arXiv subjects

Yang Xiao

Publications and source records attributed to Yang Xiao.

At least 127 records · Page 7Linked to original sources

arXiv2025

Rethinking Telemetry Design for Fine-Grained Anomaly Detection in 5G User Planes

Detecting QoS anomalies in 5G user planes requires fine-grained per-flow visibility, but existing telemetry approaches face a fundamental trade-off. Coarse per-class counters are lightweight but mask transient and per-flow anomalies, while per-packet telemetry postcards provide full visibility at prohibitive cost that grows linearly with line rate. Selective postcard schemes reduce overhead but miss anomalies that fall below configured thresholds or occur during brief intervals. We present Kestrel, a sketch-based telemetry system for 5G user planes that provides fine-grained visibility into key metric distributions such as latency tails and inter-arrival times at a fraction of the cost of per-packet postcards. Kestrel extends Count-Min Sketch with histogram-augmented buckets and per-queue partitioning, which compress per-packet measurements into compact summaries while preserving anomaly-relevant signals. We develop formal detectability guarantees that account for sketch collisions, yielding principled sizing rules and binning strategies that maximize anomaly separability. Our evaluations on a 5G testbed with Intel Tofino switches show that Kestrel achieves 10% better detection accuracy than existing selective postcard schemes while reducing export bandwidth by 10x.

cs.NI↗

arXiv2025

Context Engineering 2.0: The Context of Context Engineering

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with other entities, within which contexts play a constitutive and essential role. With the advent of computers and artificial intelligence, these contexts are no longer limited to purely human--human interactions: human--machine interactions are included as well. Then a central question emerges: How can machines better understand our situations and purposes? To address this challenge, researchers have recently introduced the concept of context engineering. Although it is often regarded as a recent innovation of the agent era, we argue that related practices can be traced back more than twenty years. Since the early 1990s, the field has evolved through distinct historical phases, each shaped by the intelligence level of machines: from early human--computer interaction frameworks built around primitive computers, to today's human--agent interaction paradigms driven by intelligent agents, and potentially to human--level or superhuman intelligence in the future. In this paper, we situate context engineering, provide a systematic definition, outline its historical and conceptual landscape, and examine key design considerations for practice. By addressing these questions, we aim to offer a conceptual foundation for context engineering and sketch its promising future. This paper is a stepping stone for a broader community effort toward systematic context engineering in AI systems.

cs.AI↗

arXiv2025

Giant-Atom Quantum Batteries

Environmentally induced decoherence poses a fundamental challenge to quantum energy storage systems, causing irreversible energy dissipation and performance aging of quantum batteries (QBs). To address this issue, we propose a QB protocol utilizing the nonlocal coupling properties of giant atoms (GAs). In this architecture, both the QB and its charger are implemented as superconducting GAs with multiple nonlocal coupling points to a shared microwave waveguide. By engineering these atoms in a braided configuration, where their coupling paths are spatially interleaved, we show the emergence of decoherence-immune interaction dynamics. This unique geometry enables destructive interference between decoherence channels while preserving coherent energy transfer between the charger and the QB, thereby effectively suppressing the aging effects induced by waveguide-mediated dissipation. The charging properties of separated and nested coupled configurations are investigated. The results show that these two configurations underperform the braided configuration. Additionally, we propose a long-range chiral charging scheme that facilitates unidirectional energy transfer between the charger and the battery, with the capability to reverse the flow direction by modulating the applied magnetic flux. Our result provides guidelines for implementing a decoherence-resistant charging protocol and remote chiral QBs in circuits with GAs engineering.

quant-ph↗

arXiv2025

WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition

While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context and fine-grained detail preservation. To address this, we propose a novel decoder architecture, WaveSeg, which jointly optimizes feature refinement in spatial and wavelet domains. Specifically, high-frequency components are first learned from input images as explicit priors to reinforce boundary details at early stages. A multi-scale fusion mechanism, Dual Domain Operation (DDO), is then applied, and the novel Spectrum Decomposition Attention (SDA) block is proposed, which is developed to leverage Mamba's linear-complexity long-range modeling to enhance high-frequency structural details. Meanwhile, reparameterized convolutions are applied to preserve low-frequency semantic integrity in the wavelet domain. Finally, a residual-guided fusion integrates multi-scale features with boundary-aware representations at native resolution, producing semantically and structurally rich feature maps. Extensive experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches both quantitatively and qualitatively, achieving efficient and precise segmentation.

cs.CV↗

arXiv2025

LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling

Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data distilled from more powerful large reasoning models (LRMs). However, these reasoning chains often contain verbose elements that mirror human problem-solving, categorized as progressive reasoning (the essential solution development path) and functional elements (verification processes, alternative solution approaches, and error corrections). While progressive reasoning is crucial, the functional elements significantly increase computational demands during test-time inference. We introduce PIR (Perplexity-based Importance Refinement), a principled framework that quantitatively evaluates the importance of each reasoning step based on its impact on answer prediction confidence. PIR systematically identifies and selectively prunes only low-importance functional steps while preserving progressive reasoning components, creating optimized training data that maintains the integrity of the core solution path while reducing verbosity. Models fine-tuned on PIR-optimized data exhibit superior test-time scaling properties, generating more concise reasoning chains while achieving improved accuracy (+0.9\% to +6.6\%) with significantly reduced token usage (-3\% to -41\%) across challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond). Our approach demonstrates strong generalizability across different model sizes, data sources, and token budgets, offering a practical solution for deploying reasoning-capable LLMs in scenarios where efficient test-time scaling, response time, and computational efficiency are valuable constraints.

cs.CL↗

arXiv2025

Enhancing Phase Transition Calculations with Fitting and Neural Network

The computation of bounce action in a phase transition involves solving partial differential equations, inherently introducing non-negligible numerical uncertainty. Deriving characteristic temperatures and properties of this transition necessitates both differentiation and integration of the action, thereby exacerbating the uncertainty. In this work, we fit the action curve as a function of temperature to mitigate the uncertainties inherent in the calculation of the phase transition parameters. We find that, after extracting a factor, the sixth-order polynomial yields an excellent fit for the action in the high temperature approximated potential. In a realistic model, the singlet extension of the Standard Model, this method performs satisfactorily across most of the parameter space after trimming the fitting data. This approach not only enhances the accuracy of phase transition calculations but also systematically reduces computation time and facilitates error estimation, particularly in models involving multiple scalar fields. Furthermore, we discussed the possible of using multiple neural networks to predict the action curve from model parameters.

hep-ph↗

arXiv2025

Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing

The design of artificial neural networks (ANNs) is inspired by the structure of the human brain, and in turn, ANNs offer a potential means to interpret and understand brain signals. Existing methods primarily align brain signals with stimulus signals using Mean Squared Error (MSE), which focuses only on local point-wise alignment and ignores global matching, leading to coarse interpretations and inaccuracies in brain signal decoding. In this paper, we address these issues through optimal transport (OT) and theoretically demonstrate why OT provides a more effective alignment strategy than MSE. Specifically, we construct a transport plan between brain voxel embeddings and image embeddings, enabling more precise matching. By controlling the amount of transport, we mitigate the influence of redundant information. We apply our alignment model directly to the Brain Captioning task by feeding brain signals into a large language model (LLM) instead of images. Our approach achieves state-of-the-art performance across ten evaluation metrics, surpassing the previous best method by an average of 6.11\% in single-subject training and 3.81\% in cross-subject training. Additionally, we have uncovered several insightful conclusions that align with existing brain research. We unveil the redundancy and synergy of brain information processing through region masking and data dimensionality reduction visualization experiments. We believe our approach paves the way for a more precise understanding of brain signals in the future. The code is available at https://github.com/NKUShaw/OT-Alignment4brain-to-image.

q-bio.NC↗

arXiv2025

LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation

Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. In this paper, we decompose the inference process into the encoding, denoising, and decoding stages, and observe that cache-based acceleration methods often lead to substantial memory surges in the latter two stages. To address this problem, we analyze the characteristics of inference across different stages and propose stage-specific strategies for reducing memory consumption: 1) Asynchronous Cache Swapping. 2) Feature chunk. 3) Slicing latents to decode. At the same time, we ensure that the time overhead introduced by these three strategies remains lower than the acceleration gains themselves. Compared with the baseline, our approach achieves faster inference speed and lower memory usage, while maintaining quality degradation within an acceptable range. The Code is available at https://github.com/NKUShaw/LightCache .

cs.CV↗

arXiv2025

EnvSDD: Benchmarking Environmental Sound Deepfake Detection

Audio generation systems now create very realistic soundscapes that can enhance media production, but also pose potential risks. Several studies have examined deepfakes in speech or singing voice. However, environmental sounds have different characteristics, which may make methods for detecting speech and singing deepfakes less effective for real-world sounds. In addition, existing datasets for environmental sound deepfake detection are limited in scale and audio types. To address this gap, we introduce EnvSDD, the first large-scale curated dataset designed for this task, consisting of 45.25 hours of real and 316.74 hours of fake audio. The test set includes diverse conditions to evaluate the generalizability, such as unseen generation models and unseen datasets. We also propose an audio deepfake detection system, based on a pre-trained audio foundation model. Results on EnvSDD show that our proposed system outperforms the state-of-the-art systems from speech and singing domains.

cs.SD↗

arXiv2025

LIMI: Less is More for Agency

We define Agency as the emergent capacity of AI systems to function as autonomous agents actively discovering problems, formulating hypotheses, and executing solutions through self-directed engagement with environments and tools. This fundamental capability marks the dawn of the Age of AI Agency, driven by a critical industry shift: the urgent need for AI systems that don't just think, but work. While current AI excels at reasoning and generating responses, industries demand autonomous agents that can execute tasks, operate tools, and drive real-world outcomes. As agentic intelligence becomes the defining characteristic separating cognitive systems from productive workers, efficiently cultivating machine autonomy becomes paramount. Current approaches assume that more data yields better agency, following traditional scaling laws from language modeling. We fundamentally challenge this paradigm. LIMI (Less Is More for Intelligent Agency) demonstrates that agency follows radically different development principles. Through strategic focus on collaborative software development and scientific research workflows, we show that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior. Using only 78 carefully designed training samples, LIMI achieves 73.5% on comprehensive agency benchmarks, dramatically outperforming state-of-the-art models: Kimi-K2-Instruct (24.1%), DeepSeek-V3.1 (11.9%), Qwen3-235B-A22B-Instruct (27.5%), and GLM-4.5 (45.1%). Most strikingly, LIMI demonstrates 53.7% improvement over models trained on 10,000 samples-achieving superior agentic intelligence with 128 times fewer samples. Our findings establish the Agency Efficiency Principle: machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations.

cs.AI↗

arXiv2025

AFT: An Exemplar-Free Class Incremental Learning Method for Environmental Sound Classification

As sounds carry rich information, environmental sound classification (ESC) is crucial for numerous applications such as rare wild animals detection. However, our world constantly changes, asking ESC models to adapt to new sounds periodically. The major challenge here is catastrophic forgetting, where models lose the ability to recognize old sounds when learning new ones. Many methods address this using replay-based continual learning. This could be impractical in scenarios such as data privacy concerns. Exemplar-free methods are commonly used but can distort old features, leading to worse performance. To overcome such limitations, we propose an Acoustic Feature Transformation (AFT) technique that aligns the temporal features of old classes to the new space, including a selectively compressed feature space. AFT mitigates the forgetting of old knowledge without retaining past data. We conducted experiments on two datasets, showing consistent improvements over baseline models with accuracy gains of 3.7\% to 3.9\%.

eess.AS↗

arXiv2025

Temporally Heterogeneous Graph Contrastive Learning for Multimodal Acoustic event Classification

Multimodal acoustic event classification plays a key role in audio-visual systems. Although combining audio and visual signals improves recognition, it is still difficult to align them over time and to reduce the effect of noise across modalities. Existing methods often treat audio and visual streams separately, fusing features later with contrastive or mutual information objectives. Recent advances explore multimodal graph learning, but most fail to distinguish between intra- and inter-modal temporal dependencies. To address this, we propose Temporally Heterogeneous Graph-based Contrastive Learning (THGCL). Our framework constructs a temporal graph for each event, where audio and video segments form nodes and their temporal links form edges. We introduce Gaussian processes for intra-modal smoothness, Hawkes processes for inter-modal decay, and contrastive learning to capture fine-grained relationships. Experiments on AudioSet show that THGCL achieves state-of-the-art performance.

cs.SD↗

arXiv2025

Vulnerability-Affected Versions Identification: How Far Are We?

Identifying which software versions are affected by a vulnerability is critical for patching, risk mitigation. Despite a growing body of tools, their real-world effectiveness remains unclear due to narrow evaluation scopes often limited to early SZZ variants, outdated techniques, and small or coarse-grained datasets. In this paper, we present the first comprehensive empirical study of vulnerability affected versions identification. We curate a high quality benchmark of 1,128 real-world C/C++ vulnerabilities and systematically evaluate 12 representative tools from both tracing and matching paradigms across four dimensions: effectiveness at both vulnerability and version levels, root causes of false positives and negatives, sensitivity to patch characteristics, and ensemble potential. Our findings reveal fundamental limitations: no tool exceeds 45.0% accuracy, with key challenges stemming from heuristic dependence, limited semantic reasoning, and rigid matching logic. Patch structures such as add-only and cross-file changes further hinder performance. Although ensemble strategies can improve results by up to 10.1%, overall accuracy remains below 60.0%, highlighting the need for fundamentally new approaches. Moreover, our study offers actionable insights to guide tool development, combination strategies, and future research in this critical area. Finally, we release the replicated code and benchmark on our website to encourage future contributions.

cs.SE↗

arXiv2025

Growth of Gravitational Wave Spectrum from Sound Waves in a Universe with Generic Expansion Rate

We derive a compact analytical expression for the growth factor $Υ$, which characterizes how the gravitational wave spectrum sourced by sound waves evolves in a universe with a generic expansion history. Assuming the dominant energy density scales as $ρ\propto a^{-3(1+w)}$, we obtain $Υ=\frac{2[1-y^{3(w-1)/2}]}{3(1-w)}$, where $y = a(t)/a(t_s)$ is the ratio of the scale factor at a later time $t$ to that at $t_s$ when gravitational wave production from sound waves starts. This general result reduces to known forms in radiation-and matter-dominated eras, thereby extending previous formulas to a broader class of cosmological backgrounds. The derivation assumes only that the source is stationary, making $Υ$ a universal factor that captures gravitational wave growth in various scenarios-not limited to phase transitions.

gr-qc↗

arXiv2025

The Bubble Wall Velocity in Local Thermal Equilibrium and Energy Budget with Full Effective Potential

We develop a framework based on the full one-loop finite-temperature effective potential model, within which the bubble wall velocity is calculated using the local thermal equilibrium (LTE) approximation, and the kinetic energy fraction $K$ is computed directly. In cosmological phase transitions, these quantities play a critical role in determining the resulting gravitational wave signals. Using the xSM as a benchmark model, we compute the peak gravitational wave spectra under different methods for determining the wall velocity and the kinetic energy fraction $K$, and compare these results to those obtained using the commonly employed bag model. Within the scanned parameter space, we find: (1) Deflagration is the most prevalent mode of fluid motion.(2) Gravitational wave spectra based on the full effective potential with LTE-derived wall velocity and integrated $K$ can differ significantly from those using the bag model with fitted $K$. In the deflagration regime, discrepancies reach up to 48\% in peak frequency and 90\% in amplitude.(3) The bag model provides a good approximation to the full equation of state in many cases. Notably, in deflagration scenarios with input wall velocity, the gravitational wave spectra obtained from the bag model more closely resemble the LTE-based results than those derived using the full potential with this input wall velocity.

hep-ph↗

arXiv2025

Closing the Visibility Gap: A Monitoring Framework for Verifiable Open RAN Operations

Open Radio Access Network (Open RAN) is reshaping mobile network architecture by promoting openness, disaggregation, and cross-vendor interoperability. However, this architectural flexibility introduces new security challenges, especially in deployments where multiple mobile network operators (MNOs) jointly operate shared components. Existing Zero Trust Architectures (ZTA) in O-RAN, as defined by governmental and industry standards, implicitly assume that authenticated components will comply with operational policies. However, this assumption creates a critical blind spot: misconfigured or compromised components can silently violate policies, misuse resources, or corrupt downstream processes (e.g., ML-based RIC xApps). To address this critical gap, we propose a monitoring framework for low-trust O-RAN environments that proactively verifies configuration state and control behavior against tenant-defined policies. Our system provides scalable, verifiable oversight to enhance transparency and trust in O-RAN operations. We implement and evaluate the framework using standardized O-RAN configurations, with total processing latency of approximately 200 ms, demonstrating its efficiency and practicality for timely policy enforcement and compliance auditing in multi-MNO deployments.

cs.NI↗

arXiv2025

DG-SED: Domain Generalization for Sound Event Detection with Heterogeneous Training Data

This work explores domain generalization (DG) for sound event detection (SED), advancing adaptability to real-world scenarios. Our approach employs a mean-teacher framework with domain generalization named DG-SED to integrate heterogeneous training data while preserving the SED model performance across the datasets. Specifically, we first apply mixstyle to the frequency dimension to adapt the mel-spectrograms from different domains. Next, we use the adaptive residual normalization method to generalize features across multiple domains by applying instance normalization in the frequency dimension. Lastly, we use the sound event bounding boxes method for post-processing. We evaluate the proposed approach DG-SED on the DCASE 2024 Challenge Task 4, measuring PSDS on the DESED dataset and macro-average pAUC on the MAESTRO dataset. The results indicate that the proposed DG-SED method improves both PSDS and macro-average pAUC compared to the baselines. The code will be released in due course.

eess.AS↗

arXiv2025

Efficient Knowledge Graph Unlearning with Zeroth-order Information

Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machine unlearning methods have been proposed. In this paper, we firstly present an efficient knowledge graph (KG) unlearning algorithm. We remark that KG unlearning is nontrivial due to the distinctive structure of KG and the semantic relations between entities. Also, unlearning by estimating the influence of removed components incurs significant computational overhead when applied to large-scale knowledge graphs. To this end, we define an influence function for KG unlearning and propose to approximate the model's sensitivity without expensive computation of first-order and second-order derivatives for parameter updates. Specifically, we use Taylor expansion to estimate the parameter changes caused by data removal. Given that the first-order gradients and second-order derivatives dominate the computational load, we use the Fisher matrices and zeroth-order optimization to approximate the inverse-Hessian vector product without constructing the computational graphs. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art graph unlearning baselines significantly in terms of unlearning efficiency and unlearning quality. Our code is released at https://github.com/NKUShaw/ZOWFKGIF.

cs.LG↗