OpenRaiser/Intern-Atlas huggingface dataset:
Motion Anything: Any to Motion Generation
Venue: arXiv cs.CV 2025
Year: 2025

Conditional motion generation has been extensively studied in computer vision, yet two critical challenges remain. First, while masked autoregressive methods have recently outperformed diffusion-based approaches, existing masking models lack a mechanism to prioritize dynamic frames and body parts based on given conditions. Second, existing methods for different conditioning modalities often fail to integrate multiple modalities effectively, limiting control and coherence in generated motion. To address these challenges, we propose Motion Anything, a multimodal motion generation framework that introduces an Attention-based Mask Modeling approach, enabling fine-grained spatial and temporal control over key frames and actions. Our model adaptively encodes multimodal conditions, including text and music, improving controllability. Additionally, we introduce Text-Music-Dance (TMD), a new motion dataset consisting of 2,153 pairs of text, music, and dance, making it twice the size of AIST++, thereby filling a critical gap in the community. Extensive experiments demonstrate that Motion Anything surpasses state-of-the-art methods across multiple benchmarks, achieving a 15% improvement in FID on HumanML3D and showing consistent performance gains on AIST++ and TMD. See our project website https://steve-zeyu-zhang.github.io/MotionAnything
SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have achieved remarkable progress in image and video stylization. However, most existing methods focus on single-style transfer, while video stylization involving multiple styles necessitates seamless transitions between them. We refer to this smooth style transition between video frames as video style morphing. Current approaches often generate stylized video frames with discontinuous structures and abrupt style changes when handling such transitions. To address these limitations, we introduce SOYO, a novel diffusion-based framework for video style morphing. Our method employs a pre-trained text-to-image diffusion model without fine-tuning, combining attention injection and AdaIN to preserve structural consistency and enable smooth style transitions across video frames. Moreover, we notice that applying linear equidistant interpolation directly induces imbalanced style morphing. To harmonize across video frames, we propose a novel adaptive sampling scheduler operating between two style images. Extensive experiments demonstrate that SOYO outperforms existing methods in open-domain video style morphing, better preserving the structural coherence of video frames while achieving stable and smooth style transitions.
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' perspective, and also lack the explainability of the results of LLM agents' code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation's automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three different level inputs of natural languages or code skeletons. ProjectEval can evaluate the generated projects by user interaction simulation for execution, and by code similarity through existing objective indicators. Through ProjectEval, we find that systematic engineering project code, overall understanding of the project and comprehensive analysis capability are the keys for LLM agents to achieve practical projects. Our findings and benchmark provide valuable insights for developing more effective programming agents that can be deployed in future real-world production.
Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness
Venue: arXiv cs.CV 2025
Year: 2025

Incremental learning (IL) aims to overcome catastrophic forgetting of previous tasks while learning new ones. Existing IL methods make strong assumptions that the incoming task type will either only increases new classes or domains (i.e. Class IL, Domain IL), or increase by a static scale in a class- and domain-agnostic manner (i.e. Versatile IL (VIL)), which greatly limit their applicability in the unpredictable and dynamic wild. In this work, we investigate $\textbf{Universal Incremental Learning (UIL)}$, where a model neither knows which new classes or domains will increase along sequential tasks, nor the scale of the increments within each task. This uncertainty prevents the model from confidently learning knowledge from all task distributions and symmetrically focusing on the diverse knowledge within each task distribution. Consequently, UIL presents a more general and realistic IL scenario, making the model face confusion arising from inter-task and intra-task distribution randomness. To $\textbf{Mi}$tigate both $\textbf{Co}$nfusion, we propose a simple yet effective framework for UIL, named $\textbf{MiCo}$. At the inter-task distribution level, we employ a multi-objective learning scheme to enforce accurate and deterministic predictions, and its effectiveness is further enhanced by a direction recalibration module that reduces conflicting gradients. Moreover, at the intra-task distribution level, we introduce a magnitude recalibration module to alleviate asymmetrical optimization towards imbalanced class distribution. Extensive experiments on three benchmarks demonstrate the effectiveness of our method, outperforming existing state-of-the-art methods in both the UIL scenario and the VIL scenario. Our code will be available at $\href{https://github.com/rolsheng/UIL}{here}$.
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion Transformers (DiTs) are a powerful yet underexplored class of generative models compared to U-Net-based diffusion architectures. We propose TIDE-Temporal-aware sparse autoencoders for Interpretable Diffusion transformErs-a framework designed to extract sparse, interpretable activation features across timesteps in DiTs. TIDE effectively captures temporally-varying representations and reveals that DiTs naturally learn hierarchical semantics (e.g., 3D structure, object class, and fine-grained concepts) during large-scale pretraining. Experiments show that TIDE enhances interpretability and controllability while maintaining reasonable generation quality, enabling applications such as safe image editing and style transfer.
Comparing User Activity on X and Mastodon
Venue: arXiv cs.SI 2025
Year: 2025

The "Fediverse", a federation of decentralized social media servers, has emerged after a decade in which centralized platforms like X (formerly Twitter) have dominated the landscape. The structure of a federation should affect user activity, as a user selects a server to access the Fediverse and posts are distributed along the structure. This paper reports on the differences in user activity between Twitter and Mastodon, a prominent example of decentralized social media. The target of the analysis is Japanese posts because both Twitter and Mastodon are actively used especially in Japan. Our findings include a larger number of replies on Twitter, more consistent user engagement on mstdn.jp, and different topic preferences on each server.
XR-VLM: Cross-Relationship Modeling with Multi-part Prompts and Visual Features for Fine-Grained Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have demonstrated impressive performance on various visual tasks, yet they still require adaptation on downstream tasks to achieve optimal performance. Recently, various adaptation technologies have been proposed, but we observe they often underperform in fine-grained visual recognition, which requires models to capture subtle yet discriminative features to distinguish similar sub-categories. Current adaptation methods typically rely on an alignment-based prediction framework, \ie the visual feature is compared with each class prompt for similarity calculation as the final prediction, which lacks class interaction during the forward pass. Besides, learning single uni-modal feature further restricts the model's expressive capacity. Therefore, we propose a novel mechanism, XR-VLM, to discover subtle differences by modeling cross-relationships, which specifically excels in scenarios involving multiple features. Our method introduces a unified multi-part visual feature extraction module designed to seamlessly integrate with the diverse backbones inherent in VLMs. Additionally, we develop a multi-part prompt learning module to capture multi-perspective descriptions of sub-categories. To further enhance discriminative capability, we propose a cross relationship modeling pattern that combines visual feature with all class prompt features, enabling a deeper exploration of the relationships between these two modalities. Extensive experiments have been conducted on various fine-grained datasets, and the results demonstrate that our method achieves significant improvements compared to current state-of-the-art approaches. Code will be released.
Symplectic Optimization for Cross Subcarrier Precoder Design with Channel Smoothing in Massive MIMO-OFDM System
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we propose a cross subcarrier precoder design (CSPD) for massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The aim is to maximize the weighted sum-rate (WSR) performance while considering the smoothness of the frequency domain effective channel. To quantify the smoothness of the effective channel, we introduce a delay indicator function to measure the large delay components of the effective channel. An optimization problem is then formulated to balance the WSR performance and the delay indicator function. By appropriately selecting the weight factors in the objective function and the parameters in the delay indicator function, the delay spread of the effective channel can be reduced, thereby enhancing the smoothness of the effective channel. To solve the optimization problem, we apply the symplectic optimization, which achieves faster convergence compared to the gradient descent methods. Simulation results indicate that the proposed algorithm achieves satisfying WSR performance while maintaining the smoothness of the effective channel.
Correctness Learning: Deductive Verification Guided Learning for Human-AI Collaboration
Venue: arXiv cs.AI 2025
Year: 2025

Despite significant progress in AI and decision-making technologies in safety-critical fields, challenges remain in verifying the correctness of decision output schemes and verification-result driven design. We propose correctness learning (CL) to enhance human-AI collaboration integrating deductive verification methods and insights from historical high-quality schemes. The typical pattern hidden in historical high-quality schemes, such as change of task priorities in shared resources, provides critical guidance for intelligent agents in learning and decision-making. By utilizing deductive verification methods, we proposed patten-driven correctness learning (PDCL), formally modeling and reasoning the adaptive behaviors-or 'correctness pattern'-of system agents based on historical high-quality schemes, capturing the logical relationships embedded within these schemes. Using this logical information as guidance, we establish a correctness judgment and feedback mechanism to steer the intelligent decision model toward the 'correctness pattern' reflected in historical high-quality schemes. Extensive experiments across multiple working conditions and core parameters validate the framework's components and demonstrate its effectiveness in improving decision-making and resource optimization.
Explainable Android Malware Detection and Malicious Code Localization Using Graph Attention
Venue: arXiv cs.CR 2025
Year: 2025

With the escalating threat of malware, particularly on mobile devices, the demand for effective analysis methods has never been higher. While existing security solutions, including AI-based approaches, offer promise, their lack of transparency constraints the understanding of detected threats. Manual analysis remains time-consuming and reliant on scarce expertise. To address these challenges, we propose a novel approach called XAIDroid that leverages graph neural networks (GNNs) and graph attention mechanisms for automatically locating malicious code snippets within malware. By representing code as API call graphs, XAIDroid captures semantic context and enhances resilience against obfuscation. Utilizing the Graph Attention Model (GAM) and Graph Attention Network (GAT), we assign importance scores to API nodes, facilitating focused attention on critical information for malicious code localization. Evaluation on synthetic and real-world malware datasets demonstrates the efficacy of our approach, achieving high recall and F1-score rates for malicious code localization. The successful implementation of automatic malicious code localization enhances the scalability, interpretability, and reliability of malware analysis.
Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms
Venue: arXiv cs.LG 2025
Year: 2025

Recent years have seen significant advancements in foundation models through generative pre-training, yet algorithmic innovation in this space has largely stagnated around autoregressive models for discrete signals and diffusion models for continuous signals. This stagnation creates a bottleneck that prevents us from fully unlocking the potential of rich multi-modal data, which in turn limits the progress on multimodal intelligence. We argue that an inference-first perspective, which prioritizes scaling efficiency during inference time across sequence length and refinement steps, can inspire novel generative pre-training algorithms. Using Inductive Moment Matching (IMM) as a concrete example, we demonstrate how addressing limitations in diffusion models' inference process through targeted modifications yields a stable, single-stage algorithm that achieves superior sample quality with over an order of magnitude greater inference efficiency.
DeFine: A Decomposed and Fine-Grained Annotated Dataset for Long-form Article Generation
Venue: arXiv cs.CL 2025
Year: 2025

Long-form article generation (LFAG) presents challenges such as maintaining logical consistency, comprehensive topic coverage, and narrative coherence across extended articles. Existing datasets often lack both the hierarchical structure and fine-grained annotation needed to effectively decompose tasks, resulting in shallow, disorganized article generation. To address these limitations, we introduce DeFine, a Decomposed and Fine-grained annotated dataset for long-form article generation. DeFine is characterized by its hierarchical decomposition strategy and the integration of domain-specific knowledge with multi-level annotations, ensuring granular control and enhanced depth in article generation. To construct the dataset, a multi-agent collaborative pipeline is proposed, which systematically segments the generation process into four parts: Data Miner, Cite Retreiver, Q&A Annotator and Data Cleaner. To validate the effectiveness of DeFine, we designed and tested three LFAG baselines: the web retrieval, the local retrieval, and the grounded reference. We fine-tuned the Qwen2-7b-Instruct model using the DeFine training dataset. The experimental results showed significant improvements in text quality, specifically in topic coverage, depth of information, and content fidelity. Our dataset publicly available to facilitate future research.
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
Venue: arXiv cs.CV 2025
Year: 2025

Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely related to pathological features such as disease subtypes, it may be valuable to augment image representation with pathological information. However, there are no attempts to leverage ST for image recognition ({\it i.e,} patch-level classification of subtypes of pathological image.). One of the big challenges is significant batch effects in spatial transcriptomics that make it difficult to extract pathological features of images from ST. In this paper, we propose a batch-agnostic contrastive learning framework that can extract consistent signals from gene expression of ST in multiple patients. To extract consistent signals from ST, we utilize the batch-agnostic gene encoder that is trained in a variational inference manner. Experiments demonstrated the effectiveness of our framework on a publicly available dataset. Code is publicly available at https://github.com/naivete5656/TPIRBAE
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion
Venue: arXiv cs.CV 2025
Year: 2025

Accurate depth and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular depth estimation of endoscopic scenes via self-supervised learning (SSL), no prior work has explored their use for pose estimation. These methods rely on low rank-based adaptation approaches, which constrain model updates to a low-rank space. We propose Endo-FASt3r, the first monocular SSL depth and pose estimation framework that uses foundation models for both tasks. We extend the Reloc3r relative pose estimation foundation model by designing Reloc3rX, introducing modifications necessary for convergence in SSL. We also present DoMoRA, a novel adaptation technique that enables higher-rank updates and faster convergence. Experiments on the SCARED dataset show that Endo-FASt3r achieves a substantial $10\%$ improvement in pose estimation and a $2\%$ improvement in depth estimation over prior work. Similar performance gains on the Hamlyn and StereoMIS datasets reinforce the generalisability of Endo-FASt3r across different datasets.
Cross-Lingual IPA Contrastive Learning for Zero-Shot NER
Venue: arXiv cs.CL 2025
Year: 2025

Existing approaches to zero-shot Named Entity Recognition (NER) for low-resource languages have primarily relied on machine translation, whereas more recent methods have shifted focus to phonemic representation. Building upon this, we investigate how reducing the phonemic representation gap in IPA transcription between languages with similar phonetic characteristics enables models trained on high-resource languages to perform effectively on low-resource languages. In this work, we propose CONtrastive Learning with IPA (CONLIPA) dataset containing 10 English and high resource languages IPA pairs from 10 frequently used language families. We also propose a cross-lingual IPA Contrastive learning method (IPAC) using the CONLIPA dataset. Furthermore, our proposed dataset and methodology demonstrate a substantial average gain when compared to the best performing baseline.
Bag Semantics Query Containment: The CQ vs. UCQ Case and Other Stories
Venue: arXiv cs.DB 2025
Year: 2025

Query Containment Problem (QCP) is a fundamental decision problem in query processing and optimization. While QCP has for a long time been completely understood for the case of set semantics, decidability of QCP for conjunctive queries under multi-set semantics ($QCP_{\text{CQ}}^{\text{bag}}$) remains one of the most intriguing open problems in database theory. Certain effort has been put, in last 30 years, to solve this problem and some decidable special cases of $QCP_{\text{CQ}}^{\text{bag}}$ were identified, as well as some undecidable extensions, including $QCP_{\text{UCQ}}^{\text{bag}}$. In this paper we introduce a new technique which produces, for a given UCQ $Φ$, a CQ $ϕ$ such that the application of $ϕ$ to a database $D$ is, in some sense, an approximation of the application of $Φ$ to $D$. Using this technique we could analyze the status of $QCP^{\text{bag}}$ when one of the queries in question is a CQ and the other is a UCQ, and we reached conclusions which surprised us a little bit. We also tried to use this technique to translate the known undecidability proof for $QCP_{\text{UCQ}}^{\text{bag}}$ into a proof of undecidability of $QCP_{\text{CQ}}^{\text{bag}}$. And, as you are going to see, we got stopped just one infinitely small $\varepsilon$ before reaching this ultimate goal.
XMutant: XAI-based Fuzzing for Deep Learning Systems
Venue: arXiv cs.SE 2025
Year: 2025

Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random perturbations to input semantic concepts until a failure is found or a timeout is reached. However, such randomness may hinder them from efficiently achieving their goal. This paper proposes XMutant, a technique that leverages explainable artificial intelligence (XAI) techniques to generate challenging test inputs. XMutant uses the local explanation of the input to inform the fuzz testing process and effectively guide it toward failures of the DL system under test. We evaluated different configurations of XMutant in triggering failures for different DL systems both for model-level (sentiment analysis, digit recognition) and system-level testing (advanced driving assistance). Our studies showed that XMutant enables more effective and efficient test generation by focusing on the most impactful parts of the input. XMutant generates up to 125% more failure-inducing inputs compared to an existing baseline, up to 7X faster. We also assessed the validity of these inputs, maintaining a validation rate above 89%, according to automated and human validators.
Beyond the Edge of Function: Unraveling the Patterns of Type Recovery in Binary Code
Venue: arXiv cs.CR 2025
Year: 2025

Type recovery is a crucial step in binary code analysis, holding significant importance for reverse engineering and various security applications. Existing works typically simply target type identifiers within binary code and achieve type recovery by analyzing variable characteristics within functions. However, we find that the types in real-world binary programs are more complex and often follow specific distribution patterns. In this paper, to gain a profound understanding of the variable type recovery problem in binary code, we first conduct a comprehensive empirical study. We utilize the TYDA dataset, which includes 163,643 binary programs across four architectures and four compiler optimization options, fully reflecting the complexity and diversity of real-world programs. We carefully study the unique patterns that characterize types and variables in binary code, and also investigate the impact of compiler optimizations on them, yielding many valuable insights. Based on our empirical findings, we propose ByteTR, a framework for recovering variable types in binary code. We decouple the target type set to address the issue of unbalanced type distribution and perform static program analysis to tackle the impact of compiler optimizations on variable storage. In light of the ubiquity of variable propagation across functions observed in our study, ByteTR conducts inter-procedural analysis to trace variable propagation and employs a gated graph neural network to capture long-range data flow dependencies for variable type recovery. We conduct extensive experiments to evaluate the performance of ByteTR. The results demonstrate that ByteTR leads state-of-the-art works in both effectiveness and efficiency. Moreover, in real CTF challenge case, the pseudo code optimized by ByteTR significantly improves readability, surpassing leading tools IDA and Ghidra.