arXiv subjects

Li Yang

Publications and source records attributed to Li Yang.

At least 19 recordsLinked to original sources

arXiv2026

Moir\'e Topology in Twisted Structures with Noncollinear Spin-Orbit Coupling

Moir\'e superlattices provide a powerful platform for flat bands and correlated topological phases, yet most established examples rely on valley-contrasting Berry curvature in hexagonal lattices. Here, we propose a different route to achieve topological moir\'e minibands based on noncollinear spin orbit coupling in centrosymmetric type II SOC bilayers. Interlayer hybridization opens local pseudogaps and produces sharply localized Berry curvature, which twisting reconstructs into isolated topological minibands without requiring valley degrees of freedom or hexagonal symmetry. We demonstrate this mechanism in tetragonal Dresselhaus-SOC HgI2, where a Lieb-like moir\'e potential yields topological flat bands. To improve miniband isolation, we develop a physics-informed machine-learning surrogate that identifies stronger SOC as a key design principle and guides the replacement of Hg by Pb. The resulting PbI2 minibands are narrower and better isolated, supporting correlation-driven magnetism and tunable quantum spin Hall and Chern insulating phases, thereby providing an optimized material realization for experimental exploration.

cond-mat.mtrl-sci↗

arXiv2026

Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding

The integration of novel view synthesis (NVS) and open-vocabulary segmentation (OVS) has recently yielded powerful feed-forward 3D foundation models. However, their inherent reliance on static-scene assumptions leads to severe misalignment of spatial features in unconstrained dynamic environments. To bridge this critical gap, we propose SPAR, a novel joint semantic-geometric encoding architecture that explicitly isolates transient dynamic noise prior to latent space aggregation. Furthermore, we introduce a dynamic-region-aware end-to-end training paradigm that structurally couples motion estimation with multi-view visual and semantic learning. This unified approach enables the network to inherently resolve motion conflicts and distill multi-view consistent, temporally stable scene representations from dynamic inputs. Extensive experiments on the challenging D-RE10K benchmark demonstrate that SPAR achieves state-of-the-art performance. Our end-to-end approach achieves exceptional novel view synthesis quality, yielding a PSNR of 22.15 dB and 23.33 dB given only 3 and 4 input views respectively. Despite being trained in a self-supervised manner, our model achieves an mIoU of 88.5% for motion mask prediction. Furthermore, our analysis reveals a strong inter-task synergy between photometric scene reconstruction and semantic understanding, where semantic synthesis learning consistently enhances photometric fidelity in novel view rendering. Code will be available at https://github.com/dmucby/SPAR.

cs.CV↗

arXiv2026

Anomalous stabilization of excitons by metallic proximity

Metallic environments are generally expected to suppress excitons through strong dielectric screening, yet their influence can differ in composite systems where metallic and insulating regions coexist. Here, we investigate excitonic states in PdxCu1-xCrO2 thin films across a percolation-driven metal-insulator transition. Optical spectroscopy and many-body GW calculations show that CuCrO2 hosts strongly bound excitons with a binding energy of about 489 meV. With increasing Pd substitution, the system approaches an insulator-to-metal transition near x = 0.5, consistent with the site-percolation threshold of a triangular lattice. In the pre-percolation regime, the excitonic resonance redshifts by 241 meV while the Tanguy continuum onset remains nearly unchanged, consistent with a substantial increase in exciton binding energy before metallization. An image-charge-based excitonic hydrogen model shows that isolated metallic regions can enhance electron-hole binding through image-charge interactions, whereas conventional screening is recovered once a continuous metallic network forms. Although this model provides a possible interpretation of the observed excitonic evolution, an alternative scenario in which metallic and excitonic responses originate from electronically distinct states and evolve independently cannot be excluded. These results reveal unusual metallic-excitonic coexistence near a percolation-driven metal-insulator transition and suggest nanoscale metallic proximity as a possible route for modifying excitonic interactions.

cond-mat.str-el↗

arXiv2026

Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination

Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data is trustworthy. In practice, training data may be corrupted through compromised logs, labeling errors, manipulated historian records, or unsafe retraining processes. This paper evaluates the robustness of offline ICS anomaly-detection pipelines on the Secure Water Treatment (SWaT) benchmark under training-time contamination. We assess 11 heterogeneous anomaly detectors under three contamination strategies: random injection, similarity-targeted injection, and feature-noise injection. The first two insert attack samples into the nominal training pool, while the third adds bounded Gaussian noise to selected normal training samples. These attacks are contamination-based rather than gradient-driven poisoning methods. Contamination budgets from 1% to 10% are evaluated using clean validation and test sets under a unified offline protocol. The results show that robustness is strongly model-dependent and cannot be predicted from clean-data performance alone. Injection-based contamination causes the greatest degradation, particularly for local-density and distance-based detectors, whereas feature-noise contamination has a comparatively limited effect. PCA, SVM, HBOS, and IForest remain relatively stable, while the tuned neural detectors demonstrate intermediate robustness. Overall, the findings highlight the importance of training-data integrity in ML-enabled ICS monitoring, subject to the evaluated dataset, models, and threat assumptions.

cs.CR↗

arXiv2026

Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies

A dual-representation flow-matching policy decodes each predicted motion into joint and end-effector spaces, and the residual between the two kinematically equivalent decodings provides a physically interpretable runtime signal. On multimodal tasks, however, independently sampled branches may choose different valid modes, causing false alarms. We study how to coordinate the two branches and at what cost. Across two robot environments and a non-robotic testbed, the tested mechanisms fall into four classes. An auxiliary latent shared by both branches but absent from the flow-matching construction is erased at the population optimum, a provable dead end confirmed within a prespecified 2% equivalence band. Sharing source noise can coordinate or anti-coordinate: its effect changes sign with the representation map and tracks the alignment of decoder mode basins. Consistency regularization gives intermediate coordination but reduces the valid-pair rate, while training-supported discrete partitions achieve near-ceiling coordination robustly. We further derive a chance-corrected coordination bound based only on each branch's Gini-Simpson diversity, yielding an attainable region and a label-free certificate that separates coordination from collapse when zero mismatch is ambiguous. On LIBERO-Plus, benign multimodality adds 1.57 percentage points of false alarms to the residual, which remains the strongest evaluated failure signal; the preregistered token intervention does not meet its false-alarm criterion or produce a seed-robust detection change. Code, models, and per-run configurations are available at https://github.com/kimo423/dual-head-coordination.

cs.RO↗

arXiv2026

A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems (IDSs) are essential for securing EV charging networks; however, conventional Machine Learning (ML)-based IDSs often rely on manual model design and mainly optimize detection performance without fully considering inference latency and model size. In this paper, a Multi-Objective Automated ML (MOO-AutoML)-based efficient IDS is proposed for EVCS security. The proposed framework uses a lightweight training strategy and a LightGBM-based automated feature selection method to select compact feature subsets based on accumulated feature importance. Then, Non-dominated Sorting Genetic Algorithm III (NSGA-III) jointly optimizes the feature selection threshold and key LightGBM hyperparameters under three objectives: maximizing weighted F1-score, minimizing 99th percentile inference latency ratio, and minimizing model size ratio. Experiments on CICEVSE2024 and CICIDS2017 show that the proposed MOO-AutoML IDS achieves competitive weighted F1-scores, lower P99 inference latency, and smaller model sizes than the compared methods. Overall, the results indicate that the proposed method can support accurate and efficient intrusion detection for EVCS and IoT security under practical deployment constraints.

cs.CR↗

arXiv2026

Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions

Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets. Neural Architecture Search (NAS) offers a systematic alternative to manual design. It automates the search over candidate architectures of deep learning models, finding designs that match the spatial-temporal structure of traffic data without manual trial and error. This survey reviews NAS methods applied to traffic prediction, organized by search strategy: gradient-based methods, evolutionary methods, and one-shot weight-sharing methods. For each category, we analyze how the search space is designed to cover spatial and temporal traffic operators, and how the search strategy balances cost against architecture quality. We also discuss open challenges, computational scalability to large road networks, manual search space design, cross-city generalization, dynamic graph structure, and the open question of NAS for spatial-temporal foundation models, and identify directions for future research.

cs.LG↗

arXiv2026

Beyond Janus Atomic Ordering: High-Throughput First-Principles Search for Hidden MoSO Monolayer Structures

Despite the growing interest in two-dimensional (2D) MoSO systems, existing studies have exclusively focused on conventional Janus structures. In this work, we perform high-throughput first-principles calculations to explore novel stable 2D MoSO monolayers. Combined with random sampling strategy, graph theory and group theory, we successfully screen out three novel non-Janus 2D MoSO monolayers from 1325 candidate structures, namely Reversed 2H-MoSO, Hybrid 2H-MoSO, and Hybrid 1T'-MoSO. Compared with Janus MoSO monolayers, the non-Janus MoSO counterparts possess lower binding energies, varying from -4.38 to -4.51 eV/atom. A systematic combination of dynamic, thermodynamic, and mechanical stability analyses corroborates their excellent structural robustness. Ab initio molecular dynamics (AIMD) simulations confirm their superior thermal resistance, with the structures remaining stable at temperatures beyond 2000 K. Interestingly, unlike the semiconducting Janus MoSO, the Hybrid 1T'-MoSO monolayer exhibits distinct metallic characteristics. Furthermore, we found that strain and curvature can enable controlled phase transitions of MoSO among semiconducting, semimetallic, and metallic phases. More importantly, the Hybrid 1T'-MoSO exhibits favorable HER activity with a Gibbs free energy of -0.002 eV, rendering it a promising candidate for hydrogen evolution catalysis. This work not only expands the family of 2D MoSO materials but also provides a reliable strategy for discovering stable functional 2D materials via high-throughput computation.

cond-mat.mtrl-sci↗

arXiv2026

Bash-Commenter: Leveraging Syntax-Aware Preference Optimization to Reinforce Large Language Model for Bash Code Comment Generation

Bash script comprehension is challenging due to Bash's syntactic freedom and complex command structures. Despite its critical role in system administration, Bash scripts often lack adequate comments, hindering readability and maintainability. Existing automated comment generation approaches face two main challenges: (1) limited training datasets that inadequately represent real-world Bash usage patterns; and (2) insufficient understanding of Bash-specific concepts by Large Language Models (LLMs). To address these, we propose Bash-Commenter, an advanced comment generation method based on LLaMA-3.1-8B. First, we construct a comprehensive dataset of complex, multi-line Bash scripts with high-quality comments. Second, we conduct Continual Pre-training (CPT) on large-scale Bash data, followed by Supervised Fine-tuning (SFT), strengthening the model's foundational knowledge of Bash syntax and semantics. Finally, we introduce Syntax-Aware Preference Optimization (SAPO), which constructs preference pairs by applying atomic operations to a script's Abstract Syntax Tree (AST), creating minimal pairs of correct and subtly incorrect scripts for fine-grained semantics learning. Our method outperforms state-of-the-art baselines, achieving 33.40% BLEU-4, 58.26% METEOR, and 57.03% ROUGE-L for 1,064 single-line commands, and 22.15% BLEU-4, 43.89% METEOR, and 32.80% ROUGE-L for 1,046 multi-line scripts. Human and LLM evaluations further confirm superior comment quality in correctness, completeness, and naturalness.

cs.SE↗

arXiv2026

Aurora: A Leverage-Aware Spectral Optimizer

We show that for tall matrix parameters, like projection matrices in the MLP layers, the Muon update can have row norms that are arbitrarily non-uniform. This can lead to a self-reinforcing feedback loop whereby neurons receive persistently small updates and eventually do not contribute meaningfully to network outputs. This problem is effectively mitigated by an additional row normalization step, but current methods do this in a way that moves the Muon update geometry away from the polar factor of the momentum matrix, which we find is undesirable. We propose Aurora, an optimizer that enforces row-uniformity of matrix parameter updates while respecting Muon's polar factor geometry. Aurora outperforms Muon in our pre-training experiments and, when combined with existing methods, achieves state-of-the-art performance among spectral optimizers on the optimizer track of the modded-nanoGPT speedrun. Additionally, we find that Aurora's empirical gains over Muon scale with the MLP expansion factor, suggesting that Aurora may allow for effective training of very wide MLP layers.

cs.LG↗

arXiv2026

BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization

Bash scripts are critical for system administration, DevOps, and CI/CD, where code quality affects stability and security. However, LLM-generated scripts often lack reasoning and contain robustness flaws such as mishandled edge cases and unchecked failures. This is particularly critical in production environments where even minor errors can lead to service disruptions. We propose BashCoder-R1, a framework that jointly addresses both issues by treating explainability as a design goal. The pipeline has three stages. Continual Pre-training adapts to Bash syntax. Long Chain-of-Thought Supervised Fine-Tuning on expert-validated samples teaches risk-averse reasoning before code generation. Robustness-Aware Group Relative Policy Optimization optimizes a weighted reward for syntax correctness, robustness (verified by shellcheck), and format adherence. This staged design ensures that the model progressively acquires syntax knowledge, reasoning capability, and robust decision-making. On our BashBench benchmark (952 real-world tasks, 773 single-line and 179 multi-line), BashCoder-R1 achieves SyntaxPass of 100.00/94.97, RobustWarnRate of 4.01/16.47, RobustPass of 95.99/79.33, FuncRate of 93.01/93.85, and FullRate of 90.04/73.18 for single-line and multi-line tasks, respectively. These are relative FullRate improvements of 37.82 and 20.18 percent over the strongest baseline, DeepSeek-V3.2 (Reasoning). Human evaluation confirms its reasoning chains are highest in quality.

cs.SE↗

arXiv2026

ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems

The open-source ecosystem on GitHub lacks a systematic hierarchical taxonomy of software repositories. GitHub Topics, the dominant organizational mechanism, is flat, inconsistent, and covers only 67% of projects. We present ATLAS, the first framework that automatically constructs a hierarchical taxonomy for software repositories and classifies projects into it end-to-end. By combining LLM global knowledge with real repository distributions, ATLAS proposes meaningful splitting dimensions and iteratively corrects those that fail to accommodate real projects. A Designer Agent proposes splitting dimensions while a Classifier Agent assigns repositories; a self-corrective refinement loop uses classification failures to drive dimension revision through escalating strategies. We evaluate ATLAS on 54,387 GitHub repositories against six baselines spanning four paradigms, two downstream tasks, and three model families. On a stratified 2,001-repository benchmark, ATLAS achieves a Taxonomy Quality F-score (TQF) of 83.13%, outperforming the best baseline by 15 percentage points (on the full 54k corpus the approximate TQF is 73.0%, a gap driven by Path Granularity's all-or-nothing scoring on longer paths rather than lower classification accuracy). It is the only method to simultaneously achieve high structural quality and high practical applicability. On downstream tasks, ATLAS enables alternative discovery with P@1 = 85.71%, surpassing even human-curated lists (62.34%), and achieves the highest P@1 for repository retrieval. The taxonomy further reveals structural ecosystem trends that are difficult to obtain from flat tags or similarity methods: the shift from libraries to AI/ML applications (now 61% of newly community-adopted projects) becomes visible only through hierarchical, type-based categorization. An interactive taxonomy explorer is available at https://atlas-taxonomy.netlify.app/

cs.SE↗

arXiv2026

High-order multi-structures-preserving exponential integrators for the derivative nonlinear Schr\"{o}dinger equation

This paper presents a novel class of high-order mass-, energy- and momentum-preserving exponential integrators for solving the derivative nonlinear Schr\"{o}dinger equation. Firstly, we reformulate the original system into an exponential supplementary variable system based on the idea of the exponential supplementary variable approach, and then the reformulated system is discretized by using the standard Fourier pseudo-spectral method in space and the high-order prediction and correction Lawson Runge-Kutta method in time, respectively. The proposed method is highly efficient, temporally high-order accurate, and simultaneously preserves the mass, energy and momentum in the discrete setting. Finally, numerical experiments validate the accuracy and energy-preserving properties.

math.NA↗

arXiv2026

Dominant-Layer ZO: A Single Layer Dominates Zeroth-Order Fine-Tuning of LLMs

Zeroth-order (ZO) optimization enables memory-efficient fine-tuning of large language models (LLMs) using only forward passes, but it remains unclear how useful adaptation is distributed across layers. In this work, we reveal a surprising phenomenon: ZO fine-tuning is sharply dominated by a single decoding layer. Across multiple LLM families and downstream tasks, fine-tuning this dominant layer alone consistently matches or even exceeds full-model ZO fine-tuning. We further show that the dominant layer is task-agnostic but model-specific, and can be identified before training through a simple inference-only analysis of activation outliers. Specifically, the dominant layer consistently aligns with the first activation-outlier layer in the pre-trained model. To explain this phenomenon, we analyze how perturbation effects propagate under ZO optimization. We find that the dominant layer combines two key properties: high perturbation sensitivity and early placement in the residual stream, allowing perturbation-induced effects to propagate and accumulate through remaining subsequent decoding layers. As a result, this layer produces disproportionately strong and stable optimization signals under forward-only updates. Extensive experiments on LLaMA2-7B and Qwen3-8B across nine benchmarks show that dominant-layer ZO fine-tuning improves average performance over full-model MeZO and LoRA-based ZO fine-tuning while achieving up to 4.52$\times$ training speedup.

cs.LG↗

arXiv2026

Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning

Data rehearsal has emerged as a leading approach for mitigating catastrophic forgetting in Continual Reinforcement Learning (CRL). However, existing work remains confined to policy gradient frameworks, regularizing only actors due to the performance degradation incurred by critic regularization. This actor-centric approach overlooks the potential of data rehearsal for value function approximation. Moreover, existing evaluations in CRL rarely consider multi-cyclic environments where task sequences repeat, a critical real-world scenario that exacerbates forgetting and plasticity. We investigate data rehearsal for Deep Q-Networks using Q-value regularization in multi-cyclic settings and propose Qreg+NWLU which introduces two simple modifications: (1) continuous data rehearsal that dynamically collects and updates stored Q-values throughout training, and (2) "No-Wait" regularization that applies immediately rather than after the first task. Together, these modifications yield improvements in learning efficiency, forgetting mitigation, and knowledge transfer over Qreg and conventional CRL methods within value function approximation settings.

cs.LG↗

arXiv2026

$\mathcal{B}^{3}$-Net: Controlled Posterior Bridge Learning for Multi-Task Dense Prediction

Multi-task dense prediction solves complementary pixel-level tasks in a unified model, such as semantic segmentation, depth estimation, surface normal estimation, and edge detection. Existing decoder-side interactions use attention, prompts, routing, diffusion, Mamba, or bridge features to exchange task evidence, but most of them organize this evidence implicitly. They usually fuse task features by similarity or affinity, without explicitly modeling that evidence reliability varies across tasks and spatial locations. As a result, unreliable evidence may contaminate the shared representation and intensify negative transfer. We propose $\mathcal{B}^{3}$-Net, a controlled posterior bridge learning framework for multi-task dense prediction. Our method decomposes decoder-side interaction into reliability estimation, posterior bridge construction, and bounded redistribution. The Precision Field Estimator estimates patch-wise evidence precision from task-reference alignment and local variation. The Posterior Bridge Operator builds a precision-weighted posterior bridge through heteroscedastic evidence fusion, yielding a shared state more reliable than uniform or heuristic mixtures. The Contractive Dispatch Operator redistributes the bridge to each task branch through a bounded update, reducing uncontrolled feature injection. Experiments on NYUD-v2, PASCAL-Context, and Cityscapes show that $\mathcal{B}^{3}$-Net achieves competitive or superior trade-offs over representative CNN-, Transformer-, diffusion-, Mamba-, and bridge-feature-based methods. Backbone-matched comparisons and extensive analyses further verify that the gains arise from controlled posterior bridge learning rather than backbone capacity or decoder scale.

cs.CV↗

arXiv2026

M\textsuperscript{4}Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation

Encoder-decoder imbalance and the reliance on large input volumes make many 3D brain tumor segmentation models both compute-heavy and brittle. We present M\textsuperscript{4}Fuse, a lightweight network that prioritizes discriminative brain tumor cues over exhaustive appearance reconstruction. Our method balances encoder and decoder capacity and replaces depth expansion with a synergistic design: it propagates long-range context with linear complexity via a grouped state space mixer, denoises and aligns skip features using a cross-scale dual-stage gating bridge, and absorbs cross-site acquisition shifts with a sample-level mixture-of-experts. On the BraTS2019 and BraTS2021 benchmarks, M\textsuperscript{4}Fuse outperforms other lightweight excellent methods in both parameter count and performance. Even at a challenging input resolution of $64\times128\times128$ (half that of existing excellent models), M\textsuperscript{4}Fuse reduces parameters by 62.63\% and improves average performance by 0.09\%. Ablations of key components validate the method's exceptional parameter-to-accuracy efficiency and robustness across diverse data centers.

cs.CV↗

arXiv2026

Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos

Capsule endoscopy (CE) enables non-invasive gastrointestinal screening, but current CE research remains largely limited to frame-level classification and detection, leaving video-level analysis underexplored. To bridge this gap, we introduce and formally define a new task, diagnosis-driven CE video summarization, which requires extracting key evidence frames that covers clinically meaningful findings and making accurate diagnoses from those evidence frames. This setting is challenging because diagnostically relevant events are extremely sparse and can be overwhelmed by tens of thousands of redundant normal frames, while individual observations are often ambiguous due to motion blur, debris, specular highlights, and rapid viewpoint changes. To facilitate research in this direction, we introduce VideoCAP, the first CE dataset with diagnosis-driven annotations derived from real clinical reports. VideoCAP comprises 240 full-length videos and provides realistic supervision for both key evidence frame extraction and diagnosis. To address this task, we further propose DiCE, a clinician-inspired framework that mirrors the standard CE reading workflow. DiCE first performs efficient candidate screening over the raw video, then uses a Context Weaver to organize candidates into coherent diagnostic contexts that preserve distinct lesion events, and an Evidence Converger to aggregate multi-frame evidence within each context into robust clip-level judgments. Experiments show that DiCE consistently outperforms state-of-the-art methods, producing concise and clinically reliable diagnostic summaries. These results highlight diagnosis-driven contextual reasoning as a promising paradigm for ultra-long CE video summarization.

cs.CV↗