arXiv subjects

Tian Xie

Publications and source records attributed to Tian Xie.

At least 19 recordsLinked to original sources

arXiv2026

Conditional-Moment Estimation and Inference in the BLP Model

The random-coefficient demand model of Berry, Levinsohn, and Pakes (1995) is commonly estimated by the generalized method of moments (GMM), using an unconditional moment restriction with a fixed set of instruments. Identification of the model, however, rests on a conditional moment restriction. The two are not equivalent: the unconditional restriction may admit additional parameter values. We construct a counterexample in which the model is identified by the conditional restriction yet standard GMM is not, even with the optimal instrument. Building directly on the identifying restriction, we propose a two-step estimator, following Ai and Chen (2003), that first estimates the relevant conditional expectations nonparametrically and then selects the structural parameters by a conditional-variance-weighted minimum-distance criterion; standard GMM is recovered as the special case of a linear projection onto finitely many instruments. We establish root-T asymptotic normality for the proposed estimator, and we develop the theory for both kernel and series implementations of the first stage. The two implementations share a common limiting distribution, attaining the semiparametric efficiency bound. Simulation evidence illustrates the consequences of the identification gap and demonstrates that the proposed estimator outperforms standard GMM in finite samples.

econ.EM↗

arXiv2026

SpectralShift: Effective Context Window Extension of Gated DeltaNet via Spectral Reparameterization

Recently, linear attention layers have been increasingly adopted to replace softmax attention at scale for long-context modeling. However, existing context extension approaches typically apply continued pretraining directly without modifying these layers, overlooking the spectral properties of linear attention state dynamics. In this work, we study long-context extension of Gated DeltaNet (GDN) from a spectral perspective of transition matrix and identify two essential factors governing long-range information retrieval: (1) a sufficiently broad slow spectral band aligned with the target dependency length, and (2) the preservation of fast-decaying modes for state clearing and context switching. Based on this observation, we propose SpectralShift, a spectral reparameterization approach for long-context continual pretraining of GDNs. Specifically, SpectralShift reparameterizes the alpha projections initialization to reshape the decay spectrum by enhancing slow propagation capacity, and further introduces a learning-rate scaling for alpha projections to facilitate long-context training. Experiments show that SpectralShift consistently improves long-context capabilities over training, providing an effective and efficient solution for extending context windows of linear attention models. The code has been open-sourced at https://github.com/RUCAIBox/GDN-SpectralShift.

cs.CL↗

arXiv2026

Glivenko--Cantelli Theorems for Integrated Volatility Functionals in Pure-Jump Semimartingales with an Application to Cryptocurrency Markets

We develop a two-step procedure for estimating integrated volatility functionals, defined through the occupation measure of the latent spot volatility process, when the asset price is a pure-jump semimartingale. In the first step, block-based estimators formed from absolute powers of high-frequency increments uniformly approximate local averages of powers of volatility. In the second step, these estimates are aggregated into an empirical occupation measure. Since price increments have infinite variance in this setting, arguments based on local Gaussianity are unavailable, and the uniform theory instead rests on maximal inequalities tailored to the stable regime. We establish Glivenko--Cantelli-type uniform consistency over classes of bounded monotone, Lipschitz-in-parameter, and locally Hölder test functions. These results deliver consistent estimation of volatility occupation times and quantiles, together with an argmax-consistency theory for $M$-estimators built on nonparametrically recovered latent processes. We further propose a stability-based rule for selecting the power index of the volatility estimator, which tracks an infeasible ex ante optimal choice closely in Monte Carlo experiments. An application to high-frequency cryptocurrency markets illustrates the framework in a jump-dominated, heavy-tailed environment.

math.ST↗

arXiv2026

CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications

The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-sentence/clause relationships, and evolving specification behaviors. In this work, we present CellSecInspector, an automated framework for security analysis of 3GPP specifications. CellSecInspector extracts structured state-condition-action (SCA) representations, models mobile network procedures with comprehensive function chains, systematically validates them against 9 foundational security properties under 4 adversarial scenarios, and automatically generates test cases. This end-to-end approach enables the automated discovery of vulnerabilities without relying on manually predefined security requirements or rules. Applying CellSecInspector to the well-studied 5G and 4G NAS and RRC specifications and selected sections of TS 23.501 and TS 24.229, it discovers 43 vulnerabilities, 7 of which are previously unreported. Our findings show that CellSecInspector is a scalable, adaptive, and effective solution to assess 3GPP specifications for safeguarding operational and next-generation cellular networks.

cs.CR↗

arXiv2026

eBPF Security in the Wild: Structural Concentration, Failure Mechanisms, and Discovery Gaps

Extended Berkeley Packet Filter (eBPF) is a security-critical in-kernel execution framework, yet its vulnerability landscape remains fragmented across components, semantic gaps, and testing techniques. We present an empirical study of observed eBPF vulnerabilities. We construct a multi-source dataset from Linux kernel fixing commits, syzbot reports, and public CVE/NVD records, and analyze it through a unified framework covering structural concentration, mechanism-level failure modes, architectural distribution, and discovery gaps in representative techniques. Our results show that the observed eBPF vulnerability landscape is structurally concentrated rather than broadly dispersed across many unrelated weakness types. The dominant portion is associated with a limited set of recurring system-level failures, especially in runtime execution, concurrency, object lifecycle management, and semantic inconsistencies across trusted stages. These failures are unevenly distributed across the eBPF pipeline: Runtime is the dominant exposure surface, whereas the Verifier and JIT are lowerfrequency but structurally distinct security boundaries. A rubric-based comparison of representative techniques and a version-aligned Syzkaller case study on Linux v5.10 show that, despite visible raw coverage of Runtime, Verifier, and JIT, effective exploration is semantically narrow, and observed discoveries concentrate in a small subset of Runtime failures. Overall, raw coverage alone provides an incomplete view of discovery effectiveness.

cs.CR↗

arXiv2026

Scaling Muon for Diffusion Transformers

The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large Diffusion Transformers (DiTs) remain unclear. We first establish Muon's scaling behavior on DiTs from 1.3B to 15B parameters, showing that its optimization and generative quality advantages over AdamW persist across model scales. However, at scale, the 5-step Newton--Schulz iteration (NS5) performed at every optimization step, together with full-momentum materialization, introduces substantial computation and communication overhead that can offset Muon's step-efficiency advantage. We introduce \emph{Periodic Row-wise Muon}, which performs a full NS5 spectral update once every $K$ steps and applies a low compute and communication cost row-wise constrained update based on the current momentum at the remaining steps. We further co-design a distributed implementation that operates directly on sharded momentum during non-refresh steps and accelerates spectral refreshes through bucketed all-gather and communication--computation overlap. Across all scales, Muon improves the best observed generative quality over AdamW by 12.9--19.1\%. Compared with vanilla Muon, Periodic Row-wise Muon remains within 0.5\% in best generative quality on the 1.3B--4B models and improves it by 4.5\% at 9B. It reduces optimizer time by 46.9--54.3\%, end-to-end step time by 15.7--24.3\%, and logical communication volume by 66.7\%, while reaching its respective best generative quality with 33.7--64.8\% less active training time. These results show that Periodic Row-wise Muon preserves Muon's generative quality advantage while translating it into end-to-end training efficiency for large DiTs.

cs.LG↗

arXiv2026

Beyond Source: An Empirical Study of Python Bytecode Security Risks

Python package security is largely source-centric, yet Python runtimes can execute bytecode directly through .pyc files, compiled-only modules, and marshalled code objects, creating an inspection-execution gap. We present an empirical study of Python bytecode as a security artifact. We measure bytecode exposure in PyPI distributions, evaluate practical analyzability using version-aware tooling, assess CPython runtime robustness under adversarial bytecode, and test source-level reproduction of bytecode findings. Across 1,034,843 collected PyPI artifacts, we identify 7,388 bytecode-containing artifacts, including 228,578 .pyc files and 28,193 artifact-local source-less .pyc files. For modern CPython 3.8-3.14 bytecode, at least one selected decompiler emits source for 204,901 of 204,904 in-scope files, a result measuring emission rather than verified functional equivalence. Tools are non-robust: observed PyPI bytecode triggers managed-code exceptions and timeouts, while adversarial mutated bytecode also drives decompilers into native process failures; together these outcomes yield 17 distinct robustness signatures. Fuzzing produces 1,009 stack-deduplicated runtime findings dominated by pointer-dereference symptoms; 261 groups exhibit potential memory-corruption characteristics, and at least 91.7% of groups reach execution beyond the documented-unsafe ingestion boundary. None reproduce from ordinary Python source. Bytecode is thus a visible ecosystem artifact, a practical analysis target, and a security-relevant interpreter input whose behavior need not match source-level behavior.

cs.CR↗

arXiv2026

Dissecting Software Graphs: Structural Insights for Driver-Guided Fuzzing

Many software systems expose multiple execution modes through command-line options, subcommands, and configuration flags. For such programs, fuzzing depends on both mutated inputs and the invoked mode. Yet evaluations still focus on coverage and bug counts, leaving unclear how execution modes partition, overlap, and miss software structure, and how these differences affect effectiveness. We present an empirical study of software structure under multi-driver fuzzing. We propose a structural abstraction that uses a static call graph as a shared backbone and projects driver-specific dynamic coverage onto it to derive driver-induced subgraphs. Based on this abstraction, we develop a four-phase methodology for backbone construction, fuzzing and profiling, graph-based analysis, and research-question-driven evaluation. We apply it to 27 OSS-Fuzz-derived C/C++ projects, spanning 43 executables and 854 driver configurations. Under the same total budget, multi-driver fuzzing outperforms the best single-driver baseline, increasing covered call-graph nodes by 27.9% and CFG-edge coverage by 73.5%, and revealing 11 unique bugs and abnormal behaviors largely missed by single-driver fuzzing. However, driver contributions are uneven, subgraphs differ substantially in cohesion, fragmentation, modularity, overlap, and residual under-exploration follows recurring regimes rather than a homogeneous tail. These results show that multi-driver fuzzing is fundamentally a structural exploration problem.

cs.SE↗

arXiv2026

HIVE-3D: Hierarchical Voxel Enhancement for High-Quality 3D Scene Generation

Recently, a line of works can generate impressive 3D objects from a single image, but they are limited by restricted representation resolution, making them unsuitable for 3D scene generation. In this work, we introduce HIVE-3D, a novel method for high-quality 3D scene generation based on hierarchical voxel enhancement framework. Specifically, given a single scene image as input, we first produce a coarse initial scene, then introduce image segmentation and attention-based retrieval to align 2D image components with 3D scene components. Subsequently, we organize these scene relations into a hierarchical component tree, where nodes closer to the leaves denote finer-grained components. Finally, we propose a voxel super-resolution model that generates refined voxels for the target instance while maintaining strong consistency with the coarse voxels. Equipped with this model, we perform coarse-to-fine hierarchical super-resolution on images and voxels for each component, producing a high-resolution and high-quality 3D scene. Extensive experiments demonstrate that our method significantly outperforms previous approaches, achieving state-of-the-art performance.

cs.CV↗

arXiv2026

Visualizing orbital magnetism in electron doped rhombohedral multilayer graphene

Electron doped rhombohedral multilayer graphene at high displacement field features an exceptionally flat band minimum with near-ideal quantum geometry. Experiments in this regime observe the formation of a 'quarter metal,' in which the electron liquid condenses into a single spin- and valley flavor. Remarkably, recent experiments have found a zero resistance state in the same region of the density- and displacement-field-tuned parameter space, attributed to the formation of a chiral superconductor from an orbitally ferromagnetic normal state. Here, we use nanoSQUID-on-tip magnetometry to map the orbital magnetization of electron-doped rhombohedral graphene devices ranging in thickness between 3 and 15 layers. Magnetization within the quarter metal phases peaks at finite density, consistent with concentration of the Berry curvature in a finite-momentum 'ring of fire'. Correlating transport and local magnetometry data in a superconducting tetralayer sample reveals a finite orbital ferromagnetic moment, providing direct evidence of valley polarization in the superconducting ground state. We further show that widely observed stochastic switching of the resistivity in both metallic and superconducting regimes arises from a density-tuned sign change in the valley-resolved total magnetic moment. This leads to the formation of metastable magnetic domains under typical gate control sequences and can also be harnessed for electric-field controlled switching of the magnetization across the entire device. Finally, high resolution measurements of the magnetization across a superconducting transition allow us to put an upper bound on the 'condensation magnetization' of 0.1 Bohr magneton per carrier, placing a strong quantitative restriction on theoretical models for ferromagnetic superconductivity.

cond-mat.mes-hall↗

arXiv2026

TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning

The Abstraction and Reasoning Corpus (ARC) tests whether a model can infer an unseen transformation from a few input-output examples and apply it to a new grid. Looped visual reasoners refine predictions over multiple iterations, but conventional training constrains only the final output, leaving intermediate refinements unconstrained. We propose that these refinements should instead follow the transformation step by step. We introduce TraceViT, a looped visual reasoner trained with semantically monotonic transformation chains. We obtain these chains by rewriting and verifying programmatic task implementations, decomposing each solution into intermediate grid states. Each iteration is grounded by a task reference derived from the few-shot demonstrations and an object workspace representing the current grid state. Because these chains may differ in length from the loop, soft trace alignment enforces only their ordering, letting the model allocate iterations freely. TraceViT achieves 67.8% pass@2 on ARC-AGI-1 and 24.3% on ARC-AGI-2. Controlled ablations on ARC-AGI-1 show that trace supervision becomes beneficial only when paired with grounding. Code and data will be available at https://github.com/LiuBinnan/TraceViT.

cs.CV↗

arXiv2026

A two-dimensional piezo-optomechanical transducer

Optical quantum networks provide a natural route for connecting distant superconducting quantum processors, enabling distributed quantum computation, sensing, and communication. Piezo-optomechanical transducers are among the leading candidates for scalable microwave-to-optical quantum interfaces. However, prior one-dimensional piezo-optomechanical transducers remain limited by optical-absorption-induced heating and the resulting thermal noise. Two-dimensional optomechanical crystals offer substantially improved thermalization from better thermal anchoring, but their structural complexity has so far hindered the realization of a fully integrated two-dimensional transducer. Here, we overcome this challenge with a new design strategy based on band structure engineering. We fabricate the devices and experimentally characterize the response, measuring an electromechanical damping rate of 4.6 kHz at room temperature and electromechanical coupling rate of 0.17 MHz by wire-bonding to a multi-mode microwave resonator at 10 mK. Bidirectional transduction is performed with a calibrated internal efficiency of 0.85\% in the continuous-wave operation, alongside pulsed photon-phonon pair generation near its quantum ground state. Our results represent a significant step toward high-efficiency and low-noise transducers for entangling remote superconducting qubits.

quant-ph↗

arXiv2026

TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation

Vision-Language-Action (VLA) models have become a powerful framework for robotic manipulation, and recent studies have introduced tactile or force feedback into VLAs to address contact-rich tasks. However, these models are typically deployed as offline policies. When contact conditions shift from the training distribution, the policy cannot perform online adaptation, leading to problems such as inappropriate contact forces and inefficient retries. Therefore, we propose TORL-VLA, a tactile-guided online reinforcement learning framework that couples tactile feedback with policy refinement for contact-rich manipulation. Our method introduces a tactile-derived wrench-aware VLA to predict reference actions and future wrench sequences, while a lightweight online RL module is used to refine the reference actions. To stabilize learning from mixed exploratory policy-generated and human-intervention data, we introduce an intervention-censored critic that prevents post-intervention success from being wrongly credited to policy-generated actions preceding intervention. Real-robot experiments on long-horizon contact-rich tasks, including latch manipulation, coffee-cup placement, and egg handling, show that TORL-VLA improves success rates at both subtask and full-task levels, as well as time-bounded execution efficiency over strong baselines. Project page: https://torl-vla.github.io/

cs.RO↗

arXiv2026

Compound Selection Decisions: An Almost SURE Approach

This paper proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown, fixed parameters $μ_ {1:n}$ and known $σ_{1:n}$ with observations $Y_i \sim \textsf{N}(μ_i, σ_i^2)$, the decision maker would like to select a subset of indices $S$ so as to maximize utility $\frac{1}{n}\sum_{i\in S} (μ_i - K_i)$, for known costs $K_i$. Inspired by Stein's unbiased risk estimate (SURE), we introduce an almost unbiased estimator, called ASSURE, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE produces decision rules that are asymptotically no worse than the optimal but infeasible decision rule in the pre-specified class. We apply ASSURE to the selection of Census tracts for economic opportunity, the identification of discriminating firms, and the analysis of $p$-value decision procedures in A/B testing.

econ.EM↗

arXiv2026

TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation

Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive performance but do not provide readable, case-specific explanations essential for decision-making. Large Language Models (LLMs) can naturally bridge this gap by generating predictions alongside explanations. However, dataset-specific patterns, such as feature distributions and interactions, make tabular data difficult for LLMs to understand and reason over, while label-only fine-tuning improves performance at the cost of catastrophic forgetting. To address this problem, we propose Tri-Level Rationale Distillation (TLRD), a framework that converts label-only tabular datasets into structured rationale supervision for LLMs. TLRD uses a high-capacity teacher to synthesize a rationale corpus grounded in three complementary levels of evidence: instance-level feature, dataset-level distributional context, and comparison-level retrieved neighbors, then distills the rationale into student LLMs, enabling zero-overhead prediction and grounded explanation from raw features only. Experiments on multiple domain datasets show that TLRD significantly closes the performance gap between LLMs and state-of-the-art tree ensembles while producing grounded and readable explanations, offering a valuable reference for high-stakes decision-making.

cs.CL↗

arXiv2026

A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval

Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate processes. LST derived from medium- to high-resolution thermal infrared (TIR) observations effectively reveals thermal environmental disparities across distinct landscape units. However, achieving accurate, robust, and globally generalizable LST retrieval remains challenging under complex atmospheric conditions and diverse land cover types. Traditional split window (SW) algorithms heavily rely on empirical parameterizations, whose fixed coefficients fail to adapt to complex scenarios such as high surface temperatures and high atmospheric water vapor content. Concurrently, conventional data-driven models exhibit limited generalizability to out-of-distribution (OOD) samples due to the absence of explicit physical structure constraints. To address these issues, this study proposes a Parallel Component Decoupled Neural Network (PCD-Net) framework, which reformulates SW retrieval as a dynamic learning problem of physical component coefficients. Using the SW equation as the physical backbone, the framework constructs parallel subnetworks to adaptively learn the dynamic coefficients corresponding to the constant, first-order, and second-order brightness temperature difference terms; meanwhile, a residual branch is incorporated to supplement the nonlinear coupling corrections induced by the joint effects of surface emissivity and atmospheric water vapor. Through this component-level decoupled modeling, PCD-Net explicitly characterizes the dynamic response relationships between land surface emissivity, atmospheric water vapor content, and different SW physical components.

physics.ao-ph↗

arXiv2026

MatterSim-MT: A multi-task foundation model for in silico materials characterization

Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progress, they remain fundamentally limited in scalability and generalizability across the vast space of structures and properties relevant to real-world materials design. We present MatterSim-MT, a multi-task foundation model for in silico materials simulation and property characterization. The model is pretrained on over 35 million first-principles-labeled structures covering 89 elements, temperatures up to 5000 K and pressures up to 1000 GPa, and is fine-tuned on various properties including Bader charges, magnetic moments, Born effective charges, and dielectric matrices. Out of the box, MatterSim-MT not only serves as a foundation model for predicting material structure, dynamics and thermodynamics, its multi-task architecture also enables a wide range of complex simulations that cannot be captured by potential energy surfaces alone. For example, we demonstrate pressure-dependent LO-TO phonon splitting in SiC with close agreement with experiment, electric hysteresis in ferroelectric BaTiO3, and the cationic-to-anionic redox transition during delithiation of a Li-rich cathode material. Finally, we show that MatterSim-MT scales well with more data and parameters, can be efficiently fine-tuned to higher levels of theory, and can be efficiently extended to new systems via active learning. Overall, we believe this approach provides a scalable route to accurate in silico materials characterization.

cond-mat.mtrl-sci↗

arXiv2026

GeRM: A Generative Rendering Model From Physically Realistic to Photorealistic

While physically-based rendering (PBR) simulates light transport that guarantees physical realism, achieving true photorealistic rendering (PRR) demands prohibitive time and labor, and still struggles to capture the intractable richness of the real world. We propose GeRM, the first multimodal generative rendering model to bridge the gap from PBR to PRR (P2P). We formulate this P2P transition by learning a distribution transfer vector (DTV) field to direct the generative process. To achieve this, we introduce a multi-condition ControlNet that synthesizes PBR images and progressively transitions them into PRR images, guided by G-buffers, text prompts, and cues for enhanced regions. To improve the model's grasp of the image distribution shift driven by text prompts, we propose a residual perceptual transfer mechanism to associate text prompts with corresponding targeted modification regions, which more clearly defines the incremental component updates. To supervise this transfer process, we introduce a multi-agent visual language model framework to construct an expert-guided pairwise transfer dataset, named P2P-50K, where each paired sample corresponds to a specific transfer vector in the DTV field. Extensive experiments demonstrate that GeRM synthesizes high-quality controllable images and outperforms state-of-the-art baselines across diverse applications, including PBR and PRR image synthesis and editing.

cs.CV↗