric and Hermitian random matrices whose entries are independent and symmetric random variables with an arbitrary variance pattern. Under a novel Short-to-Long Mixing condition, which is sharp in the sense that it precludes a corrected shift at the spectral edge, we establish GOE/GUE edge universality for such inhomogeneous random matrices. This condition effectively reduces the universality problem to verifying the mixing properties of a random walk governed by the variance profile matrix. Our universality results are applicable to a remarkably broad class of random matrix ensembles that may be highly inhomogeneous, sparse, or far beyond the mean-field setting of classical random matrix theory. Notable examples include: 1. Inhomogeneous Wishart-type random matrices; 2. Random band matrices whose entries are independent random variables with general variance profile, particularly with an optimal bandwidth in dimensions $d \le 2$; 3. Sparse random matrices with structured variance profiles; 4. Generalized Wigner matrices under significantly weaker sparsity constraints and heavy-tailed entry distributions; 5. Wegner orbital models under sharp mixing assumptions; 6. Random 2-lifts of random $d$-regular graphs where $d\geq N^{2/3+ε}$ for any $ε>0$. △ Less

Submitted 10 November, 2025; v1 submitted 25 August, 2025; originally announced August 2025.

Comments: typos corrected

MSC Class: 60B20; 60F05

Physics-informed deep operator network for traffic state estimation

Authors: Zhihao Li, Ting Wang, Guojian Zou, Ruofei Wang, Ye Li

Abstract: Traffic state estimation (TSE) fundamentally involves solving high-dimensional spatiotemporal partial differential equations (PDEs) governing traffic flow dynamics from limited, noisy measurements. While Physics-Informed Neural Networks (PINNs) enforce PDE constraints point-wise, this paper adopts a physics-informed deep operator network (PI-DeepONet) framework that reformulates TSE as an operator… ▽ More Traffic state estimation (TSE) fundamentally involves solving high-dimensional spatiotemporal partial differential equations (PDEs) governing traffic flow dynamics from limited, noisy measurements. While Physics-Informed Neural Networks (PINNs) enforce PDE constraints point-wise, this paper adopts a physics-informed deep operator network (PI-DeepONet) framework that reformulates TSE as an operator learning problem. Our approach trains a parameterized neural operator that maps sparse input data to the full spatiotemporal traffic state field, governed by the traffic flow conservation law. Crucially, unlike PINNs that enforce PDE constraints point-wise, PI-DeepONet integrates traffic flow conservation model and the fundamental diagram directly into the operator learning process, ensuring physical consistency while capturing congestion propagation, spatial correlations, and temporal evolution. Experiments on the NGSIM dataset demonstrate superior performance over state-of-the-art baselines. Further analysis reveals insights into optimal function generation strategies and branch network complexity. Additionally, the impact of input function generation methods and the number of functions on model performance is explored, highlighting the robustness and efficacy of proposed framework. △ Less

Submitted 17 August, 2025; originally announced August 2025.

Comments: under review in Transportmetrica B: Transport Dynamics

MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment

Authors: Gui Zou, Chaofan Gan, Chern Hong Lim, Supavadee Aramvith, Weiyao Lin

Abstract: With the increasing availability of 2D and 3D data, significant advancements have been made in the field of cross-modal retrieval. Nevertheless, the existence of imperfect annotations presents considerable challenges, demanding robust solutions for 2D-3D cross-modal retrieval in the presence of noisy label conditions. Existing methods generally address the issue of noise by dividing samples indepe… ▽ More With the increasing availability of 2D and 3D data, significant advancements have been made in the field of cross-modal retrieval. Nevertheless, the existence of imperfect annotations presents considerable challenges, demanding robust solutions for 2D-3D cross-modal retrieval in the presence of noisy label conditions. Existing methods generally address the issue of noise by dividing samples independently within each modality, making them susceptible to overfitting on corrupted labels. To address these issues, we propose a robust 2D-3D \textbf{M}ulti-level cross-modal adaptive \textbf{C}orrection and \textbf{A}lignment framework (MCA). Specifically, we introduce a Multimodal Joint label Correction (MJC) mechanism that leverages multimodal historical self-predictions to jointly model the modality prediction consistency, enabling reliable label refinement. Additionally, we propose a Multi-level Adaptive Alignment (MAA) strategy to effectively enhance cross-modal feature semantics and discrimination across different levels. Extensive experiments demonstrate the superiority of our method, MCA, which achieves state-of-the-art performance on both conventional and realistic noisy 3D benchmarks, highlighting its generality and effectiveness. △ Less

Submitted 8 August, 2025; originally announced August 2025.

Comments: ICMEW 2025

D2PPO: Diffusion Policy Policy Optimization with Dispersive Loss

Authors: Guowei Zou, Weibing Li, Hejun Wu, Yukun Qian, Yuhang Wang, Haitao Wang

Abstract: Diffusion policies excel at robotic manipulation by naturally modeling multimodal action distributions in high-dimensional spaces. Nevertheless, diffusion policies suffer from diffusion representation collapse: semantically similar observations are mapped to indistinguishable features, ultimately impairing their ability to handle subtle but critical variations required for complex robotic manipula… ▽ More Diffusion policies excel at robotic manipulation by naturally modeling multimodal action distributions in high-dimensional spaces. Nevertheless, diffusion policies suffer from diffusion representation collapse: semantically similar observations are mapped to indistinguishable features, ultimately impairing their ability to handle subtle but critical variations required for complex robotic manipulation. To address this problem, we propose D2PPO (Diffusion Policy Policy Optimization with Dispersive Loss). D2PPO introduces dispersive loss regularization that combats representation collapse by treating all hidden representations within each batch as negative pairs. D2PPO compels the network to learn discriminative representations of similar observations, thereby enabling the policy to identify subtle yet crucial differences necessary for precise manipulation. In evaluation, we find that early-layer regularization benefits simple tasks, while late-layer regularization sharply enhances performance on complex manipulation tasks. On RoboMimic benchmarks, D2PPO achieves an average improvement of 22.7% in pre-training and 26.1% after fine-tuning, setting new SOTA results. In comparison with SOTA, results of real-world experiments on a Franka Emika Panda robot show the excitingly high success rate of our method. The superiority of our method is especially evident in complex tasks. Project page: https://guowei-zou.github.io/d2ppo/ △ Less

Submitted 4 August, 2025; originally announced August 2025.

Journal ref: Proceedings of the AAAI Conference on Artificial Intelligence, 40(22): 18891-18899, 2026

Generation of 95-qubit genuine entanglement and verification of symmetry-protected topological phases

Authors: Tao Jiang, Jianbin Cai, Junxiang Huang, Naibin Zhou, Yukun Zhang, Jiahao Bei, Guoqing Cai, Sirui Cao, Fusheng Chen, Jiang Chen, Kefu Chen, Xiawei Chen, Xiqing Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Zhibin Deng, Pei Ding, Xun Ding, Zhuzhengqi Ding, Shuai Dong, Bo Fan, Daojin Fan , et al. (130 additional authors not shown)

Abstract: Symmetry-protected topological (SPT) phases are fundamental features of cluster states, serving as key resources for measurement-based quantum computation (MBQC). Generating large-scale cluster states and verifying their SPT phases are essential steps toward practical MBQC, which however still presents significant experimental challenges. In this work, we address these challenges by utilizing adva… ▽ More Symmetry-protected topological (SPT) phases are fundamental features of cluster states, serving as key resources for measurement-based quantum computation (MBQC). Generating large-scale cluster states and verifying their SPT phases are essential steps toward practical MBQC, which however still presents significant experimental challenges. In this work, we address these challenges by utilizing advanced superconducting hardware with optimized gate operations, enhanced readout fidelity, and error mitigation techniques. We successfully generate and verify 95-qubit one-dimensional and 72-qubit two-dimensional genuine entangled cluster states, achieving fidelities of $0.5603 \pm 0.0084$ and $0.5519 \pm 0.0054$, respectively. Leveraging these high-fidelity cluster states, we investigate SPT phases through quantum teleportation across all 95 qubits and demonstrate input-state-dependent robustness against symmetry-breaking perturbations, highlighting the practicality and intrinsic robustness of MBQC enabled by the SPT order. Our results represent a significant advancement in large-scale entanglement generation and topological phase simulation, laying the foundation for scalable and practical MBQC using superconducting quantum systems. △ Less

Submitted 3 May, 2025; originally announced May 2025.

Comments: Main text: 15 pages, 4 figures; supplementary materials: 42 pages, 19 figures. Total: 57 pages, 23 figures

Journal ref: Nat. Phys. 22 (2026) 430-438

MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin

Authors: Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou, Minqi Chen, Rong Tian, Hiroto Saigo

Abstract: Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is introduced as a multimodal framework inspired by real-world diagnostic processes. It uses pretrained models such as DINOv2, Vision Transformer, and ResNet-18 to convert high-dimensional 18F-FDG PET/CT imaging into low-dimensional, semantically meaningful features. A learnable self-attention-based fusion network then integrates… ▽ More Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is introduced as a multimodal framework inspired by real-world diagnostic processes. It uses pretrained models such as DINOv2, Vision Transformer, and ResNet-18 to convert high-dimensional 18F-FDG PET/CT imaging into low-dimensional, semantically meaningful features. A learnable self-attention-based fusion network then integrates these imaging features with clinical data for classification. Using 416 FUO patient cases from Sichuan University West China Hospital from 2017 to 2023, the multimodal fusion classification network MFCN achieved macro-AUROC scores ranging from 0.8654 to 0.9291 across seven tasks, outperforming conventional machine learning and single-modality deep learning methods. Ablation studies and five-fold cross-validation further validated its effectiveness. By combining the strengths of pretrained large models and deep learning, MedMimic offers a promising solution for disease classification. △ Less

Submitted 13 February, 2025; v1 submitted 7 February, 2025; originally announced February 2025.

Boosted fusion gates above the percolation threshold for scalable graph-state generation

Authors: Yong-Peng Guo, Geng-Yan Zou, Xing Ding, Qi-Hang Zhang, Mo-Chi Xu, Run-Ze Liu, Jun-Yi Zhao, Zhen-Xuan Ge, Li-Chao Peng, Ke-Mi Xu, Yi-Yang Lou, Zhen Ning, Lin-Jun Wang, Hui Wang, Yong-Heng Huo, Yu-Ming He, Chao-Yang Lu, Jian-Wei Pan

Abstract: Fusing small resource states into a larger, fully connected graph-state is essential for scalable photonic quantum computing. Theoretical analysis reveals that this can only be achieved when the success probability of the fusion gate surpasses a specific percolation threshold of 58.98% by using three-photon GHZ states as resource states. However, such an implementation of a fusion gate has never b… ▽ More Fusing small resource states into a larger, fully connected graph-state is essential for scalable photonic quantum computing. Theoretical analysis reveals that this can only be achieved when the success probability of the fusion gate surpasses a specific percolation threshold of 58.98% by using three-photon GHZ states as resource states. However, such an implementation of a fusion gate has never been experimentally realized before. Here, we successfully demonstrate a boosted fusion gate with a theoretical success probability of 75%, using deterministically generated auxiliary states. The success probability is experimentally measured to be 71.0(7)%. We further demonstrate the effectiveness of the boosted fusion gate by fusing two Bell states with a fidelity of 67(2)%. Our work paves a crucial path toward scalable linear optical quantum computing. △ Less

Submitted 25 December, 2024; originally announced December 2024.

Comments: 5 pages, 4 figures

RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification

Authors: Guangwenjie Zou, Liang Yao, Fan Liu, Chuanyi Zhang, Xin Li, Ning Chen, Shengxiang Xu, Jun Zhou

Abstract: Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose… ▽ More Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose an effective structural pruning approach for remote sensing image classification. Specifically, a pruning strategy that amplifies the differences in channel importance of the model is introduced. Then an adaptive mining loss function is designed for the fine-tuning process of the pruned model. Finally, we conducted experiments on two remote sensing classification datasets. The experimental results demonstrate that our method achieves minimal accuracy loss after compressing remote sensing classification models, achieving state-of-the-art (SoTA) performance. △ Less

Submitted 18 December, 2024; v1 submitted 17 December, 2024; originally announced December 2024.

Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

Authors: Dongxin Gao, Daojin Fan, Chen Zha, Jiahao Bei, Guoqing Cai, Jianbin Cai, Sirui Cao, Xiangdong Zeng, Fusheng Chen, Jiang Chen, Kefu Chen, Xiawei Chen, Xiqing Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Zhibin Deng, Pei Ding, Xun Ding, Zhuzhengqi Ding, Shuai Dong, Yupeng Dong, Bo Fan , et al. (129 additional authors not shown)

Abstract: In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle r… ▽ More In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges. △ Less

Submitted 16 December, 2024; originally announced December 2024.

Bootstrap Model Averaging

Authors: Minghui Song, Guohua Zou, Alan T. K. Wan

Abstract: Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its favorable properties, presents a new solution. In this paper, we propose a bootstrap model averaging approach that selects the weights by minimizing… ▽ More Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its favorable properties, presents a new solution. In this paper, we propose a bootstrap model averaging approach that selects the weights by minimizing a bootstrap criterion. Our weight selection criterion can also be interpreted as a bootstrap aggregating. We demonstrate that the resultant estimator is asymptotically optimal in the sense that it achieves the lowest possible squared error loss. Furthermore, we establish the convergence rate of bootstrap weights tending to the theoretically optimal weights. Additionally, we derive the limiting distribution for our proposed model averaging estimator. Through simulation studies and empirical applications, we show that our proposed method often has better performance than other commonly used model selection and model averaging methods, and bootstrap variants. △ Less

Submitted 7 December, 2024; originally announced December 2024.

Automatic Differentiation-based Full Waveform Inversion with Flexible Workflows

Authors: Feng Liu, Haipeng Li, Guangyuan Zou, Junlun Li

Abstract: Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse… ▽ More Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open-source AD-based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD-based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state-of-the-art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large-scale inversion associated with AD, the proposed framework adopts strategies such as mini-batch and checkpointing. Through comprehensive evaluations, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and the development of new inversion strategies. △ Less

Submitted 30 November, 2024; originally announced December 2024.

Comments: Manuscript including 14 pages supplement. Code link: https://github.com/liufeng2317/ADFWI

Journal ref: JGR: Machine Learning and Computation, 2, e2024JH000542

CHESTNUT: A QoS Dataset for Mobile Edge Environments

Authors: Guobing Zou, Fei Zhao, Shengxiang Hu

Abstract: Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwidth, latency, jitter, and data packet loss rate. However, most existing QoS datasets, such as the com… ▽ More Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwidth, latency, jitter, and data packet loss rate. However, most existing QoS datasets, such as the common WS-Dream dataset, focus mainly on static QoS metrics of network services and ignore dynamic attributes such as time and geographic location. This means they should have detailed the mobile device's location at the time of the service request or the chronological order in which the request was made. However, these dynamic attributes are crucial for understanding and predicting the actual performance of network services, as QoS performance typically fluctuates with time and geographic location. To this end, we propose a novel dataset that accurately records temporal and geographic location information on quality of service during the collection process, aiming to provide more accurate and reliable data to support future QoS prediction in mobile edge environments. △ Less

Submitted 26 May, 2026; v1 submitted 24 October, 2024; originally announced October 2024.

Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach

Authors: Ting Wang, Ye Li, Rongjun Cheng, Guojian Zou, Takao Dantsujic, Dong Ngoduy

Abstract: Physics-informed deep learning (PIDL)-based models have recently garnered remarkable success in traffic state estimation (TSE). However, the prior knowledge used to guide regularization training in current mainstream architectures is based on deterministic physical models. The drawback is that a solely deterministic model fails to capture the universally observed traffic flow dynamic scattering ef… ▽ More Physics-informed deep learning (PIDL)-based models have recently garnered remarkable success in traffic state estimation (TSE). However, the prior knowledge used to guide regularization training in current mainstream architectures is based on deterministic physical models. The drawback is that a solely deterministic model fails to capture the universally observed traffic flow dynamic scattering effect, thereby yielding unreliable outcomes for traffic control. This study, for the first time, proposes stochastic physics-informed deep learning (SPIDL) for traffic state estimation. The idea behind such SPIDL is simple and is based on the fact that a stochastic fundamental diagram provides the entire range of possible speeds for any given density with associated probabilities. Specifically, we select percentile-based fundamental diagram and distribution-based fundamental diagram as stochastic physics knowledge, and design corresponding physics-uninformed neural networks for effective fusion, thereby realizing two specific SPIDL models, namely \text{$α$}-SPIDL and \text{$\cal B$}-SPIDL. The main contribution of SPIDL lies in addressing the "overly centralized guidance" caused by the one-to-one speed-density relationship in deterministic models during neural network training, enabling the network to digest more reliable knowledge-based constraints.Experiments on the real-world dataset indicate that proposed SPIDL models achieve accurate traffic state estimation in sparse data scenarios. More importantly, as expected, SPIDL models reproduce well the scattering effect of field observations, demonstrating the effectiveness of fusing stochastic physics model knowledge with deep learning frameworks. △ Less

Submitted 1 September, 2024; originally announced September 2024.

Comments: under review in Information Fusion

Revisit the Partial Coloring Method: Prefix Spencer and Sampling

Authors: Dongrun Cai, Xue Chen, Wenxuan Shu, Haoyu Wang, Guangyi Zou

Abstract: As the most powerful tool in discrepancy theory, the partial coloring method has wide applications in many problems including the Beck-Fiala problem and Spencer's celebrated result. Currently, there are two major algorithmic methods for the partial coloring method: the first approach uses linear algebraic tools; and the second is called Gaussian measure algorithm. We explore the advantages of thes… ▽ More As the most powerful tool in discrepancy theory, the partial coloring method has wide applications in many problems including the Beck-Fiala problem and Spencer's celebrated result. Currently, there are two major algorithmic methods for the partial coloring method: the first approach uses linear algebraic tools; and the second is called Gaussian measure algorithm. We explore the advantages of these two methods and show the following results for them separately. 1. Spencer conjectured that the prefix discrepancy of any $\mathbf{A} \in \{0,1\}^{m \times n}$ is $O(\sqrt{m})$. We show how to find a partial coloring with prefix discrepancy $O(\sqrt{m})$ and $Ω(n)$ entries in $\{ \pm 1\}$ efficiently. To the best of our knowledge, this provides the first partial coloring whose prefix discrepancy is almost optimal. However, unlike the classical discrepancy problem, there is no reduction on the number of variables $n$ for the prefix problem. By recursively applying partial coloring, we obtain a full coloring with prefix discrepancy $O(\sqrt{m} \cdot \log \frac{O(n)}{m})$. Prior to this work, the best bounds of the prefix Spencer conjecture for arbitrarily large $n$ were $2m$ and $O(\sqrt{m \log n})$. 2. Our second result extends the first linear algebraic approach to a sampling algorithm in Spencer's classical setting. On the first hand, Spencer proved that there are $1.99^m$ good colorings with discrepancy $O(\sqrt{m})$. Hence a natural question is to design efficient random sampling algorithms in Spencer's setting. On the other hand, some applications of discrepancy theory, prefer a random solution instead of a fixed one. Our second result is an efficient sampling algorithm whose random output has min-entropy $Ω(n)$ and discrepancy $O(\sqrt{m})$. Moreover, our technique extends the linear algebraic framework by incorporating leverage scores of randomized matrix algorithms. △ Less

Submitted 25 August, 2024; originally announced August 2024.

GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction

Authors: Shengxiang Hu, Guobing Zou, Bofeng Zhang, Shaogang Wu, Shiyi Lin, Yanglan Gan, Yixin Chen

Abstract: Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slic… ▽ More Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%. △ Less

Submitted 12 September, 2024; v1 submitted 20 August, 2024; originally announced August 2024.

Comments: This work has been submitted to the IEEE for possible publication

MSC Class: 68T99 ACM Class: H.4.0; I.2.0

Outliers for deformed inhomogeneous random matrices

Authors: Ruohan Geng, Dang-Zheng Liu, Guangyi Zou

Abstract: Inhomogeneous random matrices with non-trivial variance profiles determined by symmetric stochastic matrices and with independent sub-Gaussian entries up to Hermitian symmetry, encompass a wide range of important models, including sparse Wigner matrices and random band matrices. In these models, the maximum entry variance-a natural proxy for sparsity-serves both as a key structural feature and a p… ▽ More Inhomogeneous random matrices with non-trivial variance profiles determined by symmetric stochastic matrices and with independent sub-Gaussian entries up to Hermitian symmetry, encompass a wide range of important models, including sparse Wigner matrices and random band matrices. In these models, the maximum entry variance-a natural proxy for sparsity-serves both as a key structural feature and a primary analytical obstacle. In this paper, we consider low-rank additive perturbations of such matrices and establish a sharp BBP phase transition for extreme eigenvalues at the level of the law of large numbers. Furthermore, in the Gaussian setting, we derive the fluctuations of spectral outliers under suitable conditions on the variance profile and perturbation. These fluctuations exhibit strong non-universality, depending on the eigenvectors, sparsity levels, and the underlying geometric structure. Our proof strategies rely on ribbon graph expansions, upper bounds for diagram functions, large-moment estimates, and the enumeration of typical diagrams. △ Less

Submitted 20 February, 2026; v1 submitted 16 July, 2024; originally announced July 2024.

Comments: 67 pages, 9 figures. Add figures and Section 5.3, correct typos, and rewrite some parts for readability

MSC Class: 60B20

Experimental single-photon quantum key distribution surpassing the fundamental coherent-state rate limit

Authors: Yang Zhang, Xing Ding, Yang Li, Likang Zhang, Yong-Peng Guo, Gao-Qiang Wang, Zhen Ning, Mo-Chi Xu, Run-Ze Liu, Jun-Yi Zhao, Geng-Yan Zou, Hui Wang, Yuan Cao, Yu-Ming He, Cheng-Zhi Peng, Yong-Heng Huo, Sheng-Kai Liao, Chao-Yang Lu, Feihu Xu, Jian-Wei Pan

Abstract: Single-photon sources are essential for quantum networks, enabling applications ranging from quantum key distribution (QKD) to the burgeoning quantum internet. Despite the remarkable advancements, the current reliance of QKD on attenuated coherent (laser) light sources has imposed a fundamental limit on the secret key rate (SKR). This constraint is primarily attributable to the scarcity of single-… ▽ More Single-photon sources are essential for quantum networks, enabling applications ranging from quantum key distribution (QKD) to the burgeoning quantum internet. Despite the remarkable advancements, the current reliance of QKD on attenuated coherent (laser) light sources has imposed a fundamental limit on the secret key rate (SKR). This constraint is primarily attributable to the scarcity of single-photon components within coherent light, confined by an inherent upper bound of 1/e. Here, we report high-rate QKD using a high-efficiency single-photon source, enabling an SKR transcending the fundamental rate limit of coherent light. We developed an on-demand, bright semiconductor quantum-dot single-photon source with an efficiency of 0.71(2), exceeding the inherent bound of coherent light by approximately 2.87 dB. Implementing narrow-bandwidth filtering and random polarization modulation, we conducted a field QKD trial over a 14.6(1.1)-dB-loss free-space urban channel, achieving an SKR of 0.00108 bits per pulse. This surpasses the practical limit of coherent-light-based QKD by 2.53 dB. Our findings conclusively demonstrate the superior performance of nanotechnology-based single-photon sources over coherent light for QKD applications, marking a pivotal stride towards the realization of a global quantum internet. △ Less

Submitted 4 June, 2024; originally announced June 2024.

Comments: 22 pages, 5 figures, 1 Table

Sample size planning for estimating the global win probability with assurance and precision

Authors: Di Shu, Guangyong Zou

Abstract: Most clinical trials conducted in drug development contain multiple endpoints in order to collectively assess the intended effects of the drug on various disease characteristics. Focusing on the estimation of the global win probability, defined as the average win probability (WinP) across endpoints that a treated participant would have a better outcome than a control participant, we propose a clos… ▽ More Most clinical trials conducted in drug development contain multiple endpoints in order to collectively assess the intended effects of the drug on various disease characteristics. Focusing on the estimation of the global win probability, defined as the average win probability (WinP) across endpoints that a treated participant would have a better outcome than a control participant, we propose a closed-form sample size formula incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. We make use of the equivalence of the WinP and the area under the receiver operating characteristic curve (AUC) and adapt a formula originally developed for the difference between two AUCs to handle the global WinP. Unequal variance is allowed. Simulation results suggest that the method performs very well. We illustrate the proposed formula using a Parkinson's disease clinical trial design example. △ Less

Submitted 5 April, 2024; originally announced April 2024.

Comments: 8 pages, 2 tables

MSC Class: 62 ACM Class: G.3

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation

Authors: Yining Wu, Shengyu Duan, Gaole Sai, Chenhong Cao, Guobing Zou

Abstract: Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerat… ▽ More Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either putting in additional computational resources or utilizing parallel systems, introducing a large cost. In this paper, we propose algorithmic methods to accelerate MF, without inducing any additional computational resources. In specific, we observe fine-grained structured sparsity in the decomposed feature matrices when considering a certain threshold. The fine-grained structured sparsity causes a large amount of unnecessary operations during both matrix multiplication and latent factor update, increasing the computational time of the MF training process. Based on the observation, we firstly propose to rearrange the feature matrices based on joint sparsity, which potentially makes a latent vector with a smaller index more dense than that with a larger index. The feature matrix rearrangement is given to limit the error caused by the later performed pruning process. We then propose to prune the insignificant latent factors by an early stopping process during both matrix multiplication and latent factor update. The pruning process is dynamically performed according to the sparsity of the latent factors for different users/items, to accelerate the process. The experiments show that our method can achieve 1.2-1.65 speedups, with up to 20.08% error increase, compared with the conventional MF training process. We also prove the proposed methods are applicable considering different hyperparameters including optimizer, optimization strategy and initialization method. △ Less

Submitted 25 March, 2026; v1 submitted 18 March, 2024; originally announced April 2024.

Large Language Model Meets Graph Neural Network in Knowledge Distillation

Authors: Shengxiang Hu, Guobing Zou, Song Yang, Yanglan Gan, Bofeng Zhang, Yixin Chen

Abstract: In service-oriented architectures, accurately predicting the Quality of Service (QoS) is crucial for maintaining reliability and enhancing user satisfaction. However, significant challenges remain due to existing methods always overlooking high-order latent collaborative relationships between users and services and failing to dynamically adjust feature learning for every specific user-service invo… ▽ More In service-oriented architectures, accurately predicting the Quality of Service (QoS) is crucial for maintaining reliability and enhancing user satisfaction. However, significant challenges remain due to existing methods always overlooking high-order latent collaborative relationships between users and services and failing to dynamically adjust feature learning for every specific user-service invocation, which are critical for learning accurate features. Additionally, reliance on RNNs for capturing QoS evolution hampers models' ability to detect long-term trends due to difficulties in managing long-range dependencies. To address these challenges, we propose the \underline{T}arget-Prompt \underline{O}nline \underline{G}raph \underline{C}ollaborative \underline{L}earning (TOGCL) framework for temporal-aware QoS prediction. TOGCL leverages a dynamic user-service invocation graph to model historical interactions, providing a comprehensive representation of user-service relationships. Building on this graph, it develops a target-prompt graph attention network to extract online deep latent features of users and services at each time slice, simultaneously considering implicit collaborative relationships between target users/services and their neighbors, as well as relevant historical QoS values. Additionally, a multi-layer Transformer encoder is employed to uncover temporal feature evolution patterns of users and services, leading to temporal-aware QoS prediction. Extensive experiments conducted on the WS-DREAM dataset demonstrate that our proposed TOGCL framework significantly outperforms state-of-the-art methods across multiple metrics, achieving improvements of up to 38.80\%. These results underscore the effectiveness of the TOGCL framework for precise temporal QoS prediction. △ Less

Submitted 11 June, 2024; v1 submitted 8 February, 2024; originally announced February 2024.

Comments: This work has been submitted to the IEEE for possible publication

MSC Class: 68T30; 68R10; 68T05

Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints

Authors: E. Davies Smith, V. Jairath, G. Zou

Abstract: Cluster randomization trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global hypothesis testing/effect estimation methods represent a common analysis strategy. However, specification of the joint distribution required by these methods is non-trivial, particularly when endpoint properties differ. We develop rank-based in… ▽ More Cluster randomization trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global hypothesis testing/effect estimation methods represent a common analysis strategy. However, specification of the joint distribution required by these methods is non-trivial, particularly when endpoint properties differ. We develop rank-based interval estimators for a global treatment effect referred to as the "global win probability," or the probability that a treatment individual responds better than a control individual on average. Using endpoint-specific ranks among the combined sample and within each arm, each individual-level observation is converted to a "win fraction" which quantifies the proportion of wins experienced over every observation in the comparison arm. An individual's multiple observations are then replaced by a single "global win fraction," constructed by averaging win fractions across endpoints. A linear mixed model is applied directly to the global win fractions to recover point, variance, and interval estimates of the global win probability adjusted for clustering. Simulation demonstrates our approach performs well concerning coverage and type I error, and methods are easily implemented using standard software. A case study using publicly available data is provided with corresponding R and SAS code. △ Less

Submitted 8 February, 2024; v1 submitted 22 January, 2024; originally announced January 2024.

Comments: 32 pages, 5 tables