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Kai Xu

Publications and source records attributed to Kai Xu.

At least 73 records · Page 4Linked to original sources
arXiv2026

Systematic study of exotic $1^{-+}$ tetraquark spectroscopy

The masses of exotic quantum-number $1^{-+}$ compact tetraquark states are calculated in a constituent quark model, where a Cornell-like potential is employed as the central potential, spin-spin and spin-orbit coupling derived from the Breit-Fermi interaction are treated as hyperfine corrections, and model parameters are taken from previous works. The ground state $1^{-+}$ P-wave tetraquarks are predicted at 1.9, 4.2, and 6.6~GeV for the light, charmonium-like, and fully-charm sectors, respectively. The decay width ratios of $1^{-+}$ tetraquark states are calculated for two-body strong decay channels within the rearrangement mechanism, including $ωh_1$ and $ηf_1$ for isospin $I=0$ light tetraquarks, $ρh_1$ and $πf_1$ for isospin $I=1$ light tetraquarks, $π/η+χ_{c1}$ and $ρ/ω+ h_c$ for charmonium-like tetraquarks, and $η_c χ_{c1}$ and $J/ψh_c$ for fully-charm tetraquarks. The theoretical results are compared with the observed exotic $1^{-+}$ states, and promising search channels for $1^{-+}$ tetraquarks are discussed. The work suggests that $η_1(1855)$ is unlikely to be a compact tetraquark state.

hep-ph↗
arXiv2026

Strain-Mediated Lattice Reconstruction Enhances Ferromagnetism in Cr2Ge2Te6/WTe2 van der Waals Heterobilayers

Van der Waals (vdW) heterostructures enable tailored electronic and magnetic phases by stacking atomically thin layers with pristine interfaces. Here, we investigate fully 2D Cr2Ge2Te6/WTe2 heterostructures and identify a strong enhancement of ferromagnetism in Cr2Ge2Te6 (CGT). Magnetotransport measurements across multiple devices with WTe2 thicknesses ranging from monolayer to bulk reveal a robust anomalous Hall effect together with a more than twofold increase of the Curie temperature and substantially enhanced coercive fields. Interface microscopy confirms chemically abrupt vdW interfaces with no detectable interdiffusion, while control experiments rule out processing- or stray-field-induced artifacts. Our experiments and theoretical calculations demonstrate that interfacial charge transfer renders CGT conductive and that proximity-induced lattice distortions in CGT enhance exchange and magnetocrystalline anisotropy. These results establish strain-mediated lattice reconstruction as a strategy for engineering high-temperature magnetic order in 2D heterostructures and clarify that modifications within the magnetic layer itself can govern proximity effects in vdW stacks.

cond-mat.mtrl-sci↗
arXiv2026

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning

While Large Language Models (LLMs) have demonstrated impressive capabilities, their output quality remains inconsistent across various application scenarios, making it difficult to identify trustworthy responses, especially in complex tasks requiring multi-step reasoning. In this paper, we propose a Token-level Uncertainty estimation framework for Reasoning (TokUR) that enables LLMs to self-assess and self-improve their responses in mathematical reasoning. Specifically, we introduce low-rank random weight perturbation during LLM decoding to generate predictive distributions for token-level uncertainty estimation, and we aggregate these uncertainty quantities to capture the semantic uncertainty of generated responses. Experiments on mathematical reasoning datasets of varying difficulty demonstrate that TokUR exhibits a strong correlation with answer correctness and model robustness, and the uncertainty signals produced by TokUR can be leveraged to enhance the model's reasoning performance at test time. These results highlight the effectiveness of TokUR as a principled and scalable approach for improving the reliability and interpretability of LLMs in challenging reasoning tasks.

cs.LG↗
arXiv2026

RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation

Physical feasibility in 3D bin packing is a key requirement in modern industrial logistics and robotic automation. With the growing adoption of industrial automation, online bin packing has gained increasing attention. However, inconsistencies in problem settings, test datasets, and evaluation metrics have hindered progress in the field, and there is a lack of a comprehensive benchmarking system. Direct testing on real hardware is costly, and building a realistic simulation environment is also challenging. To address these limitations, we introduce RoboBPP, a benchmarking system designed for robotic online bin packing. RoboBPP integrates a physics-based simulator to assess physical feasibility. In our simulation environment, we introduce a robotic arm and boxes at real-world scales to replicate real industrial packing workflows. By simulating conditions that arise in real industrial applications, we ensure that evaluated algorithms are practically deployable. In addition, prior studies often rely on synthetic datasets whose distributions differ from real-world industrial data. To address this issue, we collect three datasets from real industrial workflows, including assembly-line production, logistics packing, and furniture manufacturing. The benchmark comprises three carefully designed test settings and extends existing evaluation metrics with new metrics for structural stability and operational safety. We design a scoring system and derive a range of insights from the evaluation results. RoboBPP is fully open-source and is equipped with visualization tools and an online leaderboard, providing a reproducible and extensible foundation for future research and industrial applications (https://robot-bin-packing-benchmark.github.io).

cs.RO↗
arXiv2026

Exploring Hilbert-Space Fragmentation on a Superconducting Processor

Isolated interacting quantum systems generally thermalize, yet there are several examples for the breakdown of ergodicity, such as many-body localization and quantum scars. Recently, ergodicity breaking has been observed in systems subjected to linear potentials, termed Stark many-body localization. This phenomenon is closely associated with Hilbert-space fragmentation, characterized by a strong dependence of dynamics on initial conditions. Here, we explore initial-state dependent dynamics using a ladder-type superconducting processor with up to 24 qubits, which enables precise control of the qubit frequency and initial state preparation. In systems with linear potentials, we experimentally observe distinct non-equilibrium dynamics for initial states with the same quantum numbers and energy, but with varying domain wall numbers. Accompanied by the numerical simulation for systems with larger sizes, we reveal that this distinction becomes increasingly pronounced as the system size grows, in contrast with weakly disordered interacting systems. Our results provide convincing experimental evidence of the fragmentation in Stark systems, enriching our understanding of the weak breakdown of ergodicity.

quant-ph↗
arXiv2026

GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback

Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reliably as design complexity increases. Existing fine-tuning approaches rely on either limited supervised datasets or expensive post-training pipelines, resulting in brittle systems that restrict progress in generative CAD design. We argue that the primary bottleneck lies not in model or algorithmic capacity, but in the scarcity of diverse training examples that align visual geometry with program syntax. This limitation is especially acute because the collection of diverse and verified engineering datasets is both expensive and difficult to scale, constraining the development of robust generative CAD models. We introduce Geometric Inference Feedback Tuning (GIFT), a data augmentation framework that leverages geometric feedback to turn test-time compute into a bootstrapped set of high-quality training samples. GIFT combines two mechanisms: Soft-Rejection Sampling (GIFT-REJECT), which retains diverse high-fidelity programs beyond exact ground-truth matches, and Failure-Driven Augmentation (GIFT-FAIL), which converts near-miss predictions into synthetic training examples that improve robustness on challenging geometries. By amortizing inference-time search into the model parameters, GIFT captures the benefits of test-time scaling while reducing inference compute by 80%. It improves mean IoU by 12% over a strong supervised baseline and remains competitive with more complex multimodal systems, without requiring additional human annotation or specialized architectures.

cs.LG↗
arXiv2026

Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling

Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex mathematical reasoning tasks, but it is vulnerable when guided by process reward models, which often assign overconfident scores early in the reasoning process. This causes PF to suffer from premature exploitation: it myopically commits to locally promising trajectories, prunes potentially correct hypotheses, and converges to suboptimal solutions. This failure mode, known as particle impoverishment, is especially severe under constrained computational budgets. To address this, we analyze the problem and identify two root causes: a lack of diversity in the particle set due to overconfident resampling and consequent inability to assess the potential of a reasoning path. We introduce Entropic Particle Filtering (ePF), an algorithm that integrates two new techniques to solve these issues. The first technique, Entropic Annealing (EA), directly mitigates particle impoverishment by monitoring search diversity via entropy; when diversity drops, it intervenes by dynamically annealing the resampling distribution to preserve exploration. The second, an enhancement called Look-ahead Modulation (LaM), adds a predictive guide to evaluate a state's potential based on its successors. On several challenging math benchmarks, ePF significantly outperforms strong baselines and achieves up to a 50% relative improvement in task reward. Together, these methods improve PF's resilience by balancing the exploration of diverse solution spaces with the exploitation of high-reward regions, ultimately leading to higher-quality solutions.

cs.LG↗
arXiv2026

Observing Quantum Correlation Dynamics in Tunable Superconducting Bose-Hubbard Simulators

The dynamics of quantum correlations are central to understanding many physical properties of quantum systems. Here we experimentally study the correlation dynamics via two-particle quantum walks in superconducting Bose-Hubbard qutrit arrays, with tunable on-site interaction $U$ realized by Floquet engineering. Quantum walks show the characteristic change from bosonic bunching to fermionic antibunching with increasing $U$. The two-site entanglement and quantum correlation dynamics, as measured by negativity and quantum discord, are investigated. We find that depending on the initial state, the propagation of entanglement can be strongly suppressed with increasing $U$, while that of quantum discord exhibits considerably larger amplitude; or both of them appear insensitive to $U$. Furthermore, the forms of entanglement are found to persist throughout particle walks for $U =$ 0 and it is generally not the case when $U$ increases. Our work highlights the role of interaction in shaping quantum dynamics and extends the realm of simulating correlated quantum systems with superconducting circuits.

quant-ph↗
arXiv2026

A Practical Framework for Flaky Failure Triage in Distributed Database Continuous Integration

Flaky failure triage is crucial for keeping distributed database continuous integration (CI) efficient and reliable. After a failure is observed, operators must quickly decide whether to auto-rerun the job as likely flaky or escalate it as likely persistent, often under CPU-only millisecond budgets. Existing approaches remain difficult to deploy in this setting because they may rely on post-failure artifacts, produce poorly calibrated scores under telemetry and workload shifts, or learn from labels generated by finite rerun policies. To address these challenges, we present SCOUT, a practical state-aware causal online uncertainty-calibrated triage framework for distributed database CI. SCOUT uses only strict-causal features, including pre-failure telemetry and strictly historical data, to make online decisions without lookahead. Specifically, SCOUT combines lightweight state-aware scoring with optional sparse metadata fusion, applies post-hoc calibration to support fixed-threshold decisions across temporal and cross-domain shifts, and introduces a posterior-soft correction to reduce label bias induced by finite rerun budgets. We evaluated SCOUT on a benchmark of 3,680 labeled failed runs, including 462 flaky positives, and 62 telemetry/context features. Further, we studied the feasibility of SCOUT on TiDB v7/v8 and a large GitHub Actions metadata-only trace. The experimental results demonstrated its effectiveness and usefulness. We deployed SCOUT in the production environment, achieving an end-to-end P95 latency of 1.17 ms on CPU.

cs.SE↗
arXiv2026

AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research

There are different goals for literature research, from understanding an unfamiliar topic to generate hypothesis for the next research project. The nature of literature research also varies according to user's familiarity level of the topic. For inexperienced researchers, identifying gaps in the existing literature and generating feasible hypothesis are crucial but challenging. While general ``deep research'' tools can be used, they are not designed for such use case, thus often not effective. In addition, the ``black box" nature and hallucination of Large Language Models (LLMs) often lead to distrust. In this paper, we introduce a human-agent collaborative visualization system AwesomeLit to address this need. It has several novel features: a transparent user-steerable agentic workflow; a dynamically generated query exploring tree, visualizing the exploration path and provenance; and a semantic similarity view, depicting the relationships between papers. It enables users to transition from general intentions to detailed research topics. Finally, a qualitative study involving several early researchers showed that AwesomeLit is effective in helping users explore unfamiliar topics, identify promising research directions, and improve confidence in research results.

cs.HC↗
arXiv2026

Boundary effects in biological planar networks: pentagonsdominate Pyropia marginal cells

The topological and geometrical features at the boundary zone of planar polygonal networks remain poorly understood. Based on observations and mathematical proofs, we propose that marginal cells in the thalli of Pyropia haitanensis, a two-dimensional (2D) biological polygonal network, have an average edge number of approximately five. We demonstrate that this number is maintained by specific division patterns. Furthermore, we observe that both marginal cells and inner cells follow the trends predicted by the Lewis law and Aboav-Weaire law, but each cell type requires its own set of correlation parameters to more accurately describe its topological and geometrical features. The boundary effects are also evident in the differences between marginal cells and inner cells in terms of the distributions of interior angles and edge lengths. Similar to inner cells, cell division tends to occur in marginal cells with large sizes and transects a pair of unconnected edges. In particular, this study finds that the division of marginal cells preferentially transects the marginal edge. These specific topological and geometrical features of marginal cells and division patterns may inform the development of modelling algorithms for boundary conditions in biological 2D cellular networks.

q-bio.OT↗
arXiv2026

Demonstration of High-Fidelity Gates in a Strongly Anharmonic with Long-Coherence C-Shunt Flux Qubit

We demonstrate high-fidelity single-qubit gates on a C-shunt flux qubit that simultaneously combines a large anharmonicity ($\mathcal{A}/2π=848~\mathrm{MHz}$) with long relaxation time ($T_1 = 23~μ\text{s}$). The large anharmonicity significantly suppresses leakage to higher energy levels, enabling fast and precise microwave control. Using DRAG pulses and randomized benchmarking, the qubit achieves gate fidelities exceeding 99.9\%, highlighting the capability of C-shunt flux qubits for robust and high-performance quantum operations. These results establish them as a promising platform for scalable quantum information processing.

quant-ph↗
arXiv2026

IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models

In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics.

cs.RO↗
arXiv2026

Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints

The Flexible Job Shop Scheduling Problem (FJSP) originates from real production lines, while some practical constraints are often ignored or idealized in current FJSP studies, among which the limited buffer problem has a particular impact on production efficiency. To this end, we study an extended problem that is closer to practical scenarios--the Flexible Job Shop Scheduling Problem with Limited Buffers and Material Kitting. In recent years, deep reinforcement learning (DRL) has demonstrated considerable potential in scheduling tasks. However, its capacity for state modeling remains limited when handling complex dependencies and long-term constraints. To address this, we leverage a heterogeneous graph network within the DRL framework to model the global state. By constructing efficient message passing among machines, operations, and buffers, the network focuses on avoiding decisions that may cause frequent pallet changes during long-sequence scheduling, thereby helping improve buffer utilization and overall decision quality. Experimental results on both synthetic and real production line datasets show that the proposed method outperforms traditional heuristics and advanced DRL methods in terms of makespan and pallet changes, and also achieves a good balance between solution quality and computational cost. Furthermore, a supplementary video is provided to showcase a simulation system that effectively visualizes the progression of the production line.

cs.AI↗
arXiv2026

Learning Positive-Incentive Point Sampling in Neural Implicit Fields for Object Pose Estimation

Learning neural implicit fields of 3D shapes is a rapidly emerging field that enables shape representation at arbitrary resolutions. Due to the flexibility, neural implicit fields have succeeded in many research areas, including shape reconstruction, novel view image synthesis, and more recently, object pose estimation. Neural implicit fields enable learning dense correspondences between the camera space and the object's canonical space-including unobserved regions in camera space-significantly boosting object pose estimation performance in challenging scenarios like highly occluded objects and novel shapes. Despite progress, predicting canonical coordinates for unobserved camera-space regions remains challenging due to the lack of direct observational signals. This necessitates heavy reliance on the model's generalization ability, resulting in high uncertainty. Consequently, densely sampling points across the entire camera space may yield inaccurate estimations that hinder the learning process and compromise performance. To alleviate this problem, we propose a method combining an SO(3)-equivariant convolutional implicit network and a positive-incentive point sampling (PIPS) strategy. The SO(3)-equivariant convolutional implicit network estimates point-level attributes with SO(3)-equivariance at arbitrary query locations, demonstrating superior performance compared to most existing baselines. The PIPS strategy dynamically determines sampling locations based on the input, thereby boosting the network's accuracy and training efficiency. Our method outperforms the state-of-the-art on three pose estimation datasets. Notably, it demonstrates significant improvements in challenging scenarios, such as objects captured with unseen pose, high occlusion, novel geometry, and severe noise.

cs.CV↗
arXiv2026

NotebookRAG: Retrieving Multiple Notebooks to Augment the Generation of EDA Notebooks for Crowd-Wisdom

High-quality exploratory data analysis (EDA) is essential in the data science pipeline, but remains highly dependent on analysts' expertise and effort. While recent LLM-based approaches partially reduce this burden, they struggle to generate effective analysis plans and appropriate insights and visualizations when user intent is abstract. Meanwhile, a vast collection of analysis notebooks produced across platforms and organizations contains rich analytical knowledge that can potentially guide automated EDA. Retrieval-augmented generation (RAG) provides a natural way to leverage such corpora, but general methods often treat notebooks as static documents and fail to fully exploit their potential knowledge for automating EDA. To address these limitations, we propose NotebookRAG, a method that takes user intent, datasets, and existing notebooks as input to retrieve, enhance, and reuse relevant notebook content for automated EDA generation. For retrieval, we transform code cells into context-enriched executable components, which improve retrieval quality and enable rerun with new data to generate updated visualizations and reliable insights. For generation, an agent leverages enhanced retrieval content to construct effective EDA plans, derive insights, and produce appropriate visualizations. Evidence from a user study with 24 participants confirms the superiority of our method in producing high-quality and intent-aligned EDA notebooks.

cs.HC↗
arXiv2026

CycleDiff: Cycle Diffusion Models for Unpaired Image-to-image Translation

We introduce a diffusion-based cross-domain image translator in the absence of paired training data. Unlike GAN-based methods, our approach integrates diffusion models to learn the image translation process, allowing for more coverable modeling of the data distribution and performance improvement of the cross-domain translation. However, incorporating the translation process within the diffusion process is still challenging since the two processes are not aligned exactly, i.e., the diffusion process is applied to the noisy signal while the translation process is conducted on the clean signal. As a result, recent diffusion-based studies employ separate training or shallow integration to learn the two processes, yet this may cause the local minimal of the translation optimization, constraining the effectiveness of diffusion models. To address the problem, we propose a novel joint learning framework that aligns the diffusion and the translation process, thereby improving the global optimality. Specifically, we propose to extract the image components with diffusion models to represent the clean signal and employ the translation process with the image components, enabling an end-to-end joint learning manner. On the other hand, we introduce a time-dependent translation network to learn the complex translation mapping, resulting in effective translation learning and significant performance improvement. Benefiting from the design of joint learning, our method enables global optimization of both processes, enhancing the optimality and achieving improved fidelity and structural consistency. We have conducted extensive experiments on RGB$\leftrightarrow$RGB and diverse cross-modality translation tasks including RGB$\leftrightarrow$Edge, RGB$\leftrightarrow$Semantics and RGB$\leftrightarrow$Depth, showcasing better generative performances than the state of the arts.

cs.CV↗
arXiv2026

Adaptive Spatial Goodness Encoding: Advancing and Scaling Forward-Forward Learning Without Backpropagation

The Forward-Forward (FF) algorithm offers a promising alternative to backpropagation (BP). Despite advancements in recent FF-based extensions, which have enhanced the original algorithm and adapted it to convolutional neural networks (CNNs), they often suffer from limited representational capacity and poor scalability to large-scale datasets, primarily due to exploding channel dimensionality. In this work, we propose adaptive spatial goodness encoding (ASGE), a new FF-based training framework tailored for CNNs. ASGE leverages feature maps to compute spatially-aware goodness representations at each layer, enabling layer-wise supervision. Crucially, this approach decouples classification complexity from channel dimensionality, thereby addressing the issue of channel explosion and achieving competitive performance compared to other BP alternatives. ASGE outperforms all other FF-based approaches across multiple benchmarks, delivering test accuracies of 99.65% on MNIST, 93.41% on FashionMNIST, 90.62% on CIFAR-10, and 65.42% on CIFAR-100. Moreover, we present the first successful application of FF-based training to ImageNet, with Top-1 and Top-5 accuracies of 51.58% and 75.23%. Furthermore, we propose three prediction strategies to achieve flexible trade-offs among accuracy, parameters and memory usage, enabling deployment under diverse resource constraints.

cs.LG↗