arXiv subjects

Yuanyuan Yang

Publications and source records attributed to Yuanyuan Yang.

At least 19 recordsLinked to original sources

arXiv2026

QuoteBench: How Matched Scores Can Hide Command-Path Failures

LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply's raw-path outcome, so any recovery under a disclosed boundary must come from the model changing its generation. Across eight same-window configurations, replaying the same reply through the added parser lowers success by 55.4 to 73.2 percentage points; disclosure recovers 30.4 to 60.7 points for six configurations, and zero or slightly negative for the other two. Raw generation is nearly saturated at the frontier; boundary adaptation is what still separates models. GPT-5.6-sol's matched gap of -3.6 points hides -64.3 points of damage and +60.7 points of compensation. The deployment configuration reorders models: one reversal among 26 comparable pairs is unambiguous and four more sit on single-task margins. Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state validator rather than treat a matched score as an intrinsic model property.

cs.AI↗

arXiv2026

Beyond the PPAD hardness of Auto-bidding Auctions

Computing certain autobidding equilibria is PPAD complete in the worst case. Yet such instances rarely arise in practice, where advertisers running simple, decentralized learning strategies usually converge quickly. We show there is no contradiction: the hardness requires atomicity and vanishes once the value distribution is nonatomic, as it is in real world markets. To bridge worst case hardness and practical convergence, we introduce diffuse analysis, a beyond worst case framework that studies equilibrium computation when bidder values are drawn from general nonatomic distributions. Under this framework, the autobidding equilibrium becomes a separately monotone generalized Nash equilibrium (GNE). For this GNE, we give the first solver with last iterate linear convergence. Thus, the equilibrium has polynomial diffuse complexity, matching the convergence observed in real-world markets. Concretely, our framework subsumes the budget pacing and the throttling equilibrium as special cases when the payment rule is a convex combination of first and second price.

cs.GT↗

arXiv2026

Decoupled interband pairing in a bilayer iron-based superconductor evidenced by ultrahigh-resolution ARPES

We present direct experimental evidence of a weakly coupled multiband superconducting state in the bilayer iron-based superconductor ACa$_2$Fe$_4$As$_4$F$_2$ (A = K, Cs) via ultrahigh-resolution angle-resolved photoemission spectroscopy (ARPES). Remarkably, the K-containing compound exhibits two distinct transition temperatures, corresponding to two separate sets of bilayer-split bands, as evidenced by temperature-dependent superconducting gap and spectral weight near the Fermi energy, while its Cs counterpart displays conventional single transition behavior. These experimental observations are well described by the weakly coupled two-band model of Eilenberger theory, which identifies suppressed interband pairing interactions between the bilayer-split bands as the key mechanism. By exploring quantum phenomena in the weak-coupling limit within a multiband system, our findings pave the way for engineering exotic superconductivity via band-selective pairing control.

cond-mat.supr-con↗

arXiv2025

T-TAMER: Provably Taming Trade-offs in ML Serving

As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives. Multi-model serving further complicates these trade-offs; for example, in cascaded models, each early-exit decision balances latency reduction against potential accuracy loss. Despite the pervasiveness and importance of such trade-offs, current strategies remain largely heuristic and case-specific, limiting both their theoretical guarantees and general applicability. We present a general framework, T-Tamer, which formalizes this setting as a multi-stage decision process, where the objective is to determine both when to exit and which model to consult. Our main result shows that recall (i.e., the ability to revisit earlier models) is both necessary and sufficient for achieving provable performance guarantees. In particular, we prove that strategies without recall cannot obtain any constant-factor approximation to the optimal trade-off, whereas recall-based strategies provably attain the optimal trade-off in polynomial time. We validate our analysis through experiments on synthetic datasets and early-exit workloads for vision and NLP benchmarks. The results show that recall-based strategies consistently yield efficient accuracy-latency trade-offs. We hope this work provides a principled foundation for bridging heuristic practice with theoretical guarantees in the design of early-exit and cascaded models.

cs.LG↗

arXiv2025

MeV cosmic-ray electrons modify the TeV pair-beam plasma instability

Relativistic pair beams created in the intergalactic medium (IGM) by TeV gamma rays from blazars are expected to produce a detectable GeV-scale electromagnetic cascade, but the cascade component is absent in the spectra of many hard-spectrum TeV-emitting blazars. One common explanation is that weak intergalactic magnetic fields deflect the electron-positron pairs away from our line of sight. An alternative possibility is that electrostatic beam-plasma instabilities drain the energy of these pairs before a cascade can develop. Recent studies have shown that beam scattering by oblique electrostatic modes leads to minimal energy loss. But these modes might be suppressed by linear Landau damping (LLD) due to MeV-scale cosmic-ray electrons in the IGM. In this work, we explore the impact of LLD on the energy-loss efficiency of plasma instabilities in pair beams associated with 1ES 0229+200. We find that LLD effectively suppresses oblique electrostatic modes, while quasi-parallel ones grow to larger amplitudes. In this way, LLD enhances the energy-loss efficiency of the instability by more than an order of magnitude.

astro-ph.HE↗

arXiv2025

The Security Overview and Analysis of 3GPP 5G MAC CE

To more effectively control and allocate network resources, MAC CE has been introduced into the network protocol, which is a type of control signaling located in the MAC layer. Since MAC CE lacks encryption and integrity protection mechanisms provided by PDCP, the control signaling carried by MAC CE is vulnerable to interception or tampering by attackers during resource scheduling and allocation. Currently, the 3GPP has analyzed the security risks of Layer 1/Layer 2 Triggered Mobility (LTM), where handover signaling sent to the UE via MAC CE by the network can lead to privacy leaks and network attacks. However, in addition to LTM, there may be other potential security vulnerabilities in other protocol procedures. Therefore, this paper explores the security threats to MAC CE and the corresponding protection mechanisms. The research is expected to support the 3GPP's study of MAC CE and be integrated with the security research of lower-layer protocols, thereby enhancing the security and reliability of the entire communication system.

cs.CR↗

arXiv2025

Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering

Indoor localization is critical for IoT applications, yet challenges such as non-Gaussian noise, environmental interference, and measurement outliers hinder the robustness of traditional methods. Existing approaches, including Kalman filtering and its variants, often rely on Gaussian assumptions or static thresholds, limiting adaptability in dynamic environments. This paper proposes a hierarchical robust framework integrating Variational Bayesian (VB) parameter learning, Huber M-estimation, and Conformal Outlier Detection (COD) to address these limitations. First, VB inference jointly estimates state and noise parameters, adapting to time-varying uncertainties. Second, Huber-based robust filtering suppresses mild outliers while preserving Gaussian efficiency. Third, COD provides statistical guarantees for outlier detection via dynamically calibrated thresholds, ensuring a user-controlled false alarm rate. Theoretically, we prove the Semi-positive Definiteness of Huber-based Kalman filtering covariance and the coverage of sliding window conformal prediction. Experiments on geomagnetic fingerprint datasets demonstrate significant improvements: fingerprint matching accuracy increases from 81.25% to 93.75%, and positioning errors decrease from 0.62-6.87 m to 0.03-0.35 m. Comparative studies further validate the framework's robustness, showing consistent performance gains under non-Gaussian noise and outlier conditions.

eess.SY↗

arXiv2025

Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten

Free play is a fundamental aspect of early childhood education, supporting children's cognitive, social, emotional, and motor development. However, assessing children's development during free play poses significant challenges due to the unstructured and spontaneous nature of the activity. Traditional assessment methods often rely on direct observations by teachers, parents, or researchers, which may fail to capture comprehensive insights from free play and provide timely feedback to educators. This study proposes an innovative approach combining Large Language Models (LLMs) with learning analytics to analyze children's self-narratives of their play experiences. The LLM identifies developmental abilities, while performance scores across different play settings are calculated using learning analytics techniques. We collected 2,224 play narratives from 29 children in a kindergarten, covering four distinct play areas over one semester. According to the evaluation results from eight professionals, the LLM-based approach achieved high accuracy in identifying cognitive, motor, and social abilities, with accuracy exceeding 90% in most domains. Moreover, significant differences in developmental outcomes were observed across play settings, highlighting each area's unique contributions to specific abilities. These findings confirm that the proposed approach is effective in identifying children's development across various free play settings. This study demonstrates the potential of integrating LLMs and learning analytics to provide child-centered insights into developmental trajectories, offering educators valuable data to support personalized learning and enhance early childhood education practices.

cs.AI↗

arXiv2025

IAEmu: Learning Galaxy Intrinsic Alignment Correlations

The intrinsic alignments (IA) of galaxies, a key contaminant in weak lensing analyses, arise from correlations in galaxy shapes driven by tidal interactions and galaxy formation processes. Accurate IA modeling is essential for robust cosmological inference, but current approaches rely on perturbative methods that break down on nonlinear scales or on expensive simulations. We introduce IAEmu, a neural network-based emulator that predicts the galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation functions and their uncertainties using mock catalogs based on the halo occupation distribution (HOD) framework. Compared to simulations, IAEmu achieves ~3% average error for $\xi$ and ~5% for $\omega$, while capturing the stochasticity of $\eta$ without overfitting. The emulator provides both aleatoric and epistemic uncertainties, helping identify regions where predictions may be less reliable. We also demonstrate generalization to non-HOD alignment signals by fitting to IllustrisTNG hydrodynamical simulation data. As a fully differentiable neural network, IAEmu enables $\sim$10,000$\times$ speed-ups in mapping HOD parameters to correlation functions on GPUs, compared to CPU-based simulations. This acceleration facilitates inverse modeling via gradient-based sampling, making IAEmu a powerful surrogate model for galaxy bias and IA studies with direct applications to Stage IV weak lensing surveys.

astro-ph.CO↗

arXiv2025

Markovian Pandora's box

In this paper, we study the Markovian Pandora's Box Problem, where decisions are governed by both order constraints and Markovianly correlated rewards, structured within a shared directed acyclic graph. To the best of our knowledge, previous work has not incorporated Markovian dependencies in this setting. This framework is particularly relevant to applications such as data or computation driven algorithm design, where exploration of future models incurs cost. We present optimal fully adaptive strategies where the associated graph forms a forest. Under static transition, we introduce a strategy that achieves a near optimal expected payoff in multi line graphs and a 1/2 approximation in forest-structured graphs. Notably, this algorithm provides a significant speedup over the exact solution, with the improvement becoming more pronounced as the graph size increases. Our findings deepen the understanding of sequential exploration under Markovian correlations in graph-based decision-making.

cs.GT↗

arXiv2024

Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders

The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insufficient supervision. To address these issues at some extend, we propose a multi-stage graph learning framework which incorporates 1) pretrain stage : self-supervised graph learning on insufficient supervision of the fmri data 2) fine-tune stage : supervised graph learning for brain disorder diagnosis. Experiment results on three datasets, Autism Brain Imaging Data Exchange ABIDE I, ABIDE II and ADHD with AAL1,demonstrating the superiority and generalizability of the proposed framework compared to the state of art of models.(ranging from 0.7330 to 0.9321,0.7209 to 0.9021,0.6338 to 0.6699)

q-bio.NC↗

arXiv2024

Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite

Evaluating the performance of Simultaneous Localization and Mapping (SLAM) algorithms is essential for scientists and users of robotic systems alike. But there are a multitude of different permutations of possible options of hardware setups and algorithm configurations, as well as different datasets and algorithms, such that it was previously infeasible to thoroughly compare SLAM systems against the full state of the art. To solve that we present the SLAM Hive Benchmarking Suite, which is able to analyze SLAM algorithms in 1000's of mapping runs, through its utilization of container technology and deployment in the cloud. This paper presents the architecture and open source implementation of SLAM Hive and compares it to existing efforts on SLAM evaluation. We perform mapping runs with popular visual, RGBD and LiDAR based SLAM algorithms against commonly used datasets and show how SLAM Hive can be used to conveniently analyze the results against various aspects. Through this we envision that SLAM Hive can become an essential tool for proper comparisons and evaluations of SLAM algorithms and thus drive the scientific development in the research on SLAM. The open source software as well as a demo to show the live analysis of 1000's of mapping runs can be found on our SLAM Hive website.

cs.RO↗

arXiv2024

ShanghaiTech Mapping Robot is All You Need: Robot System for Collecting Universal Ground Vehicle Datasets

This paper presents the ShanghaiTech Mapping Robot, a state-of-the-art unmanned ground vehicle (UGV) designed for collecting comprehensive multi-sensor datasets to support research in robotics, Simultaneous Localization and Mapping (SLAM), computer vision, and autonomous driving. The robot is equipped with a wide array of sensors including RGB cameras, RGB-D cameras, event-based cameras, IR cameras, LiDARs, mmWave radars, IMUs, ultrasonic range finders, and a GNSS RTK receiver. The sensor suite is integrated onto a specially designed mechanical structure with a centralized power system and a synchronization mechanism to ensure spatial and temporal alignment of the sensor data. A 16-node on-board computing cluster handles sensor control, data collection, and storage. We describe the hardware and software architecture of the robot in detail and discuss the calibration procedures for the various sensors and investigate the interference for LiDAR and RGB-D sensors. The capabilities of the platform are demonstrated through an extensive outdoor dataset collected in a diverse campus environment. Experiments with two LiDAR-based and two RGB-based SLAM approaches showcase the potential of the dataset to support development and benchmarking for robotics. To facilitate research, we make the dataset publicly available along with the associated robot sensor calibration data: https://slam-hive.net/wiki/ShanghaiTech_Datasets

cs.RO↗

arXiv2024

A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction

Accurate prediction of financial time series is a key concern for market economy makers and investors. The article selects online store sales and Australian beer sales as representatives of non-stationary, trending, and seasonal financial time series, and constructs a new SGVMD-ARIMA combination model in a non-linear combination way to predict financial time series. The ARIMA model, LSTM model, and other classic decomposition prediction models are used as control models to compare the accuracy of different models. The empirical results indicate that the constructed combination prediction model has universal advantages over the single prediction model and linear combination prediction model of the control group. Within the prediction interval, our proposed combination model has improved advantages over traditional decomposition prediction control group models.

eess.SP↗

arXiv2024

Learning Galaxy Intrinsic Alignment Correlations

The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs. We find strong Pearson correlation values with the model across all three correlation functions and further predict aleatoric uncertainties through a mean-variance estimation training procedure. $\xi(r)$ predictions are generally accurate to $\leq10\%$. Our model also successfully captures the underlying signal of the noisier correlations $\omega(r)$ and $\eta(r)$, although with a lower average accuracy. We find that the model performance is inhibited by the stochasticity of the data, and will benefit from correlations averaged over multiple data realizations. Our code will be made open source upon journal publication.

astro-ph.CO↗

arXiv2024

On the robustness of double-word addition algorithms

We demonstrate that, even when there are moderate overlaps in the inputs of sloppy or accurate double-word addition algorithms in the QD library, these algorithms still guarantee error bounds of $O(u^2(|a|+|b|))$ in faithful rounding. Furthermore, the accurate algorithm can achieve a relative error bound of $O(u^2)$ in the presence of moderate overlaps in the inputs when rounding function is round-to-nearest. The relative error bound also holds in directed rounding, but certain additional conditions are required. Consequently, in double-word multiplication and addition operations, we can safely omit the normalization step of double-word multiplication and replace the accurate addition algorithm with the sloppy one. Numerical experiments confirm that this approach nearly doubles the performance of double-word multiplication and addition operations, with negligible precision costs. Moreover, in directed rounding mode, the signs of the errors of the two algorithms are consistent with the rounding direction, even in the presence of input overlap. This allows us to avoid changing the rounding mode in interval arithmetic. We also prove that the relative error bound of the sloppy addition algorithm exceeds $3u^2$ if and only if the input meets the condition of Sterbenz's Lemma when rounding to nearest. These findings suggest that the two addition algorithms are more robust than previously believed.

math.NA↗

arXiv2023

Compton scattering of electrons in the intergalactic medium

This paper investigates the distribution and implications of cosmic ray electrons within the intergalactic medium (IGM). Utilizing a synthesis model of the extragalactic background, we evolve the spectrum of Compton-included cosmic rays. The energy density distribution of cosmic ray electrons peaks at redshift $z \approx2$, and peaks in the $\sim$MeV range. The fractional contribution of cosmic ray pressure to the general IGM pressure progressively increases toward lower redshift. At mean density, the ratio of cosmic ray electron to thermal pressure in the IGM $ P_{\rm CRe} / P_{\rm th}$ is 0.3% at $z=2$, rising to 1.0% at $z=1$, and 1.8% at $z=0.1$ (considering only the cosmic rays produced locally by Compton scattering). We compute the linear Landau damping rate of plasma oscillations in the IGM caused by the $\sim$MeV cosmic ray electrons, and find it to be of order $\sim 10^{-6}\,\rm s^{-1}$ for wavenumbers $1.2\lesssim ck/\omega_{\rm p}\lesssim 5$ at $z=2$ and mean density (where $\omega_{\rm p}$ is the plasma frequency). This strongly affects the fate of TeV $e^+e^-$ pair beams produced by blazars, which are potentially unstable to oblique instabilities involving plasma oscillations with wavenumber $ck/\omega_{\rm p}\approx\sec\theta$ ($\theta$ being the angle between the beam and wave vector). Linear Landau damping is at least thousands of times faster than either pair beam instability growth or collisional effects; it thus turns off the pair beam instability except for modes with very small $\theta$ ($ck/\omega_{\rm p}\rightarrow 1$, where linear Landau damping is kinematically suppressed). This leaves open the question of whether the pair beam instability is turned off entirely, or can still proceed via the small-$\theta$ modes.

astro-ph.HE↗

arXiv2023

Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training

In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem~(\cite{prr89}): Given two sets $A \subset \{-1,+1\}^d$ and $B \subset \{-1,+1\}^d$ with $|A|=|B| = n$, if there are exact $k$ pairs whose inner product passes a certain threshold, i.e., $\{(a_1, b_1), \cdots, (a_k, b_k)\} \subset A \times B$ such that $\forall i \in [k], \langle a_i,b_i \rangle \geq ρ\cdot d$, for a threshold $ρ\in (0,1)$, the goal is to identify those $k$ heavy inner products. We provide an algorithm that runs in $O(n^{2 ω/ 3+ o(1)})$ time to find the $k$ inner product pairs that surpass $ρ\cdot d$ threshold with high probability, where $ω$ is the current matrix multiplication exponent. By solving this problem, our method speed up the training of neural networks with ReLU activation function.

cs.LG↗