arXiv subjects

Bo Lu

Publications and source records attributed to Bo Lu.

At least 19 recordsLinked to original sources

arXiv2026

DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes

Modern software projects depend on numerous third-party libraries, whose updates often introduce breaking changes. Adapting consumer code to such changes remains labor-intensive and error-prone. Existing work either characterizes dependency breaking changes without producing a verified consumer-side patch, or studies automated repair only in settings where the failure and repair context are contained within the target repository. However, dependency breaking changes violate this assumption: the decisive repair evidence lies upstream in release notes and API diffs, and no failing test localizes where the consumer breaks, leaving the repair under-informed. To study this cross-repository problem on real data, we introduce DepBench, a benchmark of 95 real-world dependency-update instances across four ecosystems, each paired with a Docker-based executable oracle that runs the consumer's own tests. To address these challenges, we propose DepRepair, a single-call LLM approach that grounds repair in structured upstream evidence through three components: an evidence filter that distills relevant upstream changes, a usage locator that identifies affected consumer sites, and a subcategory-aware guide that tailors repairs to the breaking-change type. Evaluated on DepBench, DepRepair attains the highest executable pass rate on each backbone, achieving 89.5% with GPT-5.5 and 82.1% with Claude Opus 4.6. We further find that raw upstream evidence reduces LLM and agent pass rates by 7--23 percentage points, whereas structured evidence consistently improves them.

cs.SE↗

arXiv2026

Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning

The rapid evolution of artificial intelligence has led to substantial advances in deep neural networks. Nonetheless, conventional GPU-based training remains highly energy-demanding, motivating the exploration of physical dynamics and compatible energy-based learning schemes, such as equilibrium propagation (EP). EP-based training, however, frequently suffers from convergence to local minima due to phase-space contraction. Here we introduce an Ising-dynamics-inspired equilibrium-propagation framework in which dissipative Hopfield relaxation is replaced by an extended phase-space dynamics with conjugate variables. The resulting training paradigm keeps the local two-phase learning rule of EP while changing the physical route by which neural states reach equilibrium. We show that this dynamics lowers effective energy barriers, accelerates convergence, improves noise robustness, and trains deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 with performance comparable to backpropagation.

cs.LG↗

arXiv2026

Optimizing Energy-based Neural Network Training with Coherent Ising Machine

While Ising machines serve as advanced physical solvers for the Ising model,enabling applications in combinatorial optimization and neural network training,their scalability for large-scale neural networks remains constrained by hardware connectivity limitations and suboptimal training methodologies. In this work,we leverage a Coherent Ising Machine (CIM) to train an energy-based neural network using Equilibrium Propagation, achieving performance comparable to existing software-based implementations. We further enhance the algorithm by integrating the Adam optimizer to solve for the ground state of a Hopfield energy network, significantly improving convergence speed and solution accuracy. Additionally, we demonstrate the scalability of our approach across deeper network architectures and convolutional operations. Our results highlight the potential of CIM dynamics as a scalable platform for training complex neural networks, offering a pathway toward energy-efficient implementations via analog circuits, optoelectronics, or integrated photonics. This work establishes a novel physical framework for next-generation AI hardware development.

cs.LG↗

arXiv2026

Causal Effect Estimation on Restricted Mean Survival Time in Case-Cohort Studies via a Matching Design

In large observational studies, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. For survival outcomes, literature has suggested that the restricted mean survival time (RMST) be a more appropriate marginal causal effect measure than the hazard ratio. In this paper, we develop a marginal causal effect estimation method for RMST difference under the stratified case-cohort design. We adjust for measured confounders using an innovative template matching design. Compared with conventional matching designs, template matching allows greater flexibility in the sample sizes of the exposed and unexposed groups. We establish the asymptotic properties of the proposed causal effect estimators and develop a bootstrap procedure to estimate their variances. By conducting comprehensive simulation studies, we evaluate the finite sample performance of the proposed estimators, demonstrate the advantage of template matching over conventional matching, and compare between matching on propensity score and matching on covariates. Finally, we apply the proposed methods to the Atherosclerosis Risk in Communities (ARIC) Study to estimate the marginal causal effect of serum hs-CRP level on the coronary heart disease-free survival.

stat.ME↗

arXiv2026

Spin-polarized Josephson current induced by inhomogeneous altermagnetic interlayers

The pursuit of dissipationless spin supercurrents is a central theme in superconducting spintronics. We propose a field-free Josephson junction using an inhomogeneous altermagnetic interlayer with in-plane N\'{e}el vectors. We show that the current-phase relation and the critical Josephson current are highly sensitive to the misorientation angle between the altermagnetic layers' N\'{e}el vectors. Specifically, at a $\pi$ misorientation with equal layer thicknesses the spatial oscillations of the superconducting pair amplitude, governed by the center-of-mass momentum, undergo mutual cancellation. This compensation suppresses individual layer pair-breaking, significantly enhancing the critical current and eliminating $0$-$\pi$ transitions. Furthermore, the non-collinear alignment of the N\'{e}el vectors facilitates the emergence of a net spin-polarized Josephson current. This spin current serves as a distinct signature of spin-triplet pair correlations, generated by the spin-dependent momentum shifts inherent to the altermagnetic exchange field. Our results establish a highly tunable, field-free platform for the realization of dissipationless spintronic devices.

cond-mat.supr-con↗

arXiv2026

Deep Speckle Holography Redefines Label-free Nanoparticle Phenotyping

Nanoparticle metrology has long been constrained by the assumption that, in mixed and unprocessed fluids, particle size, morphology, composition, and species-specific abundance cannot be resolved simultaneously from a single label-free measurement. Here, we revisit this long-standing limitation by showing that complex forward speckle-holographic fields define an information-rich optical space for multidimensional particle signatures. We report deep speckle holography, a physics-informed generative framework that profiles particle identity, size, morphology, and species-resolved abundance from a single non-contact optical measurement. Across purified suspensions, mixed particle populations, environmental waters, human urine, and other unprocessed native fluids, the method enables direct nanoparticle inference without purification, labeling, or destructive preprocessing, delivering concurrent multidimensional readouts in 0.9 s over a dynamic range spanning 10 orders of magnitude. Deep speckle holography establishes a route toward direct label-free nanoparticle phenotyping in real-world fluids, moving nanoscale measurement beyond isolated-particle characterization toward multidimensional inference in complex mixtures, and expanding the scope of questions nanoscale measurement can address, from real-time tracking of nanoparticle transformations in living and environmental systems to non-invasive quality control of nanomedicine formulations, and beyond.

eess.IV↗

arXiv2026

Sample-Efficient Learning with Online Expert Correction for Autonomous Catheter Steering in Endovascular Bifurcation Navigation

Robot-assisted endovascular intervention offers a safe and effective solution for remote catheter manipulation, reducing radiation exposure while enabling precise navigation. Reinforcement learning (RL) has recently emerged as a promising approach for autonomous catheter steering; however, conventional methods suffer from sparse reward design and reliance on static vascular models, limiting their sample efficiency and generalization to intraoperative variations. To overcome these challenges, this paper introduces a sample-efficient RL framework with online expert correction for autonomous catheter steering in endovascular bifurcation navigation. The proposed framework integrates three key components: (1) A segmentation-based pose estimation module for accurate real-time state feedback, (2) A fuzzy controller for bifurcation-aware orientation adjustment, and (3) A structured reward generator incorporating expert priors to guide policy learning. By leveraging online expert correction, the framework reduces exploration inefficiency and enhances policy robustness in complex vascular structures. Experimental validation on a robotic platform using a transparent vascular phantom demonstrates that the proposed approach achieves convergence in 123 training episodes -- a 25.9% reduction compared to the baseline Soft Actor-Critic (SAC) algorithm -- while reducing average positional error to 83.8% of the baseline. These results indicate that combining sample-efficient RL with online expert correction enables reliable and accurate catheter steering, particularly in anatomically challenging bifurcation scenarios critical for endovascular navigation.

cs.RO↗

arXiv2026

Deep-Learning-Based Control of a Decoupled Two-Segment Continuum Robot for Endoscopic Submucosal Dissection

Manual endoscopic submucosal dissection (ESD) is technically demanding, and existing single-segment robotic tools offer limited dexterity. These limitations motivate the development of more advanced solutions. To address this, DESectBot, a novel dual segment continuum robot with a decoupled structure and integrated surgical forceps, enabling 6 degrees of freedom (DoFs) tip dexterity for improved lesion targeting in ESD, was developed in this work. Deep learning controllers based on gated recurrent units (GRUs) for simultaneous tip position and orientation control, effectively handling the nonlinear coupling between continuum segments, were proposed. The GRU controller was benchmarked against Jacobian based inverse kinematics, model predictive control (MPC), a feedforward neural network (FNN), and a long short-term memory (LSTM) network. In nested-rectangle and Lissajous trajectory tracking tasks, the GRU achieved the lowest position/orientation RMSEs: 1.11 mm/ 4.62{\deg} and 0.81 mm/ 2.59{\deg}, respectively. For orientation control at a fixed position (four target poses), the GRU attained a mean RMSE of 0.14 mm and 0.72{\deg}, outperforming all alternatives. In a peg transfer task, the GRU achieved a 100% success rate (120 success/120 attempts) with an average transfer time of 11.8s, the STD significantly outperforms novice-controlled systems. Additionally, an ex vivo ESD demonstration grasping, elevating, and resecting tissue as the scalpel completed the cut confirmed that DESectBot provides sufficient stiffness to divide thick gastric mucosa and an operative workspace adequate for large lesions.These results confirm that GRU-based control significantly enhances precision, reliability, and usability in ESD surgical training scenarios.

cs.RO↗

arXiv2025

Crossed surface flat bands in three-dimensional superconducting altermagnets

Superconducting altermagnets have proven to be a promising ground for emergent phenomena, but their study has involved two-dimensional systems. In this work, we investigate three-dimensional $d$- and $g$-wave altermagnets with spin-singlet chiral $d$-wave superconductivity and show the formation of crossed surface flat bands due to the interplay between superconducting and altermagnetic symmetries. We find that these crossed flat bands are topologically protected, appear at zero energy in the surface along $z$ due to the superconducting nodal lines in the $xy$-plane, and their number of corners is determined by the crystal symmetry of altermagnets. We also show that the superconducting nodal lines give rise to Bogoliubov-Fermi surfaces, which then affect the appearance of zero-energy arcs in the surface along $x$. Moreover, we demonstrate that the crossed flat bands or surface arcs, and Bogoliubov-Fermi surfaces give rise to the coexistence of three distinct dependences of the charge conductance on the normal transparency, hence offering a solid way for their detection and paving the way for realizing higher-dimensional topological phases using altermagnets.

cond-mat.supr-con↗

arXiv2025

Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards

Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language Models (SLMs) offers a promising alternative, but current methods like Supervised Fine-Tuning (SFT) struggle with novelty, and Reinforcement Learning from Human Feedback (RLHF) is costly. This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback (RLAIF) framework to ignite the creative writing of a 7B-parameter SLM, specifically for generating Chinese greetings. The first strategy employs a RM trained on high-quality preference data curated by a novel multi-agent rejection sampling framework designed for creative tasks. The second, more novel strategy utilizes a principle-guided LLM-as-a-Judge, whose reward function is optimized via an adversarial training scheme with a reflection mechanism, to directly provide reward signals. Comprehensive experiments reveal that while both approaches significantly enhance creative output over baselines, the principle-guided LLM-as-a-Judge demonstrably yields superior generation quality. Furthermore, it offers notable advantages in training efficiency and reduced dependency on human-annotated data, presenting a more scalable and effective path towards creative SLMs. Our automated evaluation methods also exhibit strong alignment with human judgments. Our code and data are publicly available at https://github.com/weixiaolong94-hub/Igniting-Creative-Writing-in-Small-Language-Models.

cs.CL↗

arXiv2025

Gate-tunable nonreciprocal thermoelectric effects on the surface states of topological insulators

Thermoelectric devices at the nanoscale offer promising routes for on-chip refrigeration and waste-heat recovery, yet most semiconductor-based implementations suffer from limited tunability and narrow operational ranges. We introduce a highly flexible thermoelectric platform based on a ballistic junction formed by two gate-tunable regions of a topological insulator surface state bridged by a magnetic barrier. We theoretically demonstrate that such device exhibits strong electrical control over both refrigeration and thermoelectric power generation via side gates. We exploit the interplay between strong spin-orbit coupling and magnetism to achieve pronounced nonreciprocal transport, asymmetric cooling and tunable diode-like behavior. To demonstrate experimental feasibility, we further analyze refrigeration efficiency and phonon-limited performance in realistic material settings.

cond-mat.mes-hall↗

arXiv2025

Josephson diode effect in nanowire-based Andreev molecules

Superconducting systems exhibit non-reciprocal current transport under certain conditions of symmetry breaking, a phenomenon known as the superconducting diode effect. This effect allows for perfect rectification of supercurrent, and has received considerable research interest. We report the observation of the Josephson diode effect (JDE) in nanowire-based Andreev molecules, where the time-reversal and spatial-inversion symmetries of a Josephson junction (JJ) can be nonlocally broken by coherently coupling to another JJ. The JDE can be controlled using both non-local phase and gate voltages. Notably, the non-local phase can induce a sign reversal of the diode efficiency, a manifestation of regulating the probabilities of double elastic cotunneling and double-crossed Andreev reflection. Additionally, the diode efficiency can be further modulated by local and non-local gate voltages, exhibiting a central-peak feature in the gate-voltage space. Our theoretical calculations of the energy spectrum and the Josephson currents align well with the experimental results. These results demonstrate the non-local regulation of the JDE in Andreev molecules, offering significant implications for the control of multi-JJ devices and the development of advanced superconducting devices.

cond-mat.mes-hall↗

arXiv2025

Engineering subgap states in superconductors by the symmetry of altermagnetism

Combining superconducting and magnetic materials is a promising path to generate exotic interface subgap states. In this regard, altermagnetism is particularly interesting because it lifts spin degeneracy while providing tailored anisotropy of spin splittings. Here, we investigate the realization and control of subgap states by using the symmetry contrast between altermagnetic fields and unconventional pairings. When the symmetries of altermagnetism and unconventional superconductivity align, we demonstrate the emergence of bulk zero-energy flat bands as the Bogoliubov Fermi surface, giving rise to a zero-bias conductance peak. The symmetry and strength of $d$-wave altermagnets strongly affect the surface Andreev states from $d$-wave and chiral $d$- and $p$-wave superconductors. As a result, distinct types of subgap states are realized, including curved and flat bands, that can be detected by tunneling spectroscopy. Our results offer a solid route for designing and manipulating subgap states in superconducting systems, which can be useful for functionalizing superconducting devices.

cond-mat.supr-con↗

arXiv2025

Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions

Accurate three-dimensional (3D) reconstruction of guidewire shapes is crucial for precise navigation in robot-assisted endovascular interventions. Conventional 2D Digital Subtraction Angiography (DSA) is limited by the absence of depth information, leading to spatial ambiguities that hinder reliable guidewire shape sensing. This paper introduces a novel multimodal framework for real-time 3D guidewire reconstruction, combining preoperative 3D Computed Tomography Angiography (CTA) with intraoperative 2D DSA images. The method utilizes robust feature extraction to address noise and distortion in 2D DSA data, followed by deformable image registration to align the 2D projections with the 3D CTA model. Subsequently, the inverse projection algorithm reconstructs the 3D guidewire shape, providing real-time, accurate spatial information. This framework significantly enhances spatial awareness for robotic-assisted endovascular procedures, effectively bridging the gap between preoperative planning and intraoperative execution. The system demonstrates notable improvements in real-time processing speed, reconstruction accuracy, and computational efficiency. The proposed method achieves a projection error of 1.76$\pm$0.08 pixels and a length deviation of 2.93$\pm$0.15\%, with a frame rate of 39.3$\pm$1.5 frames per second (FPS). These advancements have the potential to optimize robotic performance and increase the precision of complex endovascular interventions, ultimately contributing to better clinical outcomes.

cs.RO↗

arXiv2025

A machine learning model for skillful climate system prediction

Climate system models (CSMs), through integrating cross-sphere interactions among the atmosphere, ocean, land, and cryosphere, have emerged as pivotal tools for deciphering climate dynamics and improving forecasting capabilities. Recent breakthroughs in artificial intelligence (AI)-driven meteorological modeling have demonstrated remarkable success in single-sphere systems and partially spheres coupled systems. However, the development of a fully coupled AI-based climate system model encompassing atmosphere-ocean-land-sea ice interactions has remained an unresolved challenge. This paper introduces FengShun-CSM, an AI-based CSM model that provides 60-day global daily forecasts for 29 critical variables across atmospheric, oceanic, terrestrial, and cryospheric domains. The model significantly outperforms the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) model in predicting most variables, particularly precipitation, land surface, and oceanic components. This enhanced capability is primarily attributed to its improved representation of intra-seasonal variability modes, most notably the Madden-Julian Oscillation (MJO). Remarkably, FengShun-CSM exhibits substantial potential in predicting subseasonal extreme events. Such breakthroughs will advance its applications in meteorological disaster mitigation, marine ecosystem conservation, and agricultural productivity enhancement. Furthermore, it validates the feasibility of developing AI-powered CSMs through machine learning technologies, establishing a transformative paradigm for next-generation Earth system modeling.

cs.LG↗

arXiv2025

Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions

Endovascular procedures have revolutionized vascular disease treatment, yet their manual execution is challenged by the demands for high precision, operator fatigue, and radiation exposure. Robotic systems have emerged as transformative solutions to mitigate these inherent limitations. A pivotal moment has arrived, where a confluence of pressing clinical needs and breakthroughs in AI creates an opportunity for a paradigm shift toward Embodied Intelligence (EI), enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, leveraging advanced computer vision, medical image analysis, and machine learning, drive this evolution by enabling real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further enhance navigation strategies and replicate expert techniques. This review systematically analyzes the integration of EI into endovascular robotics, identifying profound systemic challenges such as the heterogeneity in validation standards and the gap between human mimicry and machine-native capabilities. Based on this analysis, a conceptual roadmap is proposed that reframes the ultimate objective away from systems that supplant clinical decision-making. This vision of augmented intelligence, where the clinician's role evolves into that of a high-level supervisor, provides a principled foundation for the future of the field.

cs.RO↗

arXiv2025

Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes.

cs.RO↗

arXiv2025

Generative assimilation and prediction for weather and climate

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.

cs.LG↗