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Xinyi Li

Publications and source records attributed to Xinyi Li.

At least 55 records · Page 3Linked to original sources
arXiv2026

Tail exponents of the three-dimensional uniform spanning tree and Abelian sandpile

We study the local geometry of the three-dimensional uniform spanning tree and its connection with the Abelian sandpile model. We obtain sharp tail exponents, up to subpolynomial errors, for the past of the origin in the three-dimensional UST and for the $0$-tree of the $0$-wired uniform spanning forest. As a principal application, we prove the corresponding three-dimensional Abelian sandpile avalanche exponents: the avalanche-cluster radius has tail exponent $1$, while both the avalanche-cluster size and the total number of topplings have tail exponent $1/3$. These results identify the leading power-law behaviour of three-dimensional sandpile avalanches and improve previously known bounds.

math.PR↗
arXiv2026

Generalized intersection exponents and local cut points for three-dimensional Brownian loop soup

We study generalized non-intersection probabilities for the three-dimensional Brownian loop soup at subcritical intensities. We establish the existence of generalized intersection exponents (GIE) and prove an up-to-constants estimate for these probabilities by means of a separation lemma tailored to this setting. We also relate the Hausdorff dimension of the set of local cut points of the three-dimensional Brownian loop soup to the GIE, and show that the GIE is continuous at intensity zero, where it reduces to the classical Brownian intersection exponent. In particular, this implies that, for sufficiently small intensity parameters, the set of local cut points has Hausdorff dimension strictly larger than $1$.

math.PR↗
arXiv2026

Loop pruning and downward deviations for maximum local time of discrete-time simple random walks

We study downward deviations of the maximum local time of the discrete-time simple random walk on $\mathbb{Z}^d$, $d\ge 3$. In our previous paper \cite{li2026ldmaxlocal}, the corresponding upper bound was established, while the matching lower bound was left open. In the present paper, we prove this lower bound and hence obtain the sharp asymptotic formula for the downward-deviation probability. To provide a discrete-time analogue of the jump-chain/holding-time structure used in the continuous-time argument, we introduce a new random structure which we name as {\it loop-pruned random walk} and the associated loop-pruning decomposition, which is also of independent interest.

math.PR↗
arXiv2026

Context Learning for Multi-Agent Discussion

Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via structured discussion. However, we find that current MAD methods easily suffer from discussion inconsistency, LLMs fail to reach a coherent solution, due to the misalignment between their individual contexts.In this paper, we introduce a multi-LLM context learning method (M2CL) that learns a context generator for each agent, capable of dynamically generating context instructions per discussion round via automatic information organization and refinement. Specifically, inspired by our theoretical insights on the context instruction, M2CL train the generators to control context coherence and output discrepancies via a carefully crafted self-adaptive mechanism.It enables LLMs to avoid premature convergence on majority noise and progressively reach the correct consensus. We evaluate M2CL on challenging tasks, including academic reasoning, embodied tasks, and mobile control. The results show that the performance of M2CL significantly surpasses existing methods by 20%--50%, while enjoying favorable transferability and computational efficiency.

cs.AI↗
arXiv2026

Wireless large AI model: shaping the AI-empowered future of 6G and beyond

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is a wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, explaining its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges and discuss pivotal future research directions.

cs.IT↗
arXiv2026

HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily

Many real-world heterogeneous graphs exhibit pronounced heterophily, where connected nodes often have dissimilar labels or play different semantic roles. In such settings, standard heterogeneous graph neural networks that aggregate messages along metapaths or meta-relations primarily based on feature similarity can propagate misleading information, since feature similarity may be misaligned with underlying relational semantics. In this paper, we propose HeterSEED, a semantics-structure decoupling framework for heterogeneous graph learning under heterophily. HeterSEED decouples representation learning into a heterogeneous semantic channel that captures type- and relation-aware local semantics and a structure-aware heterophily channel that separates homophilic and heterophilic neighborhoods via pseudo-label-guided partitioning and aggregates them using metapath-based structural weights. A node-level adaptive fusion mechanism then combines the two channels to produce context-dependent node representations. Theoretically, we establish that, on heterogeneous graphs under heterophily, HeterSEED is strictly more expressive than standard heterogeneous graph neural networks that rely primarily on feature similarity and provably reduces the prediction bias introduced by heterophilic neighbors. Experiments on five real-world heterogeneous graphs, including two large-scale networks at the million-node and hundred-million-edge scale, demonstrate that HeterSEED consistently outperforms representative heterogeneous graph neural networks and recent heterophily-aware baselines, especially in strongly heterophilic regimes.

cs.LG↗
arXiv2026

Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations

Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks generated by general-purpose segmentation models (e.g., SAM) and filtered via rules are often unreliable, as these models lack the task-specific semantic understanding required for effective pseudo labeling in COD; and (2) the neglect of inherent annotation bias in scribbles, which hinders the model from capturing the global structure of camouflaged objects. To overcome these challenges, we propose ${D}^{3}$ETOR, a two-stage WSCOD framework consisting of Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing. In the first stage, we introduce an adaptive entropy-driven point sampling method and a multi-agent debate mechanism to enhance the capability of SAM for COD, improving the interpretability and precision of pseudo masks. In the second stage, we design FADeNet, which progressively fuses multi-level frequency-aware features to balance global semantic understanding with local detail modeling, while dynamically reweighting supervision strength across regions to alleviate scribble bias. By jointly exploiting the supervision signals from both the pseudo masks and scribble semantics, ${D}^{3}$ETOR significantly narrows the gap between weakly and fully supervised COD, achieving state-of-the-art performance on multiple benchmarks.

cs.CV↗
arXiv2026

Non-existence of several random fractals in Brownian motion and Brownian loop soup

We develop a unified approach to establish the non-existence of three types of random fractals: (1) the pioneer triple points of the planar Brownian motion, answering an open question in [7], (2) the pioneer double cut points of the planar and three-dimensional Brownian motions, and (3) the double points on the boundaries of the clusters of the planar Brownian loop soup at the critical intensity, answering an open question in [39]. These fractals have the common feature that they are associated with an intersection or disconnection exponent which yields a Hausdorff dimension ``exactly zero''.

math.PR↗
arXiv2026

Filter Design for Estimating the Stellar Metallicity of Metal-poor Stars from Gaia XP Spectra

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $λ_{\rm c}$ = 3960 Angstrom with a bandwidth $Δλ$ = 80 Angstrom for giant stars, and $λ_{\rm c} $= 3920 Angstrom with $Δλ$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

astro-ph.SR↗
arXiv2026

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models

A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical state of both the embodied agent and its environment. Accurate world models are essential for enabling agents to think, plan, and reason effectively in complex, dynamic settings. However, existing world models often focus on random generation of open worlds, but neglect the need for high-fidelity modeling of deterministic scenarios (such as fixed-map mazes and static space robot navigation). In this work, we take a step toward building a truly accurate world model by addressing a fundamental yet open problem: constructing a model that can fully clone a deterministic 3D world. 1) Through diagnostic experiment, we quantitatively demonstrate that high-fidelity cloning is feasible and the primary bottleneck for long-horizon fidelity is the geometric structure of the latent representation, not the dynamics model itself. 2) Building on this insight, we show that applying temporal contrastive learning principle as a geometric regularization can effectively curate a latent space that better reflects the underlying physical state manifold, demonstrating that contrastive constraints can serve as a powerful inductive bias for stable world modeling; we call this approach Geometrically-Regularized World Models (GRWM). At its core is a lightweight geometric regularization module that can be seamlessly integrated into standard autoencoders, reshaping their latent space to provide a stable foundation for effective dynamics modeling. By focusing on representation quality, GRWM offers a simple yet powerful pipeline for improving world model fidelity.

cs.LG↗
arXiv2026

One-arm probabilities for the two-dimensional metric-graph and discrete Gaussian free field

We study the one-arm probability in the level-set percolation of the discrete and metric-graph Gaussian free field (GFF) defined on a box with Dirichlet boundary conditions. For the metric-graph case, we establish asymptotic estimates on two one-arm probabilities of interest. For the discrete case, we show up-to-constants bounds on the point-to-bulk probability and demonstrate its difference from the metric-graph case.

math.PR↗
arXiv2026

Linear Regression Using Principal Components from General Hilbert-Space-Valued Covariates

We introduce Adaptive Subspace PCA (AS-PCA), a framework for principal component analysis of random elements in a general separable Hilbert space. AS-PCA projects the covariance operator onto a data-adaptive finite-dimensional subspace prior to eigendecomposition, requiring no kernel specification and accommodating multi-dimensional functional objects including images and surfaces. Under the second-moment condition, we prove a Donsker theorem for Hilbert-space-valued empirical processes and use it to establish uniform consistency and joint Gaussian limits for the leading eigenpairs. A data-driven diagnostic verifies projection accuracy, and a consistent proportion-of-variance-explained rule selects the number of components. Building on AS-PCA, we construct Hilbert-Space Principal Component Regression (HS-PCR) for models combining Euclidean and Hilbert-space-valued covariates. The HS-PCR estimator is root-$n$ consistent and asymptotically normal, with an explicit influence function decomposition accounting for eigenfunction estimation uncertainty. Both nonparametric and wild bootstrap procedures are shown to be asymptotically valid. Simulations with two- and three-dimensional imaging predictors confirm accurate eigenstructure recovery and nominal bootstrap coverage. HS-PCR is applied to Alzheimer's Disease Neuroimaging Initiative data in regression and precision-medicine settings.

math.ST↗
arXiv2026

GUIDES: Guidance Using Instructor-Distilled Embeddings for Pre-trained Robot Policy Enhancement

Pre-trained robot policies serve as the foundation of many validated robotic systems, which encapsulate extensive embodied knowledge. However, they often lack the semantic awareness characteristic of foundation models, and replacing them entirely is impractical in many situations due to high costs and the loss of accumulated knowledge. To address this gap, we introduce GUIDES, a lightweight framework that augments pre-trained policies with semantic guidance from foundation models without requiring architectural redesign. GUIDES employs a fine-tuned vision-language model (Instructor) to generate contextual instructions, which are encoded by an auxiliary module into guidance embeddings. These embeddings are injected into the policy's latent space, allowing the legacy model to adapt to this new semantic input through brief, targeted fine-tuning. For inference-time robustness, a large language model-based Reflector monitors the Instructor's confidence and, when confidence is low, initiates a reasoning loop that analyzes execution history, retrieves relevant examples, and augments the VLM's context to refine subsequent actions. Extensive validation in the RoboCasa simulation environment across diverse policy architectures shows consistent and substantial improvements in task success rates. Real-world deployment on a UR5 robot further demonstrates that GUIDES enhances motion precision for critical sub-tasks such as grasping. Overall, GUIDES offers a practical and resource-efficient pathway to upgrade, rather than replace, validated robot policies.

cs.RO↗
arXiv2026

Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation

In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.

cs.IR↗
arXiv2026

A Scalable Benchmark for Repository-Oriented Long-Horizon Conversational Context Management

In recent years, large language models (LLMs) have advanced rapidly, substantially enhancing their code understanding and generation capabilities and giving rise to powerful code assistants. However, in practical repository development, excessively long-horizon conversational context may overwhelm models, causing the loss of critical information and degraded performance, thereby limiting the utility of code assistants. Existing context management methods proposed to mitigate this context dilemma primarily target general-purpose conversations, while repository-oriented solutions remain largely unexplored, which is largely due to the lack of reliable evaluation benchmarks. To bridge this gap, we present LoCoEval, the first long-horizon conversational context management benchmark tailored to repository-oriented development scenarios. Adhering to three key principles, LoCoEval is constructed via an LLM-driven pipeline that generates realistic and diverse repository-oriented conversations, capturing key interaction patterns such as iterative requirements, noisy input, and retrospective questions. We evaluate 7 baselines, including 4 representative context management methods, using 3 advanced backbone LLMs on LoCoEval. The results reveal substantial challenges faced by standalone LLMs and existing approaches, especially memory systems, in repository-oriented conversational scenarios. To address these limitations, we further propose an improved method integrating conversational and repository information into a unified memory, which outperforms all baselines (*Oracle* excluded) and demonstrates robustness. Additionally, we investigated the impact of various factors on method performance, providing actionable insights for future research.

cs.SE↗
arXiv2026

Inferring brain plasticity rule under long-term stimulation with structured recurrent dynamics

Understanding how long-term stimulation reshapes neural circuits requires uncovering the rules of brain plasticity. While short-term synaptic modifications have been extensively characterized, the principles that drive circuit-level reorganization across hours to weeks remain unknown. Here, we formalize these principles as a latent dynamical law that governs how recurrent connectivity evolves under repeated interventions. To capture this law, we introduce the Stimulus-Evoked Evolution Recurrent dynamics (STEER) framework, a dual-timescale model that disentangles fast neural activity from slow plastic changes. STEER represents plasticity as low-dimensional latent coefficients evolving under a learnable recurrence, enabling testable inference of plasticity rules rather than absorbing them into black-box parameters. We validate STEER with four benchmarks: synthetic Lorenz systems with controlled parameter shifts, BCM-based networks with biologically grounded plasticity, a task learning setting with adaptively optimized external stimulation and longitudinal recordings from Parkinsonian rats receiving closed-loop DBS. Our results demonstrate that STEER recovers interpretable update equations, predicts network adaptation under unseen stimulation schedules, and supports the design of improved intervention protocols. By elevating long-term plasticity from a hidden confound to an identifiable dynamical object, STEER provides a data-driven foundation for both mechanistic insight and principled optimization of brain stimulation.

q-bio.NC↗
arXiv2026

DIAL: Decentralized I/O AutoTuning via Learned Client-side Local Metrics for Parallel File System

Enabling efficient, high-performance data access in parallel file systems (PFS) is critical for today's high-performance computing systems. PFS client-side I/O heavily impacts the final I/O performance delivered to individual applications and the entire system. Autotuning the key client-side I/O behaviors has been extensively studied and shows promising results. However, existing work has heavily relied on extensive number of global runtime metrics to monitor and accurate modeling of applications' I/O patterns. Such heavy overheads significantly limit the ability to enable fine-grained, dynamic tuning in practical systems. In this study, we propose DIAL (Decentralized I/O AutoTuning via Learned Client-side Local Metrics) which takes a drastically different approach. Instead of trying to extract the global I/O patterns of applications, DIAL takes a decentralized approach, treating each I/O client as an independent unit and tuning configurations using only its locally observable metrics. With the help of machine learning models, DIAL enables multiple tunable units to make independent but collective decisions, reacting to what is happening in the global storage systems in a timely manner and achieving better I/O performance globally for the application.

cs.DC↗