arXiv subjects

Chen Chen

Publications and source records attributed to Chen Chen.

At least 253 records · Page 14Linked to original sources

arXiv2025

Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations

The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale, primarily due to data fragmentation across domains and escalating infrastructure costs that hinder sustained quality improvements. To address this challenge, we propose Lattice, a recommendation framework centered around model space redesign that extends Multi-Domain, Multi-Objective (MDMO) learning beyond models and learning objectives. Lattice addresses these challenges through a comprehensive model space redesign that combines cross-domain knowledge sharing, data consolidation, model unification, distillation, and system optimizations to achieve significant improvements in both quality and cost-efficiency. Our deployment of Lattice at Meta has resulted in 10% revenue-driving top-line metrics gain, 11.5% user satisfaction improvement, 6% boost in conversion rate, with 20% capacity saving.

cs.IR↗

arXiv2025

Proximity-tuned Magnetic and Transport Anomalies in All-epitaxial Fe5-xGeTe2/WSe2 Van der Waals Heterostructures

Van der Waals (vdW) heterostructures combining two-dimensional (2D) ferromagnets and semiconducting transition-metal dichalcogenides (TMDCs) offer highly promising opportunities for tailoring 2D magnetism through interfacial proximity effects, enabling unique physical phenomena inaccessible in 3D systems and achieving functionalities beyond conventional spintronics. However, current fabrication of vdW heterostructures still relies heavily on the manual stacking of exfoliated 2D flakes, leading to critical challenges in scalability, interfacial quality, thickness control and device integration. This work reports on the realization of all-epitaxial, high-quality Fe5-xGeTe2(FGT)/WSe2 heterostructures exhibiting perpendicular magnetic anisotropy (PMA) and room-temperature ferromagnetism. The FGT/WSe2 system demonstrates temperature-driven magnetic transitions, higher-order PMA contributions and large anisotropic magnetoresistance, highlighting sublattice-specific contributions to magnetic and transport properties. Notably, the FGT/WSe2 heterostructures display unconventional physical phenomena, including thickness- and temperature-dependent sign reversal of exchange bias, a reversed thickness trend in the unconventional Hall effect, and a non-monotonic PMA-thickness dependence. These anomalies indicate pronounced interfacial contributions arising from proximity effects enhanced by epitaxial interface quality. Collectively, this study provides deep insights into the magnetic and transport properties of FGT/WSe2 vdW heterostructures, establishing a scalable platform for exploring emergent 2D physics and advancing next-generation 2D spintronic technologies.

cond-mat.mes-hall↗

arXiv2025

A Generation Framework with Strict Constraints for Crystal Materials Design

The design of crystal materials plays a critical role in areas such as new energy development, biomedical engineering, and semiconductors. Recent advances in data-driven methods have enabled the generation of diverse crystal structures. However, most existing approaches still rely on random sampling without strict constraints, requiring multiple post-processing steps to identify stable candidates with the desired physical and chemical properties. In this work, we present a new constrained generation framework that takes multiple constraints as input and enables the generation of crystal structures with specific chemical and properties. In this framework, intermediate constraints, such as symmetry information and composition ratio, are generated by a constraint generator based on large language models (LLMs), which considers the target properties. These constraints are then used by a subsequent crystal structure generator to ensure that the structure generation process is under control. Our method generates crystal structures with a probability of meeting the target properties that is more than twice that of existing approaches. Furthermore, nearly 100% of the generated crystals strictly adhere to predefined chemical composition, eliminating the risks of supply chain during production.

cs.AI↗

arXiv2025

Topological Braiding and Dynamic Probing of Phase Transitions at Temporal Interfaces in Non-Hermitian Synthetic Dimensions

Non-Hermitian systems give rise to distinct topological phenomena, yet their manifestations at temporal interfaces characterized by abrupt changes in system parameters remain largely unex plored. Upon an abrupt alteration of the Hamiltonian in a one-dimensional non-Hermitian sys tem,the ensuring temporal interface excites both reflected and refracted wave modes. By intro ducing a chiral-symmetric Hamiltonian, this study reveals the topological effects at such temporal interfaces. We find that the reflection and refraction coefficients exhibit a topological braiding struc ture. This structure is directly determined by the difference in the topological invariants across the interface, establishing a bulk-boundary correspondence for temporal interfaces in non-Hermitian systems. Furthermore, we propose a dynamical probe that leverages the geometric similarity of eigenstates at the temporal interface to detect topological phase transitions. These findings estab lish a fundamental connection between topological braiding and nonreciprocal dynamics at temporal interfaces, providing a platform to explore phase transition detection and nonreciprocal phenomena in time-varying non-Hermitian systems.

physics.optics↗

arXiv2025

ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits

Processor designs rely on iterative modifications and reuse well-established designs. However, this reuse of prior designs also leads to similar vulnerabilities across multiple processors. As processors grow increasingly complex with iterative modifications, efficiently detecting vulnerabilities from modern processors is critical. Inspired by software fuzzing, hardware fuzzing has recently demonstrated its effectiveness in detecting processor vulnerabilities. Yet, to our best knowledge, existing processor fuzzers fuzz each design individually, lacking the capability to understand known vulnerabilities in prior processors to fine-tune fuzzing to identify similar or new variants of vulnerabilities. To address this gap, we present ReFuzz, an adaptive fuzzing framework that leverages contextual bandit to reuse highly effective tests from prior processors to fuzz a processor-under-test (PUT) within a given ISA. By intelligently mutating tests that trigger vulnerabilities in prior processors, ReFuzz effectively detects similar and new variants of vulnerabilities in PUTs. ReFuzz uncovered three new security vulnerabilities and two new functional bugs. ReFuzz detected one vulnerability by reusing a test that triggers a known vulnerability in a prior processor. One functional bug exists across three processors that share design modules. The second bug has two variants. Additionally, ReFuzz reuses highly effective tests to enhance efficiency in coverage, achieving an average 511.23x coverage speedup and up to 9.33% more total coverage, compared to existing fuzzers.

cs.CR↗

arXiv2025

LLM Use for Mental Health: Crowdsourcing Users' Sentiment-based Perspectives and Values from Social Discussions

Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from "one-size-fits-all" chatbot design toward condition-specific, value-sensitive LLM design.

cs.CY↗

arXiv2025

Spatial Retrieval Augmented Autonomous Driving

Existing autonomous driving systems rely on onboard sensors (cameras, LiDAR, IMU, etc) for environmental perception. However, this paradigm is limited by the drive-time perception horizon and often fails under limited view scope, occlusion or extreme conditions such as darkness and rain. In contrast, human drivers are able to recall road structure even under poor visibility. To endow models with this ``recall" ability, we propose the spatial retrieval paradigm, introducing offline retrieved geographic images as an additional input. These images are easy to obtain from offline caches (e.g, Google Maps or stored autonomous driving datasets) without requiring additional sensors, making it a plug-and-play extension for existing AD tasks. For experiments, we first extend the nuScenes dataset with geographic images retrieved via Google Maps APIs and align the new data with ego-vehicle trajectories. We establish baselines across five core autonomous driving tasks: object detection, online mapping, occupancy prediction, end-to-end planning, and generative world modeling. Extensive experiments show that the extended modality could enhance the performance of certain tasks. We will open-source dataset curation code, data, and benchmarks for further study of this new autonomous driving paradigm.

cs.CV↗

arXiv2025

EGGS: Exchangeable 2D/3D Gaussian Splatting for Geometry-Appearance Balanced Novel View Synthesis

Novel view synthesis (NVS) is crucial in computer vision and graphics, with wide applications in AR, VR, and autonomous driving. While 3D Gaussian Splatting (3DGS) enables real-time rendering with high appearance fidelity, it suffers from multi-view inconsistencies, limiting geometric accuracy. In contrast, 2D Gaussian Splatting (2DGS) enforces multi-view consistency but compromises texture details. To address these limitations, we propose Exchangeable Gaussian Splatting (EGGS), a hybrid representation that integrates 2D and 3D Gaussians to balance appearance and geometry. To achieve this, we introduce Hybrid Gaussian Rasterization for unified rendering, Adaptive Type Exchange for dynamic adaptation between 2D and 3D Gaussians, and Frequency-Decoupled Optimization that effectively exploits the strengths of each type of Gaussian representation. Our CUDA-accelerated implementation ensures efficient training and inference. Extensive experiments demonstrate that EGGS outperforms existing methods in rendering quality, geometric accuracy, and efficiency, providing a practical solution for high-quality NVS.

cs.CV↗

arXiv2025

DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models

Current story visualization methods tend to position subjects solely by text and face challenges in maintaining artistic consistency. To address these limitations, we introduce DreamingComics, a layout-aware story visualization framework. We build upon a pretrained video diffusion-transformer (DiT) model, leveraging its spatiotemporal priors to enhance identity and style consistency. For layout-based position control, we propose RegionalRoPE, a region-aware positional encoding scheme that re-indexes embeddings based on the target layout. Additionally, we introduce a masked condition loss to further constrain each subject's visual features to their designated region. To infer layouts from natural language scripts, we integrate an LLM-based layout generator trained to produce comic-style layouts, enabling flexible and controllable layout conditioning. We present a comprehensive evaluation of our approach, showing a 29.2% increase in character consistency and a 36.2% increase in style similarity compared to previous methods, while displaying high spatial accuracy. Our project page is available at https://yj7082126.github.io/dreamingcomics/

cs.CV↗

arXiv2025

Retroreflective Optical ISAC for 6G: Technologies, Applications and Future Directions

Integrated sensing and communication (ISAC) has emerged as a key technological paradigm for sixth generation (6G) mobile networks, aiming to unify sensing and communication in a spectrally efficient and hardware lightweight manner. Radio frequency ISAC (RF-ISAC) is constrained by spectrum crowding, limited sensing resolution, and susceptibility to electromagnetic interference. In contrast, optical ISAC (O-ISAC) leverages the large bandwidth and short wavelength of optical carriers and is regarded as an important complement to RF-ISAC. However, conventional O-ISAC relies on natural optical reflections from target surfaces, which generate weak echoes that are highly dependent on surface materials, thereby limiting the achievable sensing range and sensing accuracy. This article introduces retroreflective optical ISAC (RO-ISAC), which alleviates these limitations by equipping targets with compact retroreflective modules. These modules return incident light approximately back to the source over a useful range of incidence angles, forming a well controlled double pass optical path with strong and stable echoes, and thereby further unlocking the application potential of O-ISAC. The conceptual architecture of RO-ISAC is presented together with the underlying retroreflection mechanism. Key enabling technologies for RO-ISAC systems are discussed, including channel modeling, waveform design, bidirectional transmission, and multi-target sensing and communication, with representative details and experimental validation. The suitability of RO-ISAC is analyzed in indoor, aerial, underwater, and satellite scenarios, and challenges and research directions related to mobility, cooperative networking, intelligent operation, and sustainable deployment are outlined, pointing toward robust and scalable RO-ISAC deployment in future 6G networks.

eess.SP↗

arXiv2025

LongCat-Flash-Omni Technical Report

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.

cs.MM↗

arXiv2025

TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE

Time series data is ubiquitous, with forecasting applications spanning from finance to healthcare. Beyond popular deterministic methods, generative models are gaining attention due to advancements in areas like image synthesis and video generation, as well as their inherent ability to provide probabilistic predictions. However, existing generative approaches mostly involve recurrent generative operations or repeated denoising steps, making the prediction laborious, particularly for long-term forecasting. Most of them only conduct experiments for relatively short-term forecasting, with limited comparison to deterministic methods in long-term forecasting, leaving their practical advantages unclear. This paper presents TARFVAE, a novel generative framework that combines the Transformer-based autoregressive flow (TARFLOW) and variational autoencoder (VAE) for efficient one-step generative time series forecasting. Inspired by the rethinking that complex architectures for extracting time series representations might not be necessary, we add a flow module, TARFLOW, to VAE to promote spontaneous learning of latent variables that benefit predictions. TARFLOW enhances VAE's posterior estimation by breaking the Gaussian assumption, thereby enabling a more informative latent space. TARFVAE uses only the forward process of TARFLOW, avoiding autoregressive inverse operations and thus ensuring fast generation. During generation, it samples from the prior latent space and directly generates full-horizon forecasts via the VAE decoder. With simple MLP modules, TARFVAE achieves superior performance over state-of-the-art deterministic and generative models across different forecast horizons on benchmark datasets while maintaining efficient prediction speed, demonstrating its effectiveness as an efficient and powerful solution for generative time series forecasting.

cs.LG↗

arXiv2025

GraspDiffusion: Synthesizing Realistic Whole-body Hand-Object Interaction

Recent generative models can synthesize high-quality images, but they often fail to generate humans interacting with objects using their hands. This arises mostly from the model's misunderstanding of such interactions and the hardships of synthesizing intricate regions of the body. In this paper, we propose \textbf{GraspDiffusion}, a novel generative method that creates realistic scenes of human-object interaction. Given a 3D object, GraspDiffusion constructs whole-body poses with control over the object's location relative to the human body, which is achieved by separately leveraging the generative priors for body and hand poses, optimizing them into a joint grasping pose. This pose guides the image synthesis to correctly reflect the intended interaction, creating realistic and diverse human-object interaction scenes. We demonstrate that GraspDiffusion can successfully tackle the relatively uninvestigated problem of generating full-bodied human-object interactions while outperforming previous methods. Our project page is available at https://yj7082126.github.io/graspdiffusion/

cs.CV↗

arXiv2025

EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer

Most existing spatial reasoning benchmarks focus on static or globally observable environments, failing to capture the challenges of long-horizon reasoning and memory utilization under partial observability and dynamic changes. We introduce two dynamic spatial benchmarks, locally observable maze navigation and match-2 elimination that systematically evaluate models' abilities in spatial understanding and adaptive planning when local perception, environment feedback, and global objectives are tightly coupled. Each action triggers structural changes in the environment, requiring continuous update of cognition and strategy. We further propose a subjective experience-based memory mechanism for cross-task experience transfer and validation. Experiments show that our benchmarks reveal key limitations of mainstream models in dynamic spatial reasoning and long-term memory, providing a comprehensive platform for future methodological advances. Our code and data are available at https://anonymous.4open.science/r/EvoEmpirBench-143C/.

cs.CV↗

arXiv2025

EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations

Generative models, especially text-to-image diffusion models, have significantly advanced in their ability to generate images, benefiting from enhanced architectures, increased computational power, and large-scale datasets. While the datasets play an important role, their protection has remained as an unsolved issue. Current protection strategies, such as watermarks and membership inference, are either in high poison rate which is detrimental to image quality or suffer from low accuracy and robustness. In this work, we introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage utilizing template memorization. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing and offers a pioneering perspective for unauthorized usage detection in generative models. Comprehensive experiments are provided to demonstrate its effectiveness in terms of data-alteration rate, accuracy, robustness and generation quality.

cs.CR↗

arXiv2025

Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)

Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. However, effectively extending these capabilities into multimodal contexts-where models must integrate both visual and textual inputs-continues to be a significant challenge. Multimodal reasoning introduces complexities, such as handling conflicting information across modalities, which require models to adopt advanced interpretative strategies. Addressing these challenges involves not only sophisticated algorithms but also robust methodologies for evaluating reasoning accuracy and coherence. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs. Through a thorough and up-to-date comparison, we clearly formulate core reasoning challenges and opportunities, highlighting practical methods for post-training optimization and test-time inference. Our work provides valuable insights and guidance, bridging theoretical frameworks and practical implementations, and sets clear directions for future research.

cs.CL↗

arXiv2025

Complex Instruction Following with Diverse Style Policies in Football Games

Despite advancements in language-controlled reinforcement learning (LC-RL) for basic domains and straightforward commands (e.g., object manipulation and navigation), effectively extending LC-RL to comprehend and execute high-level or abstract instructions in complex, multi-agent environments, such as football games, remains a significant challenge. To address this gap, we introduce Language-Controlled Diverse Style Policies (LCDSP), a novel LC-RL paradigm specifically designed for complex scenarios. LCDSP comprises two key components: a Diverse Style Training (DST) method and a Style Interpreter (SI). The DST method efficiently trains a single policy capable of exhibiting a wide range of diverse behaviors by modulating agent actions through style parameters (SP). The SI is designed to accurately and rapidly translate high-level language instructions into these corresponding SP. Through extensive experiments in a complex 5v5 football environment, we demonstrate that LCDSP effectively comprehends abstract tactical instructions and accurately executes the desired diverse behavioral styles, showcasing its potential for complex, real-world applications.

cs.MA↗

arXiv2025

Water induced bandgap engineering in nanoribbons of hexagonal boron nitride

Different from hexagonal boron nitride (hBN) sheets, the bandgap of hBN nanoribbons (BNNRs) can be changed by spatial/electrostatic confinement. It has been predicted that a transverse electric field can narrow the bandgap and even cause an insulator-metal transition in BNNRs. However, experimentally introducing an overhigh electric field across the BNNR remains challenging. Here, we theoretically and experimentally demonstrate that water adsorption greatly reduces bandgap of zigzag oriented BNNRs (zBNNRs). Ab initio calculations show that water adsorbed beside the BNNR induces a transverse equivalent electric field of over 2 V/nm thereby reducing its bandgap. Field effect transistors were successfully fabricated from zBNNRs with different widths. The conductance of zBNNRs with adsorbates of water could be tuned over 3 orders in magnitude via electrical field modulation at room temperature. Furthermore, photocurrent response measurements were taken to determine the optical bandgap in zBNNR. Wider zBNNRs exhibit a bandgap down to 1.17 eV. This study yields fundamental insights in new routes toward realizing electronic/optoelectronic devices and circuits based on hexagonal boron nitride.

cond-mat.mtrl-sci↗