arXiv subjects

Dong Wang

Publications and source records attributed to Dong Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Nonlinear stability of 3-D plane Couette flow for Boussinesq system in a finite channel

In this paper, we study the hydrodynamics stability of the 3-D plane Couette flow $v_s=(y,0,0)$ with constant temperature state $ρ_s$ for the Boussinesq system in a finite channel $\mathbb{T}\times [-1,1]\times \mathbb{T}$ with non-slip boundary condition in $y$ direction. We prove that if the initial velocity field $v_{in}$ and initial temperature $ρ_{in}$ satisfy \begin{align*} \|v_{in}-(y,0,0)\|_{H^2} \leq c_0 ν, \ \ \|ρ_{in}-ρ_s\|_{H^2} \leq c_0 ν^2, \end{align*} for some $c_0>0$ independent of the viscosity and thermal diffusion $ν$, then the solutions to 3D Boussinesq system do not transit from the Couette flow and constant temperature state. To our best knowledge, this is the first result about the nonlinear stability of plane Couette flow for 3-D Boussinesq system in the domain with non-slip boundary condition. This result, together with [18], implies that the strong boundary layer effect does not lead to strong instability.

math.AP↗

arXiv2026

Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts

Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confidence is calibrated. The resulting models are systematically overconfident. Recent methods train calibration inside the RLVR loop by having the model state a numerical confidence alongside its answer, but they all obtain the confidence by sampling it as text. This choice imposes two costs: a sampled confidence introduces variance and in practice collapses to a handful of distinct values, and sampling makes the confidence non-differentiable, forcing the calibration loss through a scalar reward. We propose CREDO (Confidence REaDOut) to replace sampling with a deterministic readout. While RLVR optimizes correctness, CREDO reads the confidence from a dedicated token pair in the model's output distribution and trains it by differentiable regression. CREDO further turns the trained confidence into a signal for accuracy, weighting rollouts by how far confidence and outcome disagree, so that accuracy and calibration improve together. Across mathematical and code reasoning, CREDO attains the best accuracy and calibration, and the gains extend to abstention and selective prediction.

cs.LG↗

arXiv2026

Learning to Track from Privileged Target Appearances

Target templates define what a visual tracker searches for, yet the templates available at inference trade off localization certainty with appearance freshness: the initial ground-truth template is exact but becomes stale, whereas recent templates better reflect the current appearance but are cropped from uncertain predictions. We quantify this bottleneck with a non-deployable oracle that supplies an exact current-frame target crop, improving AUC on LaSOT by 15.2 percentage points. This gap reveals a training-only opportunity: frame-level ground truths provide exact current- and future-frame target crops, although such crops are unavailable at deployment. We introduce Privileged Appearance Transfer for Tracking (PATT), a teacher-student training framework that transfers these privileged appearances to a deployable tracker through multi-level representation prediction. The privileged teacher observes exact target crops from past, current, and future frames, whereas the student receives only past-frame templates and learns to predict the teacher's search representations. To avoid transferring unreliable teacher signals, PATT weights this transfer by the teacher's relative localization advantage over the student and its absolute localization accuracy. After training, the teacher, latent predictor, reliability weights, and privileged crops are removed, leaving standard student-only inference. Across seven benchmarks at two model scales, PATT achieves consistent gains under both long- and short-term tracking protocols.

cs.CV↗

arXiv2026

UCON: Uncertainty-aware Navigation with Historical Re-association in Dynamic Environments

Autonomous navigation in dynamic environments is hindered by two fundamental challenges: perception instability and uncertainty-optimization mismatch. The former leads to identity switches and unreliable motion estimation, while the latter prevents principled incorporation of motion uncertainty into trajectory optimization. To address these challenges, we propose UCON, an uncertainty-aware navigation algorithm in dynamic environments. For perception instability, we present a point-level historical re-association mechanism that leverages historical point cloud fragments to recover lost targets while maintaining identity continuity. Subsequently, a Kalman filter is employed to provide anisotropic motion state estimation and covariance propagation. To resolve the uncertainty-optimization mismatch, we transform predicted states and their covariances into uncertainty sectors, which are embedded as differentiable cost terms within a trajectory optimization framework. This achieves consistent uncertainty-aware dynamic obstacle avoidance while maintaining smoothness and feasibility. Extensive simulations and real-world experiments demonstrate that, while maintaining high computational efficiency, UCON achieves superior perception stability and robust navigation performance in dynamic environments compared to state-of-the-art methods. The code will be open-sourced to facilitate further research.

cs.RO↗

arXiv2026

Scheduling Coflows in Multi-Core OCS Networks with Performance Guarantee

The coflow abstraction captures application-level communication patterns and enables coordinated scheduling of parallel flows to reduce job completion times in distributed systems. Modern data center networks (DCNs) are employing multiple independent optical circuit switching (OCS) cores operating concurrently to meet the massive bandwidth demands of application jobs. However, existing coflow scheduling research primarily focuses on the single-core setting, while studies of multi-core fabrics have largely considered electrical packet switching (EPS) networks. To address this gap, this paper studies the coflow scheduling problem in multi-core OCS networks under the not-all-stop reconfiguration model, in which the reconfiguration of one circuit does not interrupt other circuits. The challenges stem from two aspects: (i) cross-core coupling induced by traffic assignment across heterogeneous cores; and (ii) per-core OCS scheduling constraints, namely \textit{port exclusivity} and \textit{reconfiguration delay}. We propose an approximation algorithm that jointly integrates cross-core flow assignment and per-core circuit scheduling to minimize the total weighted coflow completion time (CCT) and establish a provable worst-case performance guarantee. Furthermore, our algorithm framework can be applied to the multi-core EPS scenario with a corresponding approximation guarantee for packet-switched fabrics. Trace-driven simulations using real Facebook workloads demonstrate that our algorithm can reduce the total weighted CCT and tail CCT.

cs.DC↗

arXiv2026

Hunyuan-A13B Technical Report

We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rigorously filtered 20T-token corpus with enhanced STEM data curation, improving factual reliability and reasoning ability. High-quality supervised fine-tuning and large-scale reinforcement learning further enhance its overall performance. Hunyuan-A13B also introduces a dual-mode Chain-of-Thought framework that adapts reasoning depth to task complexity: fast thinking for routine queries and slow thinking for complex, multi-step problems. Evaluations show competitive performance across mathematics, science, programming, general language understanding, and agent tasks, often approaching that of much larger models. Its high inference throughput makes it suitable for latency-sensitive applications. We release Hunyuan-A13B to support open research and practical LLM deployment.

cs.AI↗

arXiv2026

Hard to soft edge transition for the Muttalib-Borodin ensembles with integer parameter $θ$

We find the universal limiting correlation kernels of the Muttalib-Borodin (MB) ensembles with integer parameter $θ\geq 2$ at $0$ in the transitive regime between the hard edge regime and the soft edge regime. This generalizes the previously studied hard edge to soft edge transition in unitarily invariant random matrix theory by Its, Kuijlaars and Östensson, which is the $θ= 1$ special case of our MB ensemble. The derivation is based on the vector Riemann-Hilbert (RH) problems for the biorthogonal polynomials associated with the MB ensemble. In the analysis of the RH problems, we construct matrix-valued model RH problems of size $(θ+ 1) \times (θ+ 1)$, and prove the solvability of the model RH problems by a vanishing lemma. The new limiting correlation kernels are proved to be universal for a large class of potential functions, and they interpolate the Meijer G-kernels for the hard edge regime and the Airy kernel for the soft edge regime. We observe that the new limiting correlation kernels have the integrability that is not seen in previous studies in random matrix theory and determinantal point processes. In the $θ= 2$ case, we give a detailed analysis of the Lax pair associated with the model RH problem, show that it results in the Chazy I equation, which has a Painlevé IV reduction, and find that the Lax pair is in the Drinfeld-Sokolov hierarchies.

math-ph↗

arXiv2026

WOLF: World Model Guided LiDAR Exploration with Predictive Frontiers

LiDAR-based unmanned aerial vehicle (UAV) exploration builds maps by continually selecting where to observe next. However, decisions based on the measured map provide limited foresight into spatial continuations behind occlusions, leaving potentially informative directions unrecognized. We present WOLF, a world-model-guided framework that predicts future observations to enhance autonomous exploration. In the training stage, a recurrent world model learns observation dynamics from exploration trajectories, with recurrent memory retaining the spatial context needed to interpret partial observations across successive views. Building on this context, the model combines observation history with candidate motions during exploration to predict local occupancy and visibility. To guide further sensing, a predictive frontier generation mechanism then aligns and fuses these predictions using confidence, branch agreement, and observation quality to identify promising regions. The resulting predictive frontiers join measured ones to guide geometric viewpoint selection and trajectory generation, while new scans update subsequent predictions. In simulations, our method reduces mean terminal time by 10.9% relative to EPIC in Garage at comparable coverage and increases mean coverage from 42.12% to 98.35% in Tunnel. Real-world experiments further demonstrate onboard deployment of the learned model for online inference during physical flight.

cs.RO↗

arXiv2026

Tumor-induced lipid loss physically accelerates breast cancer invasion into mammary adipose tissue

Obesity is a major risk factor for breast cancer, yet how obesity-associated changes in the physical properties of white adipose tissue (WAT) influence tumor invasion remains poorly understood. Here, we combine experimental and computational approaches to investigate this question. Using mouse models and human mastectomy samples, we show that obesity not only increases adipocyte size, but also asphericity. We observe that tumor-conditioned media drives lipid loss and dedifferentiation of adipocytes into myofibroblast-like cells in vitro and that adipocytes in high-fat diet-fed mice lose lipid at a faster rate than those in normal diet-fed mice in response to mammary tumors in vivo. We then develop discrete element method (DEM) simulations to understand how these obesity-induced changes in cell and tissue properties alter breast cancer invasion under high-fat and normal diet conditions. In DEM simulations, adipocytes are modeled as deformable polygons and polyhedra, and cancer cells are modeled as soft, adhesive disks and spheres in two and three dimensions, respectively. Invasion is driven by cancer cell proliferation and tumor-induced lipid loss is modeled as corresponding decreases in adipocyte size. DEM simulations indicate that obesity-associated increases in tissue pressure induce adipocyte deformation, consistent with elevated asphericity in experimental data. At small lipid loss rates, the degree of cancer invasion is only weakly affected by tissue pressure. However, at sufficiently large lipid loss rates, we find that tissue pressure accelerates cancer invasion. Together, these results suggest that lipid loss physically remodels WAT to facilitate breast cancer invasion, and that obesity may exacerbate this effect.

cond-mat.soft↗

arXiv2026

From Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study

Sustained emotional support is a long-horizon interaction task closely tied to human well-being. Recent research demonstrates generative agents' capacity for momentary emotional support, yet how these capabilities sustain support over time remains unclear. To examine this challenge, we deployed PAIR, a theory-based emotion-regulation companion, with 19 participants for 14 days. Across 1,093 sessions, we paired emotion estimates with self-reports before and after guidance and analyzed logs and interviews. Estimates corresponded more closely to self-reported valence and dominance than arousal. Guided conversations were followed by higher valence and state-dependent arousal changes. Participants felt understood through contextual exploration and emotional acknowledgment, acting on guidance suited to their needs and constraints. Perceived helpfulness of guided conversation significantly increased over time. Our findings link memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over memory.

cs.HC↗

arXiv2026

A Study on the Impact of Natural Language Differences in Prompts on Automatic Code Generation Using LLMs

Large Language Models (LLMs) have demonstrated remarkable performance in automatic code generation tasks, thereby encouraging new research in this area. Although numerous studies have explored LLM-based code generation, the impact of the natural language in input prompts remains unexplored (language bias). This study aims to (1) quantify how the natural language of input prompts influences LLM-based code generation performance and (2) evaluate a mitigation strategy to reduce language bias in code generation. We assess code generation Accuracy on AtCoder, LeetCode, and BigCodeBench. To quantify the language bias on code generation, each problem is presented in English, Japanese, and Chinese. We use seven LLMs (GPT-4o, o3-mini, DeepSeek-V3.2, Llama-3, Qwen2.5-Coder-14B, Qwen2.5-Coder-0.5B, and GitHub Copilot) and assess their performance in terms of Accuracy (the number of problems for which generated code passes all test cases). We compare Accuracy before and after translation to evaluate the effectiveness of translation as a mitigation strategy. We observed that the natural language of problem statements affects LLM-based code generation performance. Specifically, the languages officially supported by each dataset achieved the highest median Accuracy. Also, translation improved Accuracy, but its effectiveness was not consistent across datasets and model types. We found that AtCoder contained a particularly high proportion of narrative-style problem statements and longer problem statements. Natural language significantly affects LLM code generation accuracy. Translation can mitigate language bias in some settings, but its effectiveness depends on the dataset and model type. Furthermore, the narrative aspects and context length of input prompts are important factors related to language bias and the effectiveness of translation as a mitigation strategy.

cs.SE↗

arXiv2026

Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation

Action-conditioned world models have emerged as a promising paradigm for robot learning, offering a scalable alternative to costly real-world experimentation by generating action-consistent video rollouts. However, persistent world modeling remains challenging in manipulation: frequent end-effector occlusions and rapid wrist-camera motion make the current observation insufficient for predicting future views, causing models to forget or hallucinate scene details seen in earlier frames. Existing memory retrieval strategies often fail to identify informative history in dynamic manipulation scenarios. To address this limitation, we propose Mem-World, a memory-augmented multi-view action-conditioned world model. At its core, we present W-VMem, a 4D wrist-view-centered surfel-indexed memory that anchors historical observations to temporally evolving surface elements. By explicitly modeling when and where scene elements are observed, W-VMem enables geometry-aware retrieval of relevant history frames conditioned on future actions. During generation, relevant history frames are selected via surfel-based rendering and scoring, providing informative and non-redundant context for prediction. Extensive experiments show that Mem-World generates persistent rollouts in complex manipulation scenarios, enables more reliable policy evaluation than Ctrl-World, improving the Pearson correlation with real-world performance by 14.5\%, and supports effective policy improvement through synthetic data generation, increasing success rates from 58\% to 72\% on long-horizon tasks.

cs.CV↗

arXiv2026

A Thresholding Based Operator-Splitting Method for Curvature-Regularized Surface Reconstruction

Surface reconstruction from point clouds is a fundamental problem in computational geometry with broad applications in computer graphics, medical imaging, and manufacturing. In this paper, we propose an efficient method for solving a curvature-regularized surface reconstruction model. To avoid the computational cost associated with reinitialization in traditional level set methods, we represent the reconstructed surface by an indicator function. By introducing an auxiliary variable, we reformulate the original optimization problem as the computation of the steady-state solution of an initial value problem. We then develop an operator-splitting method to decompose the resulting problem into two tractable subproblems, one of which can be efficiently solved by iterative thresholding. The proposed approach combines the advantages of curvature regularization, operator splitting, and threshold dynamics. Numerical experiments show that the proposed method is computationally efficient and can accurately reconstruct sharp corners and concave features.

math.NA↗

arXiv2026

Simulation study of accelerator-based muography using the GeV-scale forward muon component at SHINE

Muography exploits the penetrating power of muons to image the interior of large dense objects, but cosmic-ray sources provide only ~1 {cm}^{-2} {min}^{-1} predominantly from above, limiting imaging speed and accessible geometries. Electron-driven muon production has recently been demonstrated with laser-wakefield accelerators, but shot-to-shot fluctuations hinder systematic studies required for quantitative accelerator muography. Using Geant4 Monte Carlo simulations, we model the full experimental setup at Shaft 2 of the Shanghai High repetition rate XFEL and Extreme light facility (SHINE), from a 3 GeV, 50 pC, 50 Hz commissioning electron beam interacting with a muon target through 25 m of beamline structures and a 3 m-thick concrete isolation wall. Approximately 0.28 effective reconstructed single-muon events per bunch, with residual kinetic energies below 1.2 GeV after traversing the wall, reach the downstream muography test area (~14 s^{-1}). The wall absorbs most charged background particles below ~1 GeV, while residual neutrons can be discriminated by their characteristic detector energy deposition. Scattering-tomography simulations show that $3\times 10^{5}$ effective muon events, accumulated in approximately 6 h at the commissioning rate, yield a Structural Similarity Index above 0.9, demonstrating the feasibility of quantitative accelerator-based muography using SHINE's electron-driven GeV-scale forward muon source. At the 8 GeV/50 kHz benchmark, the projected muon intensity exceeds $5 \times 10^{4}$ μ/s, corresponding conservatively to approximately one muon per bunch at the detector. This exceeds the cosmic-ray flux by orders of magnitude, while SHINE's superconducting linac provides a stable, controlled platform for developing electron-on-target muography.

physics.acc-ph↗

arXiv2026

Twist and strain identification in moiré heterostructures

The geometrical and electronic properties of moiré materials are highly sensitive to the twist and strain in the samples due to the moiré magnifying effect. Accurate identification of twist and strain in moiré materials is therefore essential. In this work, we establish a general framework to extract the twist and strain configurations from moiré images with either atomic or moiré scale resolution. With only moiré-wavelength information, we show that there is a continuous family of possible twist and strain configurations, each one accounting for different orientations of the moiré pattern. To estimate the most likely twist-strain configuration, we discuss additional constraints and methods involving the minimum elastic energy and the electronic spectra. The minimum elastic energy, in particular, reflects that shear configurations become much more favorable as the strain increases. As an example of the developed methodology, we discuss the formation and identification of strained triangular moiré patterns. Our framework provides a comprehensive approach to identify the twist and strain configurations in systems with moiré-scale resolution.

cond-mat.mtrl-sci↗

arXiv2026

Tunable topological narrow bands in twisted bilayer-trilayer graphene

We investigate the low-energy band structure and topology of twisted bilayer--trilayer graphene with four stacking configurations: AB--ABC, BA--ABC, AB--ABA, and BA--ABA. Using both tight-binding and continuum models, we first establish that the two approaches show good agreement in the band structure in low-energy regime. We then study the evolution of the flat bands and their valley Chern numbers as functions of twist angle, perpendicular electric field, and the self-consistent Hartree potential. At relatively large twist angles and under electric field, we find a topological transition between the narrow bands, with the total Chern number of the flat bands following the Chern number sum rules derived from the chiral-limit description. We also observe another type of topological transition when the flat bands hybridize with adjacent remote bands, where gap closing and reopening processes lead to Chern number and charge density transfer. By constructing topological phase diagrams in the space of twist angle and electric field, we show that the perpendicular electric field provides an efficient tuning knob for controlling the stability and transitions of the Chern bands. Finally, we find that the Hartree potential mainly induce weak band shifts and reshaping in the narrow bands. However, with a combination of Hartree potential and the electric fields, the narrow bands show rich topological phase diagram. Our results clarify the interplay between the stacking, twist angle and electric field in manipulating the narrow bands and their topology in twisted bilayer--trilayer graphene, and provide guidance for engineering topological narrow bands with tunable Chern numbers in realistic twisted multilayer graphene systems.

cond-mat.mes-hall↗

arXiv2026

CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention

Post-hoc sparse attention accelerates long-context prefill by routing each query to a small set of token-level interactions. Hard selection, however, assigns zero probability to every omitted chunk: a routing miss cannot be recovered, and a fixed expansion budget spends the same work on easy and ambiguous queries. We introduce Coarse-to-fine Error-aware Dynamic Attention Routing (CEDAR), a coarse-to-fine method that keeps the language model frozen while preserving global coverage. Each semantic chunk contributes a cheap key--value summary to a residual attention path; chunks with high estimated approximation error are then expanded to exact token attention. Exact and summarized contributions are combined in a single softmax normalization, so refinement replaces, rather than duplicates, coarse evidence. We derive an output-error bound governed by within-chunk key/value dispersion and use it to allocate a variable refinement budget. A controlled clustered-attention study shows that residual summaries reduce reconstruction error by more than 98% relative to hard dropping at equal exact-chunk budgets. Experiments on long-context benchmarks demonstrate that CEDAR recovers most of the quality lost by hard sparse routing while maintaining approximately $3\times$ kernel speedup at 128K context.

cs.CL↗

arXiv2026

Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning

Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressively expand the horizon outperform fixed-horizon alternatives. However, existing schedules are open-loop: they monotonically increase the horizon until a manually specified maximum, with no mechanism to detect when further expansion stops helping. We propose the effective interaction frontier hypothesis: a dynamic boundary beyond which additional interactions yield diminishing returns while cost grows linearly. We then introduce Elastic Horizon, a closed-loop controller that tracks this boundary via the 90th percentile of successful trajectory lengths. On AppWorld and BFCL, fixed-horizon sweeps reveal clear saturation plateaus; Elastic Horizon stabilizes the horizon inside the saturation band from both under- and over-capacity initializations, attains the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Our work shifts the paradigm from how to scale interaction horizons to when to stop scaling.

cs.AI↗