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Researcher profile

Yu Cheng

Yu Cheng contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceApplicationsastro-ph.GAComputation and LanguageCryptography and Securityeess.SYMachine Learningmath.STMultiagent Systemsnlin.AORoboticsStatistics TheorySystems and Control

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Published work

7 published item(s)

preprint2026arXiv

Investigating red packet fraud in Android applications: Insights from user reviews

With the popularization of smartphones, red packets have been widely used in mobile apps. However, the issues of fraud associated with them have also become increasingly prominent. As reported in user reviews from mobile app markets, many users have complained about experiencing red packet fraud and being persistently troubled by fraudulent red packets. To uncover this phenomenon, we conduct the first investigation into an extensive collection of user reviews on apps with red packets. In this paper, we first propose a novel automated approach, ReckDetector, for effectively identifying apps with red packets from app markets. We then collect over 360,000 real user reviews from 334 apps with red packets available on Google Play and three popular alternative Android app markets. We preprocess the user reviews to extract those related to red packets and fine-tune a pre-trained BERT model to identify negative reviews. Finally, based on semantic analysis, we have summarized six distinct categories of red packet fraud issues reported by users. Through our study, we found that red packet fraud is highly prevalent, significantly impacting user experience and damaging the reputation of apps. Moreover, red packets have been widely exploited by unscrupulous app developers as a deceptive incentive mechanism to entice users into completing their designated tasks, thereby maximizing their profits.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only what the environment looks like, but also how its objects are structured. Existing pipelines, however, typically represent generated content as static meshes and inherit articulation only from curated asset libraries, which limits object-level controllability and prevents new interactable assets from being produced on demand. We address this gap by formulating physically interactable indoor scene synthesis as programmatic world generation, and present SceneCode, a framework that compiles a natural language prompt into an executable, code-driven indoor world rather than a collection of opaque meshes. A room-level agentic backbone first turns the prompt into a structured house layout and emits per-object AssetRequests through a planner--designer--critic loop. Each request is then routed to one of five code-generation strategies and converted into a synthesized part-wise Blender Python programs that are validated through an execution-guided repair-and-refine loop. The resulting programs are compiled into simulation-ready assets, and exported as SDF for physics simulation. A persistent scene-state registry links object requests, executable programs, rendered geometry, and simulation assets, turning scene assembly into a traceable and locally editable world-building process. We evaluate SceneCode across scene-level synthesis, object-level asset quality, human judgment, and downstream robot interaction. Results show that executable world programs improve prompt-faithful indoor scene generation and produce assets with cleaner mesh structure, and simulator-loadable articulation metadata. Project page: https://scene-code.github.io/.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model

Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in Mamba, a state space model (SSM) with linear complexity, offer promising efficiency gains but suffer from unstable contextual learning and multitask generalization. Some works conduct layer-level hybrid structures that combine Transformer and Mamba layers, aiming to make full use of both advantages. This paper proposes TransMamba, a novel sequence-level hybrid framework that unifies Transformer and Mamba through shared parameter matrices (QKV and CBx), and thus could dynamically switch between attention and SSM mechanisms at different token lengths and layers. We design the Memory Converter to bridge Transformer and Mamba by converting attention outputs into SSM-compatible states, ensuring seamless information flow at TransPoints where the transformation happens. The TransPoint scheduling is also thoroughly explored for balancing effectiveness and efficiency. We conducted extensive experiments demonstrating that TransMamba achieves superior training efficiency and performance compared to single and hybrid baselines, and validated the deeper consistency between Transformer and Mamba paradigms at sequence level, offering a scalable solution for next-generation language modeling. Code and data are available at https://github.com/Yixing-Li/TransMamba

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Emergent Multi-View Fidelity in Autonomous UAV Swarm Sport Injury Detection

Accurate, real-time collision detection is essential for ensuring player safety and effective refereeing in high-contact sports such as rugby, particularly given the severe risks associated with traumatic brain injuries (TBI). Traditional collision-monitoring methods employing fixed cameras or wearable sensors face limitations in visibility, coverage, and responsiveness. Previously, we introduced a framework using unmanned aerial vehicles (UAVs) for monitoring and real time kinematics extraction from videos of collision events. In this paper, we show that the strategies operating on the objective of ensuring at least one UAV captures every incident on the pitch have an emergent property of fulfilling a stronger key condition for successful kinematics extraction. Namely, they ensure that almost all collisions are captured by multiple drones, establishing multi-view fidelity and redundancy, while not requiring any drone-to-drone communication.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

The JWST-NIRCam View of Sagittarius C. III. The Extinction Curve

Determining the infrared extinction curve towards the Galactic centre is crucial for accurately correcting observed data and deriving the underlying stellar populations. However, extinction curves reported in the literature often show discrepancies. We aim to derive the infrared extinction curve towards the Galactic centre based on JWST-NIRCam data for the first time, using observations of the Sagittarius C region in the 1-5 $μ$m range. We determined extinction ratios using two different methods, both based on measuring the reddening vector using the slope of red clump stars, whose intrinsic properties are well known, in observed colour-magnitude diagrams. The extinction curve derived in this work is in good agreement with previous results in the literature. We obtained the following extinction ratios relative to F162M: $A_\mathrm{F115W} : A_\mathrm{F162M} : A_\mathrm{F182M} : A_\mathrm{F212N} : A_\mathrm{F360M} : A_\mathrm{F405N} : A_\mathrm{F470N} : A_\mathrm{F480M} = 1.84 \pm 0.03 : 1.00 : 0.789 \pm 0.005 : 0.607 \pm 0.014 : 0.306 \pm 0.011 : 0.248 \pm 0.017 : 0.240 \pm 0.019 : 0.21 \pm 0.03$. Besides, we found different values of the extinction index for the short- ($λ\sim 1-2.5\,μ$m, $α\sim 2$) and long-wavelength ($λ\sim 2.5-5\,μ$m, $α\sim 1.4$) regimes, with the extinction curve flattening at longer wavelengths. Comparison with extinction curves derived both inside and outside the Galactic centre suggests that the infrared extinction curve does not significantly vary in the central regions, and shows no significant evidence for variations between different lines of sight beyond the inner Galaxy within the uncertainties.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pre-averaging fractional processes contaminated by noise, with an application to turbulence

In this article, we consider the problem of estimating fractional processes based on noisy high-frequency data. Generalizing the idea of pre-averaging to a fractional setting, we exhibit a sequence of consistent estimators for the unknown parameters of interest by proving a law of large numbers for associated variation functionals. In contrast to the semimartingale setting, the optimal window size for pre-averaging depends on the unknown roughness parameter of the underlying process. We evaluate the performance of our estimators in a simulation study and use them to empirically verify Kolmogorov's 2/3-law in turbulence data contaminated by instrument noise.

0 citations0 reviewsNo code linkedOpen access