arXiv subjects

Yichen Song

Publications and source records attributed to Yichen Song.

7 recordsLinked to original sources

arXiv2026

NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation

The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-ready scenes remains challenging due to the lack of physical grounding and scene-level interactivity in current image-to-URDF methods. We propose NeoWorld-Pro, a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments. Leveraging the zero-shot reasoning and code synthesis capabilities of MLLMs, NeoWorld-Pro converts a single RGB image into executable programs specifying object geometry, articulation, and physical properties. A physics-in-the-loop mechanism then iteratively refines the generated programs by validating their execution in a physics engine, enforcing physically plausible articulations, valid object compositions and interactions, and accurate spatial relationships. Experiments show that NeoWorld-Pro outperforms open-loop and prior monocular reconstruction methods, while enabling complex downstream tasks such as stable stacking and fine-grained manipulation.

cs.CV↗

arXiv2026

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling

Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bounded'', struggling to generalize across heterogeneous datasets.To bridge this gap, we introduce Unicorn (Universal Correlation Network), a framework for scalable, multi-dataset pretraining on high-dimensional time series. At the core of Unicorn is a latent prototype codebook that decouples correlation modeling from specific channel identities. By projecting heterogeneous channels into a shared latent space, UniCorN learns identity-agnostic, reusable interaction patterns that transfer across domains with diverse dimensionalities and semantics. Extensive experiments show that Unicorn significantly outperforms state-of-the-art forecasting architectures, particularly in few-shot transfer scenarios, offering a scalable path toward multivariate time series foundation models.

cs.LG↗

arXiv2025

Pure Exploration with Feedback Graphs

We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenarios between full-information, pure bandit feedback, and settings with no feedback on the chosen action. While variants of this problem have been investigated for regret minimization, no prior work has addressed the pure exploration setting, which is the focus of our study. We derive an instance-specific lower bound on the sample complexity of learning the best action with fixed confidence, even when the feedback graph is unknown and stochastic, and present unidentifiability results for Bernoulli rewards. Additionally, our findings reveal how the sample complexity scales with key graph-dependent quantities. Lastly, we introduce TaS-FG (Track and Stop for Feedback Graphs), an asymptotically optimal algorithm, and demonstrate its efficiency across different graph configurations.

stat.ML↗

arXiv2025

DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring

The process monitoring task is characterized by stringent demands for accuracy and efficiency. Current transformer-based methods, characterized by self-attention for temporal fusion, exhibit limitations in accurately understanding the semantic context and efficiently processing monitoring logs, rendering them inadequate for process monitoring. To address these limitations, we introduce DeepFilter, which revises the self-attention mechanism to improve both accuracy and efficiency. As a straightforward yet versatile approach, DeepFilter provides an instrumental baseline for practitioners in process monitoring, whether initiating new projects or enhancing existing capabilities.

cs.AI↗

arXiv2024

DimINO: Dimension-Informed Neural Operator Learning

In computational physics, a longstanding challenge lies in finding numerical solutions to partial differential equations (PDEs). Recently, research attention has increasingly focused on Neural Operator methods, which are notable for their ability to approximate operators-mappings between functions. Although neural operators benefit from a universal approximation theorem, achieving reliable error bounds often necessitates large model architectures, such as deep stacks of Fourier layers. This raises a natural question: Can we design lightweight models without sacrificing generalization? To address this, we introduce DimINO (Dimension-Informed Neural Operators), a framework inspired by dimensional analysis. DimINO incorporates two key components, DimNorm and a redimensionalization operation, which can be seamlessly integrated into existing neural operator architectures. These components enhance the model's ability to generalize across datasets with varying physical parameters. Theoretically, we establish a universal approximation theorem for DimINO and prove that it satisfies a critical property we term Similar Transformation Invariance (STI). Empirically, DimINO achieves up to 76.3% performance gain on PDE datasets while exhibiting clear evidence of the STI property.

cs.LG↗

arXiv2023

Safe Non-Stochastic Control of Control-Affine Systems: An Online Convex Optimization Approach

We study how to safely control nonlinear control-affine systems that are corrupted with bounded non-stochastic noise, i.e., noise that is unknown a priori and that is not necessarily governed by a stochastic model. We focus on safety constraints that take the form of time-varying convex constraints such as collision-avoidance and control-effort constraints. We provide an algorithm with bounded dynamic regret, i.e., bounded suboptimality against an optimal clairvoyant controller that knows the realization of the noise a prior. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite real-world unpredictable disturbances such as wind gusts. To develop the algorithm, we capture our problem as a sequential game between a controller and an adversary, where the controller plays first, choosing the control input, whereas the adversary plays second, choosing the noise's realization. The controller aims to minimize its cumulative tracking error despite being unable to know the noise's realization a prior. We validate our algorithm in simulated scenarios of (i) an inverted pendulum aiming to stay upright, and (ii) a quadrotor aiming to fly to a goal location through an unknown cluttered environment.

eess.SY↗

arXiv2018

Gate-tunable Room-temperature Ferromagnetism in Two-dimensional Fe$_3$GeTe$_2$

Material research has been a major driving force in the development of modern nano-electronic devices. In particular, research in magnetic thin films has revolutionized the development of spintronic devices; identifying new magnetic materials is key to better device performance and new device paradigm. The advent of two-dimensional van der Waals crystals creates new possibilities. This family of materials retain their chemical stability and structural integrity down to monolayers and, being atomically thin, are readily tuned by various kinds of gate modulation. Recent experiments have demonstrated that it is possible to obtain two-dimensional ferromagnetic order in insulating Cr$_2$Ge$_2$Te$_6$ and CrI$_3$ at low temperatures. Here, we developed a new device fabrication technique, and successfully isolated monolayers from layered metallic magnet Fe$_3$GeTe$_2$ for magnetotransport study. We found that the itinerant ferromagnetism persists in Fe$_3$GeTe$_2$ down to monolayer with an out-of-plane magnetocrystalline anisotropy. The ferromagnetic transition temperature, $T_c$, is suppressed in pristine Fe$_3$GeTe$_2$ thin flakes. An ionic gate, however, dramatically raises the $T_c$ up to room temperature, significantly higher than the bulk $T_c$ of 205 Kelvin. The gate-tunable room-temperature ferromagnetism in two-dimensional Fe$_3$GeTe$_2$ opens up opportunities for potential voltage-controlled magnetoelectronics based on atomically thin van der Waals crystals.

cond-mat.mtrl-sci↗