arXiv subjects

Kai Liu

Publications and source records attributed to Kai Liu.

At least 127 records · Page 7Linked to original sources

arXiv2025

UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection

Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces, which serialize the 3D voxels into the flattened 1D sequence for iterative self-attention. However, the spatial structure of 3D voxels will be inevitably destroyed during the serialization process. Besides, due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field. Inspired by the impressive performance of State Space Models (SSM) achieved in the field of 2D vision tasks, in this paper, we propose a novel Unified Mamba (UniMamba), which seamlessly integrates the merits of 3D convolution and SSM in a concise multi-head manner, aiming to perform "local and global" spatial context aggregation efficiently and simultaneously. Specifically, a UniMamba block is designed which mainly consists of spatial locality modeling, complementary Z-order serialization and local-global sequential aggregator. The spatial locality modeling module integrates 3D submanifold convolution to capture the dynamic spatial position embedding before serialization. Then the efficient Z-order curve is adopted for serialization both horizontally and vertically. Furthermore, the local-global sequential aggregator adopts the channel grouping strategy to efficiently encode both "local and global" spatial inter-dependencies using multi-head SSM. Additionally, an encoder-decoder architecture with stacked UniMamba blocks is formed to facilitate multi-scale spatial learning hierarchically. Extensive experiments are conducted on three popular datasets: nuScenes, Waymo and Argoverse 2. Particularly, our UniMamba achieves 70.2 mAP on the nuScenes dataset.

cs.CV↗

arXiv2025

Tequila: Trapping-free Ternary Quantization for Large Language Models

Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication that lacks efficient hardware support, making it not feasible. Ternary weight quantization addresses this by constraining weights to {-1, 0, 1}, replacing expensive multiplications with hardware-efficient additions. However, such aggressive compression leads to significant accuracy degradation, even after costly quantization-aware training with massive data. We identify the core issue as deadzone trapping: a large number of weights are trapped at the deadzone boundary. This occurs because these weights receive only noisy, uninformative gradients, preventing stable escape from the deadzone and severely impeding model capacity and optimization. To address this issue, we propose Tequila, a trapping-free quantization optimization method that reactivates deadzone-trapped weights by repurposing them as dynamic biases. This allows the repurposed weights to provide a continuous signal in the forward pass and, critically, receive direct, meaningful gradient signals during backpropagation, thereby enhancing model capacity and optimization with nearly zero inference overhead. Extensive evaluations demonstrate that Tequila outperforms state-of-the-art (SOTA) ternary quantization methods across five benchmarks. Specifically, on the ARC benchmark, it achieves >4% accuracy gain over the SOTA baseline, nearly matching full-precision performance (within <1% gap) with a 3.0x inference speedup. Consequently, Tequila offers a highly practical and efficient implementation for the deployment of advanced LLMs in resource-constrained environments. The code is available at https://github.com/Tencent/AngelSlim.

cs.LG↗

arXiv2025

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom

Large vision-language models (LVLMs) have witnessed significant progress on visual understanding tasks. However, they often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. To tackle this issue, we first identify the drawbacks of existing solutions (i.e., limited multi-modal reasoning capacities, and insufficient and irrelevant visual descriptions). We then decompose visual reasoning process into two stages: proactive visual perception (i.e., eyesight) and textual reasoning (i.e., wisdom), and introduce a novel visual reasoning framework named ProReason. This framework features decoupled vision-reasoning capabilities and multi-run proactive perception. Briefly, given a multi-modal question, ProReason iterates proactive information collection and reasoning until the answer can be concluded with necessary and sufficient visual descriptions. Notably, the disassociation of capabilities allows seamless integration of existing large language models (LLMs) to compensate for the reasoning deficits of LVLMs. Our extensive experiments demonstrate that ProReason outperforms existing multi-step reasoning frameworks on various benchmarks for both open-source and closed-source models, with the average performance gain reaching 13.2%. Besides, the integration of LLMs allows ProReason to produce high-quality visual reasoning data, which empowers ProReason-distilled models (i.e., ProReason-VL and ProReason-Q3) to achieve superior performance in downstream tasks. Our insights into existing solutions and the decoupled perspective for feasible integration of LLMs illuminate future research on visual reasoning techniques, especially LLM-assisted ones.

cs.CV↗

arXiv2025

CLQ: Cross-Layer Guided Orthogonal-based Quantization for Diffusion Transformers

Visual generation quality has been greatly promoted with the rapid advances in diffusion transformers (DiTs), which is attributed to the scaling of model size and complexity. However, these attributions also hinder the practical deployment of DiTs on edge devices, limiting their development and application. Serve as an efficient model compression technique, model post-training quantization (PTQ) can reduce the memory consumption and speed up the inference, with inevitable performance degradation. To alleviate the degradation, we propose CLQ, a cross-layer guided orthogonal-based quantization method for DiTs. To be specific, CLQ consists of three key designs. First, we observe that the calibration data used by most of the PTQ methods can not honestly represent the distribution of the activations. Therefore, we propose cross-block calibration (CBC) to obtain accurate calibration data, with which the quantization can be better guided. Second, we propose orthogonal-based smoothing (OBS), which quantifies the outlier score of each channel and leverages block Hadamard matrix to smooth the outliers with negligible overhead. Third, we propose cross-layer parameter searching (CLPS) to search. We evaluate CLQ with both image generation and video generation models and successfully compress the model into W4A4 with negligible degradation in visual quality and metrics. CLQ achieves 3.98x memory saving and 3.95x speedup. Our code is available at \hyperlink{https://github.com/Kai-Liu001/CLQ}{https://github.com/Kai-Liu001/CLQ}.

cs.CV↗

arXiv2025

Augmented Reality Indoor Wayfinding in Hospital Environments An Empirical Study on Navigation Efficiency, User Experience, and Cognitive Load

Hospitals are among the most cognitively demanding indoor environments, especially for patients and visitors unfamiliar with their layout. This study investigates the effectiveness of an augmented reality (AR)-based handheld navigation system compared to traditional paper maps in a large hospital setting. Through a mixed-methods experiment with 32 participants, we measured navigation performance, cognitive workload (NASA-TLX), situational anxiety (STAI-State), spatial behavior, and user satisfaction. Results show that AR users completed navigation tasks significantly faster, made fewer errors, and reported lower anxiety and workload. However, paper map users demonstrated stronger spatial memory in sketch-based recall tasks, highlighting a trade-off between real-time efficiency and long-term spatial learning. We discuss implications for inclusive AR design, spatial cognition, and healthcare accessibility, offering actionable design strategies for adaptive indoor navigation tools.

cs.HC↗

arXiv2025

Superconductivity in cubic La3Al with interstitial anionic electrons

We report the observation of superconductivity in cubic La3Al single crystal. It shows a metallic behavior at a normal state without observable structural transition and enters the superconducting state below Tc ~ 6.32 K. Detailed characterizations and analysis indicate that cubic La3Al is a bulk type-II BCS superconductor. Moreover, theoretical calculations show that it can host interstitial anionic electrons, which are located at the body center of cubic unit cell, and confirm the electron-phonon coupling as the superconducting mechamism. Thus, cubic La3Al can be regarded as an novel electride superconductor.

cond-mat.supr-con↗

arXiv2025

Reinforcement Learning on Pre-Training Data

The growing disparity between the exponential scaling of computational resources and the finite growth of high-quality text data now constrains conventional scaling approaches for large language models (LLMs). To address this challenge, we introduce Reinforcement Learning on Pre-Training data (RLPT), a new training-time scaling paradigm for optimizing LLMs. In contrast to prior approaches that scale training primarily through supervised learning, RLPT enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL). While existing RL strategies such as reinforcement learning from human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR) rely on human annotation for reward construction, RLPT eliminates this dependency by deriving reward signals directly from pre-training data. Specifically, it adopts a next-segment reasoning objective, rewarding the policy for accurately predicting subsequent text segments conditioned on the preceding context. This formulation allows RL to be scaled on pre-training data, encouraging the exploration of richer trajectories across broader contexts and thereby fostering more generalizable reasoning skills. Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of RLPT. For example, when applied to Qwen3-4B-Base, RLPT yields absolute improvements of $3.0$, $5.1$, $8.1$, $6.0$, $6.6$, and $5.3$ on MMLU, MMLU-Pro, GPQA-Diamond, KOR-Bench, AIME24, and AIME25, respectively. The results further demonstrate favorable scaling behavior, suggesting strong potential for continued gains with more compute. In addition, RLPT provides a solid foundation, extending the reasoning boundaries of LLMs and enhancing RLVR performance.

cs.CL↗

arXiv2025

Evolution of ultra-flat band in van der Waals kagome semiconductor Pd3P2(S1-xSex)8

We investigate the evolutions of structural parameters, optical properties, and electronic structures of van der Waals kagome semiconductor Pd3P2S8 with Se doping. When the doping level of Se increases, the bandgaps of Pd3P2(S1-xSex)8 single crystals decrease gradually, accompanying with the expanded unit cells. The first-principles calculations show that there is a flat band (FB) near the Fermi level in bulk Pd3P2S8. This FB mainly originates from the dz2-like orbitals of Pd atoms in the Pd kagome lattice, which has a finite interlayer electron hopping perpendicular to the PdS4 square plane. The interlayer hopping can be reinforced with the Se doping, inducing a stronger interlayer coupling via the chalcogen atoms at apical sites, which reduces the bandgap and enhances the cleavage energy. In contrast, the vanishing interlayer hopping in the two-dimensional limit results in the formation of ultra-FB in the monolayers of these compounds. The easy exfoliation and the existence of unique ultra-FB near EF make Pd3P2(S1-xSex)8 a model system to explore the exotic physics of FB in two-dimensional kagome lattice.

cond-mat.mtrl-sci↗

arXiv2025

Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation

The creation of high-quality 3D assets, a cornerstone of modern game development, has long been characterized by labor-intensive and specialized workflows. This paper presents Hunyuan3D Studio, an end-to-end AI-powered content creation platform designed to revolutionize the game production pipeline by automating and streamlining the generation of game-ready 3D assets. At its core, Hunyuan3D Studio integrates a suite of advanced neural modules (such as Part-level 3D Generation, Polygon Generation, Semantic UV, etc.) into a cohesive and user-friendly system. This unified framework allows for the rapid transformation of a single concept image or textual description into a fully-realized, production-quality 3D model complete with optimized geometry and high-fidelity PBR textures. We demonstrate that assets generated by Hunyuan3D Studio are not only visually compelling but also adhere to the stringent technical requirements of contemporary game engines, significantly reducing iteration time and lowering the barrier to entry for 3D content creation. By providing a seamless bridge from creative intent to technical asset, Hunyuan3D Studio represents a significant leap forward for AI-assisted workflows in game development and interactive media.

cs.CV↗

arXiv2025

Movable Antenna-Enhanced Secure Communication: Opportunities, Challenges, and Solutions

The broadcast nature of wireless communication renders it inherently vulnerable to security threats such as jamming and eavesdropping. While traditional array beamforming techniques help to mitigate these threats, they usually incur high hardware and processing costs, particularly in large-scale arrays with fixed-position antennas (FPAs). In contrast, movable antenna (MA) arrays can fully exploit the channel variation in spatial regions by enabling flexible antenna movement, which has emerged as a promising technology for secure communications. This article provides a magazine-type overview of MA-aided secure communications. Specifically, we first illuminate the promising application scenarios for MA-enhanced secure communication systems. Then, we examine the security advantages of MAs over conventional FPA systems, fundamentally stemming from their ability to adjust channel correlations between legitimate users, eavesdroppers, and jammers. Furthermore, we discuss important technical challenges and their potential solutions related to MA hardware architecture, channel acquisition, and antenna position optimization to realize secure transmissions. Finally, several promising directions for MA-aided secure communications are presented to inspire future research.

cs.IT↗

arXiv2025

Movable Antenna Empowered Secure Near-Field MIMO Communications

This paper investigates movable antenna (MA) empowered secure transmission in near-field multiple-input multiple-output (MIMO) communication systems, where the base station (BS) equipped with an MA array transmits confidential information to a legitimate user under the threat of a potential eavesdropper. To enhance physical layer security (PLS) of the considered system, we aim to maximize the secrecy rate by jointly designing the hybrid digital and analog beamformers, as well as the positions of MAs at the BS. To solve the formulated non-convex problem with highly coupled variables, an alternating optimization (AO)-based algorithm is introduced by decoupling the original problem into two separate subproblems. Specifically, for the subproblem of designing hybrid beamformers, a semi-closed-form solution for the fully-digital beamformer is first derived by a weighted minimum mean-square error (WMMSE)-based algorithm. Subsequently, the digital and analog beamformers are determined by approximating the fully-digital beamformer through the manifold optimization (MO) technique. For the MA positions design subproblem, we utilize the majorization-minimization (MM) algorithm to iteratively optimize each MA's position while keeping others fixed. Extensive simulation results validate the considerable benefits of the proposed MA-aided near-field beam focusing approach in enhancing security performance compared to the traditional far-field and/or the fixed position antenna (FPA)-based systems. In addition, the proposed scheme can realize secure transmission even if the eavesdropper is located in the same direction as the user and closer to the BS.

cs.IT↗

arXiv2025

Intern-S1: A Scientific Multimodal Foundation Model

In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that of closed-source models. However, in high-value but more challenging scientific professional fields, either the fields still rely on expert models, or the progress of general foundation models lags significantly compared to those in popular areas, far from sufficient for transforming scientific research and leaving substantial gap between open-source models and closed-source models in these scientific domains. To mitigate this gap and explore a step further toward Artificial General Intelligence (AGI), we introduce Intern-S1, a specialized generalist equipped with general understanding and reasoning capabilities with expertise to analyze multiple science modal data. Intern-S1 is a multimodal Mixture-of-Experts (MoE) model with 28 billion activated parameters and 241 billion total parameters, continually pre-trained on 5T tokens, including over 2.5T tokens from scientific domains. In the post-training stage, Intern-S1 undergoes offline and then online reinforcement learning (RL) in InternBootCamp, where we propose Mixture-of-Rewards (MoR) to synergize the RL training on more than 1000 tasks simultaneously. Through integrated innovations in algorithms, data, and training systems, Intern-S1 achieved top-tier performance in online RL training. On comprehensive evaluation benchmarks, Intern-S1 demonstrates competitive performance on general reasoning tasks among open-source models and significantly outperforms open-source models in scientific domains, surpassing closed-source state-of-the-art models in professional tasks, such as molecular synthesis planning, reaction condition prediction, predicting thermodynamic stabilities for crystals. Our models are available at https://huggingface.co/internlm/Intern-S1.

cs.LG↗

arXiv2025

A novel semi-analytical multiple invariants-preserving integrator for conservative PDEs

Many conservative partial differential equations such as the Korteweg-de Vries (KdV) equation, and the nonlinear Schrödinger equations, the Klein-Gordon equation have more than one invariant functionals. In this paper, we propose the definition of the discrete variational derivative, based on which, a novel semi-analytical multiple invariants-preserving integrator for the conservative partial differential equations is constructed by projection technique. The proposed integrators are shown to have the same order of accuracy as the underlying integrators. For applications, some concrete mass-momentum-energy-preserving integrators are derived for the KdV equation.

math.NA↗

arXiv2025

HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels

Creating immersive and playable 3D worlds from texts or images remains a fundamental challenge in computer vision and graphics. Existing world generation approaches typically fall into two categories: video-based methods that offer rich diversity but lack 3D consistency and rendering efficiency, and 3D-based methods that provide geometric consistency but struggle with limited training data and memory-inefficient representations. To address these limitations, we present HunyuanWorld 1.0, a novel framework that combines the best of both worlds for generating immersive, explorable, and interactive 3D scenes from text and image conditions. Our approach features three key advantages: 1) 360° immersive experiences via panoramic world proxies; 2) mesh export capabilities for seamless compatibility with existing computer graphics pipelines; 3) disentangled object representations for augmented interactivity. The core of our framework is a semantically layered 3D mesh representation that leverages panoramic images as 360° world proxies for semantic-aware world decomposition and reconstruction, enabling the generation of diverse 3D worlds. Extensive experiments demonstrate that our method achieves state-of-the-art performance in generating coherent, explorable, and interactive 3D worlds while enabling versatile applications in virtual reality, physical simulation, game development, and interactive content creation.

cs.CV↗

arXiv2025

Differentiated Information Mining: A Semi-supervised Learning Framework for GNNs

In semi-supervised learning (SSL) for enhancing the performance of graph neural networks (GNNs) with unlabeled data, introducing mutually independent decision factors for cross-validation is regarded as an effective strategy to alleviate pseudo-label confirmation bias and training collapse. However, obtaining such factors is challenging in practice: additional and valid information sources are inherently scarce, and even when such sources are available, their independence from the original source cannot be guaranteed. To address this challenge, In this paper we propose a Differentiated Factor Consistency Semi-supervised Framework (DiFac), which derives differentiated factors from a single information source and enforces their consistency. During pre-training, the model learns to extract these factors; in training, it iteratively removes samples with conflicting factors and ranks pseudo-labels based on the shortest stave principle, selecting the top candidate samples to reduce overconfidence commonly observed in confidence-based or ensemble-based methods. Our framework can also incorporate additional information sources. In this work, we leverage the large multimodal language model to introduce latent textual knowledge as auxiliary decision factors, and we design a accountability scoring mechanism to mitigate additional erroneous judgments introduced by these auxiliary factors. Experiments on multiple benchmark datasets demonstrate that DiFac consistently improves robustness and generalization in low-label regimes, outperforming other baseline methods.

cs.LG↗

arXiv2025

CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge

Unit testing plays a pivotal role in the software development lifecycle, as it ensures code quality. However, writing high-quality unit tests remains a time-consuming task for developers in practice. More recently, the application of large language models (LLMs) in automated unit test generation has demonstrated promising results. Existing approaches primarily focus on interpreted programming languages (e.g., Java), while mature solutions tailored to compiled programming languages like C++ are yet to be explored. The intricate language features of C++, such as pointers, templates, and virtual functions, pose particular challenges for LLMs in generating both executable and high-coverage unit tests. To tackle the aforementioned problems, this paper introduces CITYWALK, a novel LLM-based framework for C++ unit test generation. CITYWALK enhances LLMs by providing a comprehensive understanding of the dependency relationships within the project under test via program analysis. Furthermore, CITYWALK incorporates language-specific knowledge about C++ derived from project documentation and empirical observations, significantly improving the correctness of the LLM-generated unit tests. We implement CITYWALK by employing the widely popular LLM GPT-4o. The experimental results show that CITYWALK outperforms current state-of-the-art approaches on a collection of ten popular C++ projects. Our findings demonstrate the effectiveness of CITYWALK in generating high-quality C++ unit tests.

cs.SE↗

arXiv2025

Curvature induced modifications of chirality and magnetic configuration in perpendicular magnetized films

Designing curvature in three-dimensional (3D) magnetic nanostructures enables controlled manipulation of local energy landscapes, allowing for the modification of noncollinear spin textures relevant for next-generation spintronic devices. In this study, we experimentally investigate 3D magnetization textures in a Co/Pd multilayer film, exhibiting strong perpendicular magnetic anisotropy (PMA), deposited onto curved Cu nanowire meshes with diameters as small as 50nm and lengths of several microns. Utilizing magnetic soft X-ray nanotomography, we achieve reconstructions of 3D magnetic domain patterns at approximately 30nm spatial resolution. This approach provides detailed information on both the orientation and magnitude of magnetization within the film. Our results reveal that interfacial anisotropy in the Co/Pd multilayers drives the magnetization towards the local surface normal. In contrast to typical labyrinth domains observed in planar films, the presence of curved nanowires significantly alters the domain structure, with domains preferentially aligning along the nanowire axis in close proximity, while adopting random orientations farther away. We report direct experimental observation of a curvature-induced Dzyaloshinskii-Moriya interaction (DMI), which is quantified to be approximately one-third of the intrinsic DMI in Co/Pd stacks. The curvature induced DMI enhances stability of Neel-type domain walls. These experimental observations are further supported by micromagnetic simulations. Altogether, our findings demonstrate that introducing curvature into magnetic nanostructures provides a powerful strategy for tailoring complex magnetic behaviors, paving the way for the design of advanced 3D racetrack memory and neuromorphic computing devices.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous charge density wave in altermagnetism

Exploring the intricate interplay between magnetism and charge density waves has long been a fundamental pursuit at the forefront of condensed matter research. In this letter, based on symmetry analysis and first-principles calculations, we propose for the first time that anomalous charge density wave can be realized in two-dimensional altermagnetic WO. The anomalous charge density wave is characterized by three key features: (i) Unlike conventional charge density wave, whose stabilization is driven by the opening of a gap near the Fermi level, the anomalous charge density wave is stabilized by the occupied states with energies shifting lower far away from the Fermi level; (ii) the anomalous charge density wave increases the density of states near the Fermi level and then enhances-rather than diminishes-the metallicity of materials; (iii) altermagnetism plays a crucial role in stabilizing anomalous charge density wave. Thus, our work offers a pathway for exploring both the realization and the underlying mechanisms of anomalous charge density waves in magnetic systems.

cond-mat.mtrl-sci↗