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Xin Liu

Publications and source records attributed to Xin Liu.

At least 109 records · Page 6Linked to original sources
arXiv2026

Experimental demonstration of scalable quantum blockchain with exponentially superior quantum communication complexity

To secure modern distributed digital infrastructures, quantum blockchains exploit quantum resources to achieve information-theoretic security and surpass the classical one-third fault-tolerance bound. However, existing high-fault-tolerant protocols face a fundamental scalability challenge: the blockchain trilemma imposes either exponential communication complexity or experimentally demanding multipartite entanglement. Here, we experimentally demonstrate a scalable quantum blockchain protocol based on weak coherent states that achieves an exponential reduction in quantum communication complexity. The protocol employs a circular quantum Byzantine agreement mechanism that preserves information-theoretic security while avoiding multipartite entanglement. We implement this protocol on a photonic integrated circuit platform, realizing a six-node network over commercially available telecommunication infrastructure. Compared with previous schemes, the protocol requires less than 4% of the quantum communication resources. Leveraging this advantage, we further demonstrate a quantum-secured token exchange application achieving a throughput of 805.3 transactions per second with zero failures. These results establish a practical pathway toward scalable quantum blockchain.

quant-ph↗
arXiv2026

HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning

RLVR has become a standard recipe for training LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency hinges on which prompts are sampled and when. In practice, prompt pools are often static or only weakly coupled to policy progress, so uniform sampling fails to track the moving capability frontier and wastes rollouts on regions that are already solved or still unreachable. Prior methods improve efficiency via filtering, curricula, adaptive rollout allocation, or teacher guidance, but they often assume a fixed pool, which does not support stable on-policy pool growth, or they introduce additional teacher cost and latency. In this work, we propose HeaPA (Heap Sampling and On-Policy Query Augmentation), which maintains a bounded, evolving pool, tracks the frontier with heap-based boundary sampling, grows the pool via on-policy augmentation under lightweight asynchronous validation, and stabilizes correlated queries via topology-aware pool statistics re-estimation and controlled reinsertion. Across two training corpora, two training recipes, and seven benchmarks, HeaPA consistently improves accuracy and reaches target performance with fewer computations at comparable wall-clock time. Analyses attribute the gains to frontier-focused sampling and on-policy pool growth, with more pronounced improvements at mid-to-large model scales. Our training code is publicly available at https://github.com/horizon-llm/HeaPA.

cs.LG↗
arXiv2026

A Smoothed GMM for Dynamic Quantile Preferences Estimation

This paper suggests methods for estimation of the $τ$-quantile, $τ\in (0,1)$, as a parameter along with the other finite-dimensional parameters identified by general conditional quantile restrictions. We employ a generalized method of moments framework allowing for non-linearities and dependent data, where moment functions are smoothed to aid both computation and tractability. Consistency and asymptotic normality of the estimators are established under weak assumptions. Simulations illustrate the finite-sample properties of the methods. An empirical application using a quantile intertemporal consumption model with multiple assets estimates the risk attitude, which is captured by $τ$, together with the elasticity of intertemporal substitution.

econ.GN↗
arXiv2026

Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillance. Current multimodal FAS methods often struggle with effective generalization, mainly due to modality-specific biases and domain shifts. To address these challenges, we introduce the \textbf{M}ulti\textbf{m}odal \textbf{D}enoising and \textbf{A}lignment (\textbf{MMDA}) framework. By leveraging the zero-shot generalization capability of CLIP, the MMDA framework effectively suppresses noise in multimodal data through denoising and alignment mechanisms, thereby significantly enhancing the generalization performance of cross-modal alignment. The \textbf{M}odality-\textbf{D}omain Joint \textbf{D}ifferential \textbf{A}ttention (\textbf{MD2A}) module in MMDA concurrently mitigates the impacts of domain and modality noise by refining the attention mechanism based on extracted common noise features. Furthermore, the \textbf{R}epresentation \textbf{S}pace \textbf{S}oft (\textbf{RS2}) Alignment strategy utilizes the pre-trained CLIP model to align multi-domain multimodal data into a generalized representation space in a flexible manner, preserving intricate representations and enhancing the model's adaptability to various unseen conditions. We also design a \textbf{U}-shaped \textbf{D}ual \textbf{S}pace \textbf{A}daptation (\textbf{U-DSA}) module to enhance the adaptability of representations while maintaining generalization performance. These improvements not only enhance the framework's generalization capabilities but also boost its ability to represent complex representations. Our experimental results on four benchmark datasets under different evaluation protocols demonstrate that the MMDA framework outperforms existing state-of-the-art methods in terms of cross-domain generalization and multimodal detection accuracy. The code will be released soon.

cs.CV↗
arXiv2026

LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering

CI/CD workflows have become executable operational policy: they decide what gets built, tested, released, and deployed, and they mediate how maintainers interact with delivery infrastructure. That makes them an important measurement point for cyber-systems engineering. Recent large language model (LLM) work shows that workflow stages can be recognized directly from configuration files, but stage labels alone do not tell us whether a workflow is brittle, unusual for its ecosystem, or worth revising first. We present an LLM-based CI/CD analysis pipeline that combines repository enrichment, anti-pattern detection, stage mining, and recommendation generation over a large GitHub corpus. Starting from 59,550 repositories with at least 1,000 stars, we identify 34,225 projects with CI/CD and collect 127,559 configuration files. Across 75,201 analyzed workflows, the anti-pattern detector reports 434,769 findings, dominated by reliability and maintainability issues. Across 59,906 configurations, stage usage differs significantly by language ($χ^2 = 4168.88$, $p < 0.001$, Cramer's $V = 0.063$), and domain analysis shows distinct operational profiles, including higher release and cache usage in mobile projects. For repository-level recommendation generation, few-shot prompting performs best overall, averaging 8.25 recommendations per repository with 96.1% YAML-valid snippets. Taken together, the results argue for CI/CD observability that combines diagnosis, context, and human review rather than treating workflow mining as a stage-classification problem alone.

cs.SE↗
arXiv2026

Quantum Hashing via Constrained Rydberg Many-Body Dynamics

In this Letter, we show that constrained many-body dynamics in Rydberg atom arrays naturally gives rise to a quantum hashing mechanism. By encoding ternary strings into deterministic trajectories in the state space, the classical information space is mapped onto a quantum state ensemble in the Hilbert space with an induced geometric structure. Statistical analysis reveals that this ensemble exhibits high probability near-orthogonality, random-like distribution, and broad geometric coverage. These geometric features naturally give rise to the essential cryptographic properties of quantum hashing, including low collision probability, one-wayness, tamper sensitivity, and privacy preservation. Our results demonstrate that the cryptographic functionality of quantum hashing need not rely on deliberately engineered algorithms, but can instead emerge naturally from constrained many-body dynamics, identifying quantum dynamics itself as a physical resource for cryptographic information processing.

quant-ph↗
arXiv2026

AdaStop: Cost-Aware Early Stopping for DNN Test Selection

Existing methods for testing deep neural networks (DNNs) primarily prioritize test inputs likely to reveal model faults under a fixed labeling budget. In practice, choosing that budget is difficult: too little testing misses failures, while too much incurs unnecessary labeling costs. This work studies the stopping problem in DNN testing. We formulate testing as a cost--benefit decision process in which labeling an input incurs cost $c$ and discovering a fault yields value $v$. Based on this formulation, we introduce \textit{AdaStop}, a framework that estimates the marginal fault discovery rate during testing and stops labeling when the estimated rate falls below the threshold $τ= c/v$. Experiments across multiple datasets, architectures, and selection strategies show that $65$--$84\%$ of faults can be discovered using only $9$--$31\%$ of the labeling budget.

cs.LG↗
arXiv2026

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning

Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming large language models (LLMs) into robust reasoners. We argue that this combined success is driven by compositional generalization, which we formalize through a hierarchical latent selection model. In this framework, reasoning traces are generated by a cascade of discrete latent selection variables corresponding to reusable atomic modules, including both skills (local operations) and routing mechanisms (how intermediate information is selected, reused, and composed). Within this model, we theoretically show that SFT and RL play asymmetric, complementary roles: SFT supplies the raw module materials in compositional traces, and RL decomposes those traces to identify the latent atomic modules and enable compositional generalization. We design controlled experiments to validate this theory. Our results demonstrate that RL can extract atomic modules from compound traces supplied by SFT and recombine them to solve new configurations. Moreover, we find that training on compound traces yields stronger generalization than training on isolated atomic modules. Finally, we investigate the relationship between SFT and RL data and identify an effective protocol in which SFT ensures coverage of all atomic modules through compositional traces, while RL focuses on novel compositions outside the SFT support to drive exploration.

cs.LG↗
arXiv2026

Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization

In this paper, we consider the nonsmooth nonconvex decentralized optimization problem, where inter-agent communication is compressed. We propose a general framework that unifies various decentralized stochastic subgradient-type methods with unbiased compression and contractive compression with error compensation. By relating the consensus-error iterates and the averaged iterates to the trajectories of continuous-time differential inclusions, we establish global convergence for all methods encompassed by our framework when the objective functions are nonsmooth and lack Clarke regularity. Based on our framework, we further develop several compression-based methods, including decentralized stochastic subgradient methods utilizing sign-based regularization and gradient-tracking momentum. Preliminary numerical experiments empirically support our theoretical results and highlight the communication-accuracy trade-off of the newly developed methods.

math.OC↗
arXiv2026

Quantifying angular momentum of coherently driven circular phonons

The use of intense terahertz (THz) pulses to manipulate low-energy excitations offers a powerful approach for ultrafast control of electronic and magnetic properties in materials. Theory suggests that circular ionic motions driven by THz fields carry angular momentum, potentially generating internal magnetic fields. Recent experiments in nonmagnetic SrTiO3 (STO) have hinted at such THz-induced fields, but their origin remains debated. Here, we employ ultrafast x-ray diffraction to resolve the time-dependent ionic trajectories in STO following excitation by circularly polarized THz pulses. Our analysis reveals that oxygen ions, despite their lower mass, contribute around 90% of the phonon angular momentum. The resulting imbalance between the negatively and positively charged ions provides a clear explanation for the mechanism behind induced magnetism in STO. This work further provides the first quantitative measurement of circular ionic motions and their angular momentum and establishes a general methodology for the investigation of angular momentum transfer in solids, paving the way for new strategies to control topological phonon transport and phonon-driven magnetism in quantum materials.

cond-mat.mtrl-sci↗
arXiv2026

Physical Characteristics of the Asteroid (469219) Kamo'oalewa as a target of the Chinese Tianwen-2 mission

The Near-earth asteroid (469219) Kamo'oalewa, a quasi-satellite of the Earth, is going to be observed in site and sampled by the Chinese space mission Tianwen-2 in near future. Here. we analyze its photometric and spectroscopic data to figure out its basic physical properties, which are very important for the sample return task of the Tianwen-2 mission. With photometry inversion methods, we derived a pole $(276^{o}.79, -21^{o}.43)$ with a spin period of 28.4517 minutes and a slightly flat convex shape. The estimated photometry slope of $0.998 mag/rad$ implies a large albedo of the Kamo'oalewa, i.e. S-type. Using the estimated absolute magnitude of $24.98$ mag, its size could be 27.4m assuming a typical albedo of S-type asteroids. The taxonomy analysis with a constructed ANN tool also supports that the Kamo'oalewa should belong to S-type asreroids, it may be a strong weathering fragment of an A-type or Q-type asteroid. Using derived pole, size and shape information of the target, we estimated its thermal inertia as $163.0 Jm^{-2}K^{-1}s^{-1/2}$ based on the new derived Yarkovski draft $A_2=-13.29349563\times10^{-14}au/day^2$, which means the target has a surface of mixture of grains and small bounds, like the surface of asteroid Bennu.

astro-ph.EP↗
arXiv2026

Slay the Shear: A Unified Statistical Framework for Weak Gravitational Lensing Shear Estimation

Weak gravitational lensing shear measurements are fundamentally limited by shape noise arising from the intrinsic diversity of galaxy morphologies. Upcoming surveys such as Rubin/LSST, Euclid, and Roman demand more flexible, statistically optimal approaches that can fully exploit high-dimensional image information. In this work, we develop a unified theoretical framework for shear estimation that connects classical response-based methods, shape noise, and modern machine-learning estimators through the concept of the score function -- the gradient of the image likelihood with respect to shear. We show that, for a general spin-2 ellipticity definition, the ensemble shear response corresponds to an inner product between the estimator and the score function, and that the score provides the minimum-variance unbiased shear estimator. By incorporating response into the classical inverse-variance weight, we prove that the response-weighted inverse-variance weight is a general shape-noise-minimizing weight, independent of the intrinsic shape distribution. Furthermore, we propose Response-weighted Denoising Score Matching (RDSM) that exploits the remaining structure to reduce shape noise by ${\sim}17.5\%$ relative to moment-based methods at LSST 10-year depth while maintaining a multiplicative shear estimation bias below $2\times 10^{-3}$. Our result clarifies the optimality of existing calibration techniques while revealing a principled pathway for constructing improved estimators via nonlinear shape transformations and learned representations.

astro-ph.CO↗
arXiv2026

Chemical and optical control of chiral-domain dynamics in 1T-TaS$_2$

Optical control of symmetry-breaking quantum phases is often constrained when one domain is strongly favored in equilibrium. This limitation is exemplified by the chiral charge-density-wave (CDW) order in 1T-TaS$_2$, where pristine samples predominantly select a single chirality. Here we show that free-energy landscape engineering through Ti substitution enables a distinct nonthermal pathway for ultrafast chiral-domain redistribution. Ti doping stabilizes coexisting chiral domains and tunes their relative stability, allowing femtosecond excitation to drive an asymmetric and anisotropic redistribution from the dominant toward the minority chirality. The subpicosecond response follows a $\sim2$ THz amplitude mode and is consistent with a phonon-assisted pathway involving transient domain-wall configurations. Our results establish free-energy landscape engineering as a strategy for selecting nonequilibrium transition pathways.

cond-mat.str-el↗
arXiv2026

Source coherence orchestrates nonlinear random wave revivals

We demonstrate that the Talbot length of periodic wave packets, long believed to be solely determined by their periodicity, is strongly affected by the source coherence in the nonlinear propagation regime. We reveal that reducing the source coherence -- and consequently the speckle size of a periodic field -- shortens the Talbot length and significantly improves the quality of the recurrent Talbot images, despite a fixed source periodicity. This effect arises from coherence-mediated nonlinear mode coupling, which alters a phase synchronization condition undergirding the wave packet revivals. Our findings expose a hidden role of coherence in governing Talbot revivals of random waves in the nonlinear regime, crucially informing the understanding and application of the Talbot effect in realistic media.

physics.optics↗
arXiv2026

Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails

Large language models (LLMs) achieve strong performance across many tasks, but their high computational cost limits deployment in resource-constrained environments. Knowledge Distillation (KD) offers a practical solution by transferring knowledge from a teacher model of a larger size to a smaller student model. While prior work has mainly examined task-specific or small-scale settings, the post-training stage for building general instruction-following models has received limited attention. In this paper, we conduct a systematic study of KD in post-training using the large-scale Tulu 3 dataset. We find that KD outperforms supervised fine-tuning (SFT) in low-data regimes, but its advantage diminishes as more training data is added. Distilling from a stronger instruction-tuned teacher restores substantial gains even with abundant data, indicating that KD remains effective when the teacher provides knowledge that the student cannot easily acquire from the training data alone. We further study domain-specific, low-resource scenarios and propose a two-stage KD strategy that leverages synthetic teacher-labeled data followed by refinement on human annotations. This method consistently improves student performance, providing practical guidance for building compact models in data-scarce environments.

cs.CL↗
arXiv2026

An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data

Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We introduce CoDaS, an AI co-data-scientist that integrates multi-agent hypothesis generation, deterministic statistical analysis, adversarial validation and literature-grounded interpretation under human oversight. Across three wearable cohorts comprising 9,279 participant-observations, CoDaS prioritized candidate associations for mental-health and metabolic endpoints after internal checks for replication, stability, robustness and leakage. The system identified related circadian-instability signals associated with depression, including sleep-duration variability in DWB ($ρ$ = 0.252, $p$ < 0.001) and sleep-onset variability in GLOBEM ($ρ$ = 0.126, $p$ < 0.001), and derived a wearable cardiovascular-fitness index associated with insulin resistance (steps/resting heart rate; $ρ$ = -0.374, $p$ < 0.001). Adding these features to demographic models produced modest gains ($ΔR^2$ = 0.040 for depression, 0.021 for insulin resistance). In a 12-clinician review totaling approximately 25 active hours, clinician validity judgments aligned with CoDaS confidence tiers ($ρ$ = 0.67, $p$ = 0.005), whereas added clinical value and confidence to act were rated lower. CoDaS supports traceable, hypothesis-generating prioritization of wearable candidate biomarkers.

cs.AI↗
arXiv2026

JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions. Despite its theoretical simplicity, the practical deployment of a sequence model in production is non-trivial due to complexity of the sequence and sparse labels. For example, in Airbnb, guest sequences are often long, exploratory and complex, and we focus on booking labels, which are sparse. As such, we are often required to make various design decisions regarding data and modeling to strike a balance between effectiveness and scalability. This work delved into these production challenges and deployed JourneyFormer, a sequence modeling solution for search ranking at Airbnb. We detail crucial design considerations, covering aspects such as guest event selection, ID embeddings, model architecture, and label attribution. Additionally, we describe several tailored strategies to accelerate model training and inference. JourneyFormer has been successfully deployed within Airbnb's production, where its effectiveness and impact have been evidenced not only by improved offline ranking metrics but also by significant gains in key business metrics through online A/B testing across 2 production surfaces.

cs.LG↗
arXiv2026

WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning

Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians. However, answering questions about wearable health data remains challenging and understudied, as these ubiquitous sensors produce continuous, high-dimensional, and longitudinal data, which is non-trivial to align with text-centric distributions in LLM pretraining. The diversity of sensor modalities and user intents cannot be effectively handled by a fixed reasoning workflow or a single pretrained foundation model. To address these challenges, we propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller is employed to synthesize execution plans and dynamically route each query to the appropriate combination of sensor analysis and pretrained models, and perform grounded response auditing with external knowledge. We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains. Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.

cs.AI↗