OpenRaiser/Intern-Atlas huggingface dataset:
Bilevel Models for Adversarial Learning and A Case Study
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial attacks is not well interpreted. How to measure the effect of attacks is still not quite clear. In this paper, we investigate the adversarial learning from the perturbation analysis point of view. We characterize the robustness of learning models through the calmness of the solution mapping. In the case of convex clustering models, we identify the conditions under which the clustering results remain the same under perturbations. When the noise level is large, it leads to an attack. Therefore, we propose two bilevel models for adversarial learning where the effect of adversarial learning is measured by some deviation function. Specifically, we systematically study the so-called $δ$-measure and show that under certain conditions, it can be used as a deviation function in adversarial learning for convex clustering models. Finally, we conduct numerical tests to verify the above theoretical results as well as the efficiency of the two proposed bilevel models.
Scaling Cultural Resources for Improving Generative Models
Venue: arXiv cs.CY 2025
Year: 2025

Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been commonly conducted to improve overall model performance, bolstering and evaluating this cross-cultural competence of generative AI models requires data resources to be intentionally expanded to include global contexts and languages. In this work, we construct a repeatable, scalable, multi-pronged pipeline to collect and contribute culturally salient, multilingual data. We posit that such data can assess the state of the global applicability of our models and thus, in turn, help identify and improve upon cross-cultural gaps.
Agentic Moderation: Multi-Agent Design for Safer Vision-Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Agentic methods have emerged as a powerful and autonomous paradigm that enhances reasoning, collaboration, and adaptive control, enabling systems to coordinate and independently solve complex tasks. We extend this paradigm to safety alignment by introducing Agentic Moderation, a model-agnostic framework that leverages specialised agents to defend multimodal systems against jailbreak attacks. Unlike prior approaches that apply as a static layer over inputs or outputs and provide only binary classifications (safe or unsafe), our method integrates dynamic, cooperative agents, including Shield, Responder, Evaluator, and Reflector, to achieve context-aware and interpretable moderation. Extensive experiments across five datasets and four representative Large Vision-Language Models (LVLMs) demonstrate that our approach reduces the Attack Success Rate (ASR) by 7-19%, maintains a stable Non-Following Rate (NF), and improves the Refusal Rate (RR) by 4-20%, achieving robust, interpretable, and well-balanced safety performance. By harnessing the flexibility and reasoning capacity of agentic architectures, Agentic Moderation provides modular, scalable, and fine-grained safety enforcement, highlighting the broader potential of agentic systems as a foundation for automated safety governance.
GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
Venue: arXiv cs.IR 2025
Year: 2025

In a multi-stage recommendation system, reranking plays a crucial role in modeling intra-list correlations among items. A key challenge lies in exploring optimal sequences within the combinatorial space of permutations. Recent research follows a two-stage (generator-evaluator) paradigm, where a generator produces multiple feasible sequences, and an evaluator selects the best one. In practice, the generator is typically implemented as an autoregressive model. However, these two-stage methods face two main challenges. First, the separation of the generator and evaluator hinders end-to-end training. Second, autoregressive generators suffer from inference efficiency. In this work, we propose a Unified Generative Efficient Reranking Framework (GReF) to address the two primary challenges. Specifically, we introduce Gen-Reranker, an autoregressive generator featuring a bidirectional encoder and a dynamic autoregressive decoder to generate causal reranking sequences. Subsequently, we pre-train Gen-Reranker on the item exposure order for high-quality parameter initialization. To eliminate the need for the evaluator while integrating sequence-level evaluation during training for end-to-end optimization, we propose post-training the model through Rerank-DPO. Moreover, for efficient autoregressive inference, we introduce ordered multi-token prediction (OMTP), which trains Gen-Reranker to simultaneously generate multiple future items while preserving their order, ensuring practical deployment in real-time recommender systems. Extensive offline experiments demonstrate that GReF outperforms state-of-the-art reranking methods while achieving latency that is nearly comparable to non-autoregressive models. Additionally, GReF has also been deployed in a real-world video app Kuaishou with over 300 million daily active users, significantly improving online recommendation quality.
Hybrid Vision Servoing with Depp Alignment and GRU-Based Occlusion Recovery
Venue: arXiv cs.RO 2025
Year: 2025

Vision-based control systems, such as image-based visual servoing (IBVS), have been extensively explored for precise robot manipulation. A persistent challenge, however, is maintaining robust target tracking under partial or full occlusions. Classical methods like Lucas-Kanade (LK) offer lightweight tracking but are fragile to occlusion and drift, while deep learning-based approaches often require continuous visibility and intensive computation. To address these gaps, we propose a hybrid visual tracking framework that bridges advanced perception with real-time servo control. First, a fast global template matcher constrains the pose search region; next, a deep-feature Lucas-Kanade module operating on early VGG layers refines alignment to sub-pixel accuracy (<2px); then, a lightweight residual regressor corrects local misalignments caused by texture degradation or partial occlusion. When visual confidence falls below a threshold, a GRU-based predictor seamlessly extrapolates pose updates from recent motion history. Crucially, the pipeline's final outputs-translation, rotation, and scale deltas-are packaged as direct control signals for 30Hz image-based servo loops. Evaluated on handheld video sequences with up to 90% occlusion, our system sustains under 2px tracking error, demonstrating the robustness and low-latency precision essential for reliable real-world robot vision applications.
VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations
Venue: arXiv cs.CV 2025
Year: 2025

Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video aesthetic database with 10,490 diverse videos annotated by 37 professionals across multiple aesthetic dimensions, including overall and attribute-specific aesthetic scores, rich language comments and objective tags. We propose VADB-Net, a dual-modal pre-training framework with a two-stage training strategy, which outperforms existing video quality assessment models in scoring tasks and supports downstream video aesthetic assessment tasks. The dataset and source code are available at https://github.com/BestiVictory/VADB.
One-shot Humanoid Whole-body Motion Learning
Venue: arXiv cs.RO 2025
Year: 2025

Whole-body humanoid motion represents a cornerstone challenge in robotics, integrating balance, coordination, and adaptability to enable human-like behaviors. However, existing methods typically require multiple training samples per motion category, rendering the collection of high-quality human motion datasets both labor-intensive and costly. To address this, we propose a novel approach that trains effective humanoid motion policies using only a single non-walking target motion sample alongside readily available walking motions. The core idea lies in leveraging order-preserving optimal transport to compute distances between walking and non-walking sequences, followed by interpolation along geodesics to generate new intermediate pose skeletons, which are then optimized for collision-free configurations and retargeted to the humanoid before integration into a simulated environment for policy training via reinforcement learning. Experimental evaluations on the CMU MoCap dataset demonstrate that our method consistently outperforms baselines, achieving superior performance across metrics. Code will be released upon acceptance.
MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference
Venue: arXiv cs.DC 2025
Year: 2025

As large language models (LLMs) continue to scale up, mixture-of-experts (MoE) has become a common technology in SOTA models. MoE models rely on expert parallelism (EP) to alleviate memory bottleneck, which introduces all-to-all communication to dispatch and combine tokens across devices. However, in widely-adopted GPU clusters, high-overhead cross-node communication makes all-to-all expensive, hindering the adoption of EP. Recently, wafer-scale chips (WSCs) have emerged as a platform integrating numerous devices on a wafer-sized interposer. WSCs provide a unified high-performance network connecting all devices, presenting a promising potential for hosting MoE models. Yet, their network is restricted to a mesh topology, causing imbalanced communication pressure and performance loss. Moreover, the lack of on-wafer disk leads to high-overhead expert migration on the critical path. To fully unleash this potential, we first propose Entwined Ring Mapping (ER-Mapping), which co-designs the mapping of attention and MoE layers to balance communication pressure and achieve better performance. We find that under ER-Mapping, the distribution of cold and hot links in the attention and MoE layers is complementary. Therefore, to hide the migration overhead, we propose the Non-invasive Balancer (NI-Balancer), which splits a complete expert migration into multiple steps and alternately utilizes the cold links of both layers. Evaluation shows ER-Mapping achieves communication reduction up to 62%. NI-Balancer further delivers 54% and 22% improvements in MoE computation and communication, respectively. Compared with the SOTA NVL72 supernode, the WSC platform delivers an average 39% higher per-device MoE performance owing to its scalability to larger EP.
The influence of the random numbers quality on the results in stochastic simulations and machine learning
Venue: arXiv cs.PF 2025
Year: 2025

Pseudorandom number generators (PRNGs) are ubiquitous in stochastic simulations and machine learning (ML), where they drive sampling, parameter initialization, regularization, and data shuffling. While widely used, the potential impact of PRNG statistical quality on computational results remains underexplored. In this study, we investigate whether differences in PRNG quality, as measured by standard statistical test suites, can influence outcomes in representative stochastic applications. Seven PRNGs were evaluated, ranging from low-quality linear congruential generators (LCGs) with known statistical deficiencies to high-quality generators such as Mersenne Twister, PCG, and Philox. We applied these PRNGs to four distinct tasks: an epidemiological agent-based model (ABM), two independent from-scratch MNIST classification implementations (Python/NumPy and C++), and a reinforcement learning (RL) CartPole environment. Each experiment was repeated 30 times per generator using fixed seeds to ensure reproducibility, and outputs were compared using appropriate statistical analyses. Results show that very poor statistical quality, as in the ''bad'' LCG failing 125 TestU01 Crush tests, produces significant deviations in ABM epidemic dynamics, reduces MNIST classification accuracy, and severely degrades RL performance. In contrast, mid-and good-quality LCGs-despite failing a limited number of Crush or BigCrush tests-performed comparably to top-tier PRNGs in most tasks, with the RL experiment being the primary exception where performance scaled with statistical quality. Our findings indicate that, once a generator meets a sufficient statistical robustness threshold, its family or design has negligible impact on outcomes for most workloads, allowing selection to be guided by performance and implementation considerations. However, the use of low-quality PRNGs in sensitive stochastic computations can introduce substantial and systematic errors.
Adaptive Design of mmWave Initial Access Codebooks using Reinforcement Learning
Venue: arXiv cs.NI 2025
Year: 2025

Initial access (IA) is the process by which user equipment (UE) establishes its first connection with a base station. In 5G systems, particularly at millimeter-wave frequencies, IA integrates beam management to support highly directional transmissions. The base station employs a codebook of beams for the transmission of Synchronization Signal Blocks (SSBs), which are periodically swept to detect and connect users. The design of this SSB codebook is critical for ensuring reliable, wide-area coverage. In current networks, SSB codebooks are meticulously engineered by domain experts. While these expert-defined codebooks provide a robust baseline, they lack flexibility in dynamic or heterogeneous environments where user distributions vary, limiting their overall effectiveness. This paper proposes a hybrid Reinforcement Learning (RL) framework for adaptive SSB codebook design. Building on top of expert knowledge, the RL agent leverages a pool of expert-designed SSB beams and learns to adaptively select or combine them based on real-time feedback. This enables the agent to dynamically tailor codebooks to the actual environment, without requiring explicit user location information, while always respecting practical beam constraints. Simulation results demonstrate that, on average, the proposed approach improves user connectivity by 10.8$\%$ compared to static expert configurations. These findings highlight the potential of combining expert knowledge with data-driven optimization to achieve more intelligent, flexible, and resilient beam management in next-generation wireless networks.
Analysis of Semi-Supervised Learning on Hypergraphs
Venue: arXiv cs.LG 2025
Year: 2025

Hypergraphs provide a natural framework for modeling higher-order interactions, yet their theoretical underpinnings in semi-supervised learning remain limited. We provide an asymptotic consistency analysis of variational learning on random geometric hypergraphs, precisely characterizing the conditions ensuring the well-posedness of hypergraph learning as well as showing convergence to a weighted $p$-Laplacian equation. Motivated by this, we propose Higher-Order Hypergraph Learning (HOHL), which regularizes via powers of Laplacians from skeleton graphs for multiscale smoothness. HOHL converges to a higher-order Sobolev seminorm. Empirically, it performs strongly on standard baselines.
Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Visual Prompt Tuning (VPT) of pre-trained Vision Transformers (ViTs) has proven highly effective as a parameter-efficient fine-tuning technique for adapting large models to downstream tasks with limited data. Its parameter efficiency makes it particularly suitable for Federated Learning (FL), where both communication and computation budgets are often constrained. However, global prompt tuning struggles to generalize across heterogeneous clients, while personalized tuning overfits to local data and lacks generalization. We propose PEP-FedPT (Prompt Estimation from Prototypes for Federated Prompt Tuning), a unified framework designed to achieve both generalization and personalization in federated prompt tuning of ViTs. Within this framework, we introduce the novel Class-Contextualized Mixed Prompt (CCMP) - based on class-specific prompts maintained alongside a globally shared prompt. For each input, CCMP adaptively combines class-specific prompts using weights derived from global class prototypes and client class priors. This approach enables per-sample prompt personalization without storing client-dependent trainable parameters. The prompts are collaboratively optimized via traditional federated averaging technique on the same. Comprehensive evaluations on CIFAR-100, TinyImageNet, DomainNet, and iNaturalist datasets demonstrate that PEP-FedPT consistently surpasses the state-of-the-art baselines under diverse data heterogeneity scenarios, establishing a strong foundation for efficient and generalizable federated prompt tuning of Vision Transformers.
Towards Automated Quality Assurance of Patent Specifications: A Multi-Dimensional LLM Framework
Venue: arXiv cs.IR 2025
Year: 2025

Although AI drafting tools have gained prominence in patent writing, the systematic evaluation of AI-generated patent content quality represents a significant research gap. To address this gap, We propose to evaluate patents using regulatory compliance, technical coherence, and figure-reference consistency detection modules, and then generate improvement suggestions via an integration module. The framework is validated on a comprehensive dataset comprising 80 human-authored and 80 AI-generated patents from two patent drafting tools. Evaluation is performed on 10,841 total sentences, 8,924 non-template sentences, and 554 patent figures for the three detection modules respectively, achieving balanced accuracies of 99.74%, 82.12%, and 91.2% against expert annotations. Additional analysis was conducted to examine defect distributions across patent sections, technical domains, and authoring sources. Section-based analysis indicates that figure-text consistency and technical detail precision require particular attention. Mechanical Engineering and Construction show more claim-specification inconsistencies due to complex technical documentation requirements. AI-generated patents show a significant gap compared to human-authored ones. While human-authored patents primarily contain surface-level errors like typos, AI-generated patents exhibit more structural defects in figure-text alignment and cross-references.
Can Like Attract Like? A Study of Homonymous Gathering in Networks
Venue: arXiv cs.DC 2025
Year: 2025

A team of mobile agents, starting from distinct nodes of a network, have to meet at the same node and declare that they all met. Agents execute the same algorithm, which they start when activated by an adversary or by an agent entering their initial node. When activated, agents traverse edges of the network in synchronous rounds. Their perception and communication are strictly local. This task, known as gathering, is a central problem in distributed mobile systems. Most prior work focuses on minimizing its time complexity, i.e., the worst-case number of rounds between the start of the earliest agent and the task completion. To break possible symmetries, deterministic solutions typically assume that agents have pairwise distinct IDs, called labels, known only to themselves. But must all labels be pairwise distinct to guarantee deterministic gathering? We address this question by considering agents that may share the same label. A team L is said to be gatherable if, for every initial setting of L, there is an algorithm that solves gathering. Our contribution is threefold. (1) We give a full characterization of the gatherable teams. (2) We design an algorithm that gathers all of them in poly$(n,\logλ)$ time, where $n$ (resp. $λ$) is the graph order (resp. the smallest label in L). This algorithm requires the agents to initially share only $O(\log \log \log μ)$ bits of common knowledge, where $μ$ is the largest label multiplicity in L. (3) We show this dependency is almost optimal to get a poly$(n,\logλ)$-time complexity. As a by-product, we get the first deterministic poly$(n,\logλ)$-time algorithm requiring no common knowledge to gather any team when all labels are distinct. Known to be achievable for two-agent teams, extending this to any team size faced a major challenge: termination detection. Our techniques to address it may be of independent interest.
Fine-Tuned Language Models for Domain-Specific Summarization and Tagging
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging. The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring. By leveraging the LLaMA Factory framework, the study fine-tunes LLMs on both generalpurpose and custom domain-specific datasets, particularly in the political and security domains. The models are evaluated using BLEU and ROUGE metrics, demonstrating that instruction fine-tuning significantly enhances summarization and tagging accuracy, especially for specialized corpora. Notably, the LLaMA3-8B-Instruct model, despite its initial limitations in Chinese comprehension, outperforms its Chinese-trained counterpart after domainspecific fine-tuning, suggesting that underlying reasoning capabilities can transfer across languages. The pipeline enables concise summaries and structured entity tagging, facilitating rapid document categorization and distribution. This approach proves scalable and adaptable for real-time applications, supporting efficient information management and the ongoing need to capture emerging language trends. The integration of LLMs and NER offers a robust solution for transforming unstructured text into actionable insights, crucial for modern knowledge management and security operations.
Combining Moving Mass Actuators and Manoeuvring Models for Underwater Vehicles: A Lagrangian Approach
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we present a Newton-Euler formulation of the equations of motion for underwater vehicles with an interntal moving mass actuator. Furthermore, the moving mass dynamics are expressed as an extension to the manoeuvring model for underwater vehicles, originally introduced by Fossen (1991). The influence of the moving mass is described in body-frame and included as states in both an additional kinematic equation and as part of the coupled rigid-body kinetics of the underwater vehicle. The Coriolis-centripetal effects are derived from Kirchhoff's equations and the hydrostatics are derived using first principals. The proposed Newton-Euler model is validated through simulation and compared with the traditional Hamiltonian internal moving mass actuator formulation.