OpenRaiser/Intern-Atlas huggingface dataset:
SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
Venue: arXiv cs.CL 2025
Year: 2025

Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.
Monadic type-and-effect soundness
Venue: arXiv cs.PL 2025
Year: 2025

We introduce the abstract notions of "monadic operational semantics", a small-step semantics where computational effects are modularly modeled by a monad, and "type-and-effect system", including "effect types" whose interpretation lifts well-typedness to its monadic version. In this meta-theory, as usually done in the non-monadic case, we can express progress and subject reduction properties and provide a proof, given once and for all, that they imply soundness. The approach is illustrated on a lambda calculus with generic effects. We equip the calculus with an expressive type-and-effect system, and provide proofs of progress and subject reduction which are parametric on the interpretation of effect types. In this way, we obtain as instances many significant examples, such as checking exceptions, preventing/limiting non-determinism, constraining order/fairness of outputs on different locations. We also provide an extension with constructs to raise and handle computational effects, which can be instantiated to model different policies.
DeepTrans: Deep Reasoning Translation via Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

Recently, deep reasoning LLMs (e.g., OpenAI o1 and DeepSeek-R1) have shown promising performance in various downstream tasks. Free translation is an important and interesting task in the multilingual world, which requires going beyond word-for-word translation. However, the task is still under-explored in deep reasoning LLMs. In this paper, we introduce DeepTrans, a deep reasoning translation model that learns free translation via reinforcement learning (RL). Specifically, we carefully build a reward model with pre-defined scoring criteria on both the translation results and the thought processes. The reward model teaches DeepTrans how to think and free-translate the given sentences during RL. Besides, our RL training does not need any labeled translations, avoiding the human-intensive annotation or resource-intensive data synthesis. Experimental results show the effectiveness of DeepTrans. Using Qwen2.5-7B as the backbone, DeepTrans improves performance by 16.3% in literature translation, and outperforms strong deep reasoning LLMs. Moreover, we summarize the failures and interesting findings during our RL exploration. We hope this work could inspire other researchers in free translation.
Efficient Generative Model Training via Embedded Representation Warmup
Venue: arXiv cs.LG 2025
Year: 2025

Generative models face a fundamental challenge: they must simultaneously learn high-level semantic concepts (what to generate) and low-level synthesis details (how to generate it). Conventional end-to-end training entangles these distinct, and often conflicting objectives, leading to a complex and inefficient optimization process. We argue that explicitly decoupling these tasks is key to unlocking more effective and efficient generative modeling. To this end, we propose Embedded Representation Warmup (ERW), a principled two-phase training framework. The first phase is dedicated to building a robust semantic foundation by aligning the early layers of a diffusion model with a powerful pretrained encoder. This provides a strong representational prior, allowing the second phase -- generative full training with alignment loss to refine the representation -- to focus its resources on high-fidelity synthesis. Our analysis confirms that this efficacy stems from functionally specializing the model's early layers for representation. Empirically, our framework achieves a 11.5$\times$ speedup in 350 epochs to reach FID=1.41 compared to single-phase methods like REPA. Code is available at https://github.com/LINs-lab/ERW.
Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?
Venue: arXiv cs.AI 2025
Year: 2025

Multi-agents-based news-driven time series forecasting is considered as a potential paradigm shift in the era of large language models (LLMs). The challenge of this task lies in measuring the influences of different news events towards the fluctuations of time series. This requires agents to possess stronger abilities of innovative thinking and the identifying misleading logic. However, the existing multi-agent discussion framework has limited enhancement on time series prediction in terms of optimizing these two capabilities. Inspired by the role of competition in fostering innovation, this study embeds a competition mechanism within the multi-agent discussion to enhance agents' capability of generating innovative thoughts. Furthermore, to bolster the model's proficiency in identifying misleading information, we incorporate a fine-tuned small-scale LLM model within the reflective stage, offering auxiliary decision-making support. Experimental results confirm that the competition can boost agents' capacity for innovative thinking, which can significantly improve the performances of time series prediction. Similar to the findings of social science, the intensity of competition within this framework can influence the performances of agents, providing a new perspective for studying LLMs-based multi-agent systems.
Bingo: Radix-based Bias Factorization for Random Walk on Dynamic Graphs
Venue: arXiv cs.DC 2025
Year: 2025

Random walks are a primary means for extracting information from large-scale graphs. While most real-world graphs are inherently dynamic, state-of-the-art random walk engines failed to efficiently support such a critical use case. This paper takes the initiative to build a general random walk engine for dynamically changing graphs with two key principles: (i) This system should support both low-latency streaming updates and high-throughput batched updates. (ii) This system should achieve fast sampling speed while maintaining acceptable space consumption to support dynamic graph updates. Upholding both standards, we introduce Bingo, a GPU-based random walk engine for dynamically changing graphs. First, we propose a novel radix-based bias factorization algorithm to support constant time sampling complexity while supporting fast streaming updates. Second, we present a group-adaption design to reduce space consumption dramatically. Third, we incorporate GPU-aware designs to support high-throughput batched graph updates on massively parallel platforms. Together, Bingo outperforms existing efforts across various applications, settings, and datasets, achieving up to a 271.11x speedup compared to the state-of-the-art efforts.
Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting
Venue: arXiv cs.HC 2025
Year: 2025

This study examines the role of AI-assisted pretesting in enhancing learning outcomes, particularly when integrated with generative AI tools like ChatGPT. Pretesting, a learning strategy in which students attempt to answer questions or solve problems before receiving instruction, has been shown to improve retention by activating prior knowledge. The adaptability and interactivity of AI-assisted pretesting introduce new opportunities for optimizing learning in digital environments. Across three experimental studies, we explored how pretesting strategies, task characteristics, and student motivation influence learning. Findings suggest that AI-assisted pretesting enhances learning outcomes, particularly for tasks requiring higher-order thinking. While adaptive AI-driven pretesting increased engagement, its benefits were most pronounced in complex, exploratory tasks rather than straightforward computational problems. These results highlight the importance of aligning pretesting strategies with task demands, demonstrating that AI can optimize learning when applied to tasks requiring deeper cognitive engagement. This research provides insights into how AI-assisted pretesting can be effectively integrated with generative AI tools to enhance both cognitive and motivational outcomes in learning environments.
MASSeg : 2nd Technical Report for 4th PVUW MOSE Track
Venue: arXiv cs.CV 2025
Year: 2025

Complex video object segmentation continues to face significant challenges in small object recognition, occlusion handling, and dynamic scene modeling. This report presents our solution, which ranked second in the MOSE track of CVPR 2025 PVUW Challenge. Based on an existing segmentation framework, we propose an improved model named MASSeg for complex video object segmentation, and construct an enhanced dataset, MOSE+, which includes typical scenarios with occlusions, cluttered backgrounds, and small target instances. During training, we incorporate a combination of inter-frame consistent and inconsistent data augmentation strategies to improve robustness and generalization. During inference, we design a mask output scaling strategy to better adapt to varying object sizes and occlusion levels. As a result, MASSeg achieves a J score of 0.8250, F score of 0.9007, and a J&F score of 0.8628 on the MOSE test set.
Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study
Venue: arXiv cs.CV 2025
Year: 2025

Recent developments in hardware, computer graphics, and AI may soon enable AR/VR head-mounted displays (HMDs) to become everyday devices like smartphones and tablets. Eye trackers within HMDs provide a special opportunity for such setups as it is possible to facilitate gaze-based research and interaction. However, estimating users' gaze information often requires raw eye images and videos that contain iris textures, which are considered a gold standard biometric for user authentication, and this raises privacy concerns. Previous research in the eye-tracking community focused on obfuscating iris textures while keeping utility tasks such as gaze estimation accurate. Despite these attempts, there is no comprehensive benchmark that evaluates state-of-the-art approaches. Considering all, in this paper, we benchmark blurring, noising, downsampling, rubber sheet model, and iris style transfer to obfuscate user identity, and compare their impact on image quality, privacy, utility, and risk of imposter attack on two datasets. We use eye segmentation and gaze estimation as utility tasks, and reduction in iris recognition accuracy as a measure of privacy protection, and false acceptance rate to estimate risk of attack. Our experiments show that canonical image processing methods like blurring and noising cause a marginal impact on deep learning-based tasks. While downsampling, rubber sheet model, and iris style transfer are effective in hiding user identifiers, iris style transfer, with higher computation cost, outperforms others in both utility tasks, and is more resilient against spoof attacks. Our analyses indicate that there is no universal optimal approach to balance privacy, utility, and computation burden. Therefore, we recommend practitioners consider the strengths and weaknesses of each approach, and possible combinations of those to reach an optimal privacy-utility trade-off.
Change Your Perspective, Widen Your Worldview! Societally Beneficial Perceptual Filter Bubbles in Personalized Reality
Venue: arXiv cs.HC 2025
Year: 2025

Extended Reality (XR) technologies enable the personalized mediation of an individual's perceivable reality across modalities, thereby creating a Personalized Reality (PR). While this may lead to individually beneficial effects in the form of more efficient, more fun, and safer experiences, it may also lead to perceptual filter bubbles since individuals are exposed predominantly or exclusively to content that is congruent with their existing beliefs and opinions. This undermining of a shared basis for interaction and discussion through constrained perceptual worldviews may impact society through increased polarization and other well-documented negative effects of filter bubbles. In this paper, we argue that this issue can be mitigated by increasing individuals' awareness of their current perspective and providing avenues for development, including through support for engineered serendipity and fostering of self-actualization that already show promise for traditional recommender systems. We discuss how these methods may be transferred to XR to yield valuable tools to give people transparency and agency over their perceptual worldviews in a responsible manner.
LMFormer: Lane based Motion Prediction Transformer
Venue: arXiv cs.CV 2025
Year: 2025

Motion prediction plays an important role in autonomous driving. This study presents LMFormer, a lane-aware transformer network for trajectory prediction tasks. In contrast to previous studies, our work provides a simple mechanism to dynamically prioritize the lanes and shows that such a mechanism introduces explainability into the learning behavior of the network. Additionally, LMFormer uses the lane connection information at intersections, lane merges, and lane splits, in order to learn long-range dependency in lane structure. Moreover, we also address the issue of refining the predicted trajectories and propose an efficient method for iterative refinement through stacked transformer layers. For benchmarking, we evaluate LMFormer on the nuScenes dataset and demonstrate that it achieves SOTA performance across multiple metrics. Furthermore, the Deep Scenario dataset is used to not only illustrate cross-dataset network performance but also the unification capabilities of LMFormer to train on multiple datasets and achieve better performance.
Who Speaks for Ethics? How Demographics Shape Ethical Advocacy in Software Development
Venue: arXiv cs.SE 2025
Year: 2025

The integration of ethics into software development faces significant challenges due to market fundamentalism in organizational practices, where profit often takes precedence over ethical considerations. Additionally, the critical influence of practitioners' individual backgrounds on ethical decision-making remains underexplored, highlighting a gap in comprehensive research. This is especially essential to understand due to the demographic imbalance in software roles. This study investigates ethical concerns in software development, focusing on how they are perceived, prioritized, and addressed by demographically different practitioners. By surveying 217 software practitioners across diverse roles, industries, and countries, we identify critical barriers to ethical integration and examine practitioners' capacity to mitigate these issues. Our findings reveal pronounced demographic disparities, with marginalized groups - including women, BIPOC, and disabled individuals - reporting ethical concerns at higher frequencies. Notably, marginalized practitioners demonstrated heightened sensitivity to ethical implementation and greater empowerment to address them. However, practitioners overall often lack the support needed to address ethical challenges effectively. These insights underscore the urgent need for reforms in software education and development processes that center on diverse perspectives. Such reforms are essential to advancing ethical integration in software development and ensuring responsible computing practices in an increasingly complex technological landscape.
Optimal Graph Stretching for Distributed Averaging
Venue: arXiv cs.DC 2025
Year: 2025

The performance of distributed averaging depends heavily on the underlying topology. In various fields, including compressed sensing, multi-party computation, and abstract graph theory, graphs may be expected to be free of short cycles, i.e. to have high girth. Though extensive analyses and heuristics exist for optimising the performance of distributed averaging in general networks, these studies do not consider girth. As such, it is not clear what happens to convergence time when a graph is stretched to a higher girth. In this work, we introduce the optimal graph stretching problem, wherein we are interested in finding the set of edges for a particular graph that ensures optimal convergence time under constraint of a minimal girth. We compare various methods for choosing which edges to remove, and use various convergence heuristics to speed up the searching process. We generate many graphs with varying parameters, stretch and optimise them, and measure the duration of distributed averaging. We find that stretching by itself significantly increases convergence time. This decrease can be counteracted with a subsequent repair phase, guided by a convergence time heuristic. Existing heuristics are capable, but may be suboptimal.
Universal Algebra and Effectful Computation
Venue: arXiv cs.PL 2025
Year: 2025

Abstract clones serve as an algebraic presentation of the syntax of a simple type theory. From the perspective of universal algebra, they define algebraic theories like those of groups, monoids and rings. This link allows one to study the language of simple type theory from the viewpoint of universal algebra. Programming languages, however, are much more complicated than simple type theory. Many useful features like reading, writing, and exception handling involve interacting with the environment; these are called side-effects. Algebraic presentations for languages with the appropriate syntax for handling effects are given by premulticategories and effectful multicategories. We study these structures with the aim of defining a suitable notion of an algebra. To achieve this goal, we proceed in two steps. First, we define a tensor on $[\to,\category{Set}]$, and show that this tensor along with the cartesian product gives the category a duoidal structure. Secondly, we introduce the novel notion of a multicategory enriched in a duoidal category which generalize the traditional notion of a multicategory. Further, we prove that an effectful multicategory is the same as a multicategory enriched in the duoidal category $[\to,\category{Set}]$. This result places multicategories and effectful multicategories on a similar footing, and provides a mechanism for transporting concepts from the theory of multicategories (which model pure computation) to the theory of effectful multicategories (which model effectful computation). As an example of this, we generalize the definition of a 2-morphism for multicategories to the duoidally enriched case. Our equivalence result then gives a natural definition of a 2-morphism for effectful multicategories, which we then use to define the notion of an algebra.
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
Venue: arXiv cs.CV 2025
Year: 2025

Video anomaly detection (VAD) aims to identify unexpected events in videos and has wide applications in safety-critical domains. While semi-supervised methods trained on only normal samples have gained traction, they often suffer from high false alarm rates and poor interpretability. Recently, vision-language models (VLMs) have demonstrated strong multimodal reasoning capabilities, offering new opportunities for explainable anomaly detection. However, their high computational cost and lack of domain adaptation hinder real-time deployment and reliability. Inspired by dual complementary pathways in human visual perception, we propose SlowFastVAD, a hybrid framework that integrates a fast anomaly detector with a slow anomaly detector (namely a retrieval augmented generation (RAG) enhanced VLM), to address these limitations. Specifically, the fast detector first provides coarse anomaly confidence scores, and only a small subset of ambiguous segments, rather than the entire video, is further analyzed by the slower yet more interpretable VLM for elaborate detection and reasoning. Furthermore, to adapt VLMs to domain-specific VAD scenarios, we construct a knowledge base including normal patterns based on few normal samples and abnormal patterns inferred by VLMs. During inference, relevant patterns are retrieved and used to augment prompts for anomaly reasoning. Finally, we smoothly fuse the anomaly confidence of fast and slow detectors to enhance robustness of anomaly detection. Extensive experiments on four benchmarks demonstrate that SlowFastVAD effectively combines the strengths of both fast and slow detectors, and achieves remarkable detection accuracy and interpretability with significantly reduced computational overhead, making it well-suited for real-world VAD applications with high reliability requirements.
LL-Gaussian: Low-Light Scene Reconstruction and Enhancement via Gaussian Splatting for Novel View Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

Novel view synthesis (NVS) in low-light scenes remains a significant challenge due to degraded inputs characterized by severe noise, low dynamic range (LDR) and unreliable initialization. While recent NeRF-based approaches have shown promising results, most suffer from high computational costs, and some rely on carefully captured or pre-processed data--such as RAW sensor inputs or multi-exposure sequences--which severely limits their practicality. In contrast, 3D Gaussian Splatting (3DGS) enables real-time rendering with competitive visual fidelity; however, existing 3DGS-based methods struggle with low-light sRGB inputs, resulting in unstable Gaussian initialization and ineffective noise suppression. To address these challenges, we propose LL-Gaussian, a novel framework for 3D reconstruction and enhancement from low-light sRGB images, enabling pseudo normal-light novel view synthesis. Our method introduces three key innovations: 1) an end-to-end Low-Light Gaussian Initialization Module (LLGIM) that leverages dense priors from learning-based MVS approach to generate high-quality initial point clouds; 2) a dual-branch Gaussian decomposition model that disentangles intrinsic scene properties (reflectance and illumination) from transient interference, enabling stable and interpretable optimization; 3) an unsupervised optimization strategy guided by both physical constrains and diffusion prior to jointly steer decomposition and enhancement. Additionally, we contribute a challenging dataset collected in extreme low-light environments and demonstrate the effectiveness of LL-Gaussian. Compared to state-of-the-art NeRF-based methods, LL-Gaussian achieves up to 2,000 times faster inference and reduces training time to just 2%, while delivering superior reconstruction and rendering quality.