arXiv subjects

Xiaokun Yang

Publications and source records attributed to Xiaokun Yang.

8 recordsLinked to original sources

arXiv2026

A Novel Nb-Based Eutectic Superalloy with Exceptional Ultrahigh-Temperature Mechanical Properties

Refractory alloys operating above 1300 °C, beyond the limit of Ni-based superalloys, must balance high-temperature strength, thermal stability, low density, low cost, and room-temperature tensile ductility, a combination that existing refractory alloys have yet to achieve. Here we report a castable Nb-based eutectic superalloy, Nb-10Mo-9.5C (at. %, NMC-1), comprising a uniform lamellar structure of Nb-Mo solid solution and Nb2C carbide phases. NMC-1 achieves 0.2% yield strengths of ~300 MPa and ~200 MPa at 1300 °C and 1500 °C, respectively, among the highest reported for Nb-based alloys, with pronounced strain hardening, non-zero room-temperature tensile ductility, and no observed microstructural coarsening after 100 h at 1400 °C. Its low density (8.65 g/cc) and cost (~$85/kg) yield a specific-strength-per-cost merit index that far surpasses commercial Nb, Mo, Ta, and W alloys, establishing a new design paradigm for ultrahigh-temperature structural materials.

cond-mat.mtrl-sci↗

arXiv2026

When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability

An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed. We treat this evaluator-replacement ambiguity as a measurement-validity problem. Across four judgment datasets, we compare two upgrade paths available in practice: scaling Qwen3 dense judges from 1.7B to 32B parameters and moving across MiniMax M2-M2.7 released APIs. The main pattern is that judge upgrades are not interchangeable: only Qwen3 1.7B to 4B gives a robust adjacent gain, while MiniMax adjacent releases do not. Stronger judges reduce but do not remove position and verbosity bias. Repeated-sample juries add little when errors are correlated. Structured debate can move decisions substantially, but without parser and fallback logs those shifts cannot be attributed to deliberation. We argue that LLM-as-judge reports should include dataset slices, bias probes, error-dependence estimates, and protocol audit trails.

cs.CL↗

arXiv2026

Application of Hybrid Chain Storage Framework in Energy Trading and Carbon Asset Management

Distributed energy trading and carbon asset management involve high-frequency, small-value settlements with strong audit requirements. Fully on-chain designs incur excessive cost, while purely off-chain approaches lack verifiable consistency. This paper presents a hybrid on-chain and off-chain settlement framework that anchors settlement commitments and key constraints on-chain and links off-chain records through deterministic digests and replayable auditing. Experiments under publicly constrained workloads show that the framework significantly reduces on-chain execution and storage cost while preserving audit trustworthiness.

cs.CR↗

arXiv2026

Degradation-Consistent Paired Training for Robust AI-Generated Image Detection

AI-generated image detectors suffer significant performance degradation under real-world image corruptions such as JPEG compression, Gaussian blur, and resolution downsampling. We observe that state-of-the-art methods, including B-Free, treat degradation robustness as a byproduct of data augmentation rather than an explicit training objective. In this work, we propose Degradation-Consistent Paired Training (DCPT), a simple yet effective training strategy that explicitly enforces robustness through paired consistency constraints. For each training image, we construct a clean view and a degraded view, then impose two constraints: a feature consistency loss that minimizes the cosine distance between clean and degraded representations, and a prediction consistency loss based on symmetric KL divergence that aligns output distributions across views. DCPT adds zero additional parameters and zero inference overhead. Experiments on the Synthbuster benchmark (9 generators, 8 degradation conditions) demonstrate that DCPT improves the degraded-condition average accuracy by 9.1 percentage points compared to an identical baseline without paired training, while sacrificing only 0.9% clean accuracy. The improvement is most pronounced under JPEG compression (+15.7% to +17.9%). Ablation further reveals that adding architectural components leads to overfitting on limited training data, confirming that training objective improvement is more effective than architectural augmentation for degradation robustness.

cs.CV↗

arXiv2026

SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution

Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts federated collaboration as robots scale, and static resource allocation failing to share containers across robots when workloads shift dynamically. We present SwiftBot, a federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% decomposition accuracy across diverse tasks, reduces task startup latency by 1.5-5.4x and average training latency by 1.4-2.5x, and improves tail latency by 1.2-4.7x under high load through federated warm container migration. Evaluation on multimedia tasks validates that co-designing semantic understanding and federated resource management enables both flexibility and efficiency for robotic task control.

cs.RO↗

arXiv2025

Towards An Approach to Identify Divergences in Hardware Designs for HPC Workloads

Developing efficient hardware accelerators for mathematical kernels used in scientific applications and machine learning has traditionally been a labor-intensive task. These accelerators typically require low-level programming in Verilog or other hardware description languages, along with significant manual optimization effort. Recently, to alleviate this challenge, high-level hardware design tools like Chisel and High-Level Synthesis have emerged. However, as with any compiler, some of the generated hardware may be suboptimal compared to expert-crafted designs. Understanding where these inefficiencies arise is crucial, as it provides valuable insights for both users and tool developers. In this paper, we propose a methodology to hierarchically decompose mathematical kernels - such as Fourier transforms, matrix multiplication, and QR factorization - into a set of common building blocks or primitives. Then the primitives are implemented in the different programming environments, and the larger algorithms get assembled. Furthermore, we employ an automatic approach to investigate the achievable frequency and required resources. Performing this experimentation at each level will provide fairer comparisons between designs and offer guidance for both tool developers and hardware designers to adopt better practices.

cs.AR↗

arXiv2021

New general DBI action, its solution to the paradox of the conversion of kinetic and potential energy in equal rights and their true applications to inflationary cosmology

The Dirac-Born-Infeld (DBI) field theory in string theory is important and can provide the field of the universe's inflation. At the same time, it provides a causal mechanism for generating the original density perturbation, thereby providing the necessary density perturbation for existing the dense and sparse matter distributions of the universe. However, there is the paradox of the conversion of potential energy and kinetic energy in equal rights in string theory. Therefore, we give a new general DBI action, which enables the kinetic energy and potential energy in the action to be converted each other in equal rights, i.e., solving the paradox. Therefore, we deduce a new general DBI action, introduce it into inflationary cosmology to calculate various inflation parameters, further calculate the scalar perturbation spectrum and the tensor-scalar ratio, which are compared with Planck + WMAP9 + BAO data, the power spectrum predicted by the new general DBI inflation theory satisfies the CMB Experiment constraints, i.e., is consistent with the current theories and experimental observations. Consequently, the theory of this paper conforms to current experiments and is supplying the current theories, and also a new way of explaining the inflation of the universe.

gr-qc↗

arXiv2018

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.

cs.CL↗