OpenRaiser/Intern-Atlas huggingface dataset:
A mini-batch training strategy for deep subspace clustering networks
Venue: arXiv cs.CV 2025
Year: 2025

Mini-batch training is a cornerstone of modern deep learning, offering computational efficiency and scalability for training complex architectures. However, existing deep subspace clustering (DSC) methods, which typically combine an autoencoder with a self-expressive layer, rely on full-batch processing. The bottleneck arises from the self-expressive module, which requires representations of the entire dataset to construct a self-representation coefficient matrix. In this work, we introduce a mini-batch training strategy for DSC by integrating a memory bank that preserves global feature representations. Our approach enables scalable training of deep architectures for subspace clustering with high-resolution images, overcoming previous limitations. Additionally, to efficiently fine-tune large-scale pre-trained encoders for subspace clustering, we propose a decoder-free framework that leverages contrastive learning instead of autoencoding for representation learning. This design not only eliminates the computational overhead of decoder training but also provides competitive performance. Extensive experiments demonstrate that our approach not only achieves performance comparable to full-batch methods, but outperforms other state-of-the-art subspace clustering methods on the COIL100 and ORL datasets by fine-tuning deep networks.
MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Medical image segmentation plays an important role in computer-aided diagnosis. Traditional convolution-based U-shape segmentation architectures are usually limited by the local receptive field. Existing vision transformers have been widely applied to diverse medical segmentation frameworks due to their superior capabilities of capturing global contexts. Despite the advantage, the real-world application of vision transformers is challenged by their non-linear self-attention mechanism, requiring huge computational costs. To address this issue, the selective state space model (SSM) Mamba has gained recognition for its adeptness in modeling long-range dependencies in sequential data, particularly noted for its efficient memory costs. In this paper, we propose MambaVesselNet++, a Hybrid CNN-Mamba framework for medical image segmentation. Our MambaVesselNet++ is comprised of a hybrid image encoder (Hi-Encoder) and a bifocal fusion decoder (BF-Decoder). In Hi-Encoder, we first devise the texture-aware layer to capture low-level semantic features by leveraging convolutions. Then, we utilize Mamba to effectively model long-range dependencies with linear complexity. The Bi-Decoder adopts skip connections to combine local and global information of the Hi-Encoder for the accurate generation of segmentation masks. Extensive experiments demonstrate that MambaVesselNet++ outperforms current convolution-based, transformer-based, and Mamba-based state-of-the-arts across diverse medical 2D, 3D, and instance segmentation tasks. The code is available at https://github.com/CC0117/MambaVesselNet.
Offloading tracing for real-time systems using a scalable cloud infrastructure
Venue: arXiv cs.DC 2025
Year: 2025

Real-time embedded systems require precise timing and fault detection to ensure correct behavior. Traditional tracing tools often rely on local desktops with limited processing and storage capabilities, which hampers large-scale analysis. This paper presents a scalable, cloud-based architecture for software tracing in real-time systems based on microservices and edge computing. Our approach shifts the trace processing workload from the developer's machine to the cloud, using a dedicated tracing component that captures trace data and forwards it to a scalable backend via WebSockets and Apache Kafka. This enables long-term monitoring and collaborative analysis of target executions, e.g., to detect and investigate sporadic errors. We demonstrate how this architecture supports scalable analysis of parallel tracing sessions and lays the foundation for future integration of rule-based testing and runtime verification. The evaluation results show that the architecture can handle many parallel tracing sessions efficiently, although the per-session throughput decreases slightly as the system load increases, while the overall throughput increases. Although the design includes a dedicated tracer for analysis during development, this approach is not limited to such setups. Target systems with network connectivity can stream reduced trace data directly, enabling runtime monitoring in the field.
CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints
Venue: arXiv cs.RO 2025
Year: 2025

Clothes manipulation, such as folding or hanging, is a critical capability for home service robots. Despite recent advances, most existing methods remain limited to specific clothes types and tasks, due to the complex, high-dimensional geometry of clothes. This paper presents CLothes mAnipulation with Semantic keyPoints (CLASP), which aims at general-purpose clothes manipulation over diverse clothes types, T-shirts, shorts, skirts, long dresses, ..., as well as different tasks, folding, flattening, hanging, .... The core idea of CLASP is semantic keypoints-e.g., ''left sleeve'' and ''right shoulder''-a sparse spatial-semantic representation, salient for both perception and action. Semantic keypoints of clothes can be reliably extracted from RGB-D images and provide an effective representation for a wide range of clothes manipulation policies. CLASP uses semantic keypoints as an intermediate representation to connect high-level task planning and low-level action execution. At the high level, it exploits vision language models (VLMs) to predict task plans over the semantic keypoints. At the low level, it executes the plans with the help of a set of pre-built manipulation skills conditioned on the keypoints. Extensive simulation experiments show that CLASP outperforms state-of-the-art baseline methods on multiple tasks across diverse clothes types, demonstrating strong performance and generalization. Further experiments with a Franka dual-arm system on four distinct tasks-folding, flattening, hanging, and placing-confirm CLASP's performance on real-life clothes manipulation.
When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language versus geometry. We revisit this question by re-evaluating an instruction-guided pipeline, InstructNav, under a detector-controlled setting and introducing two training-free variants that only alter the action value map: a geometry-only Frontier Proximity Explorer (FPE) and a lightweight Semantic-Heuristic Frontier (SHF) that polls the LLM with simple frontier votes. Across HM3D and MP3D, FPE matches or exceeds the detector-controlled instruction follower while using no API calls and running faster; SHF attains comparable accuracy with a smaller, localized language prior. These results suggest that carefully engineered frontier geometry accounts for much of the reported progress, and that language is most reliable as a light heuristic rather than an end-to-end planner.
Towards Next Generation Immersive Applications in 5G Environments
Venue: arXiv cs.NI 2025
Year: 2025

The Multi-user Immersive Reality (MIR) landscape is evolving rapidly, with applications spanning virtual collaboration, entertainment, and training. However, wireless network limitations create a critical bottleneck, struggling to meet the high-bandwidth and ultra-low latency demands essential for next-generation MIR experiences. This paper presents Hera, a modular framework for next-generation immersive applications, comprising a high-level streaming and synchronization layer for AR/VR systems and a low-level delay-based QoE-aware rate control protocol optimized for dynamic wireless environments. The Hera framework integrates application-aware streaming logic with a QoE-centric rate control core, enabling adaptive video quality, multi-user fairness, and low-latency communication across challenging 5G network conditions. We demonstrate that Hera outperforms existing state-of-the-art rate control algorithms by maintaining up to 66% lower latencies with comparable throughput performance, higher visual quality with 50% average bitrate improvements in our analysis, and improved fairness. By bridging the gap between application-level responsiveness and network-level adaptability, Hera lays the foundation for more scalable, robust, and high-fidelity multi-user immersive experiences.
Strategic Filtering for Content Moderation: Free Speech or Free of Distortion?
Venue: arXiv cs.LG 2025
Year: 2025

User-generated content (UGC) on social media platforms is vulnerable to incitements and manipulations, necessitating effective regulations. To address these challenges, those platforms often deploy automated content moderators tasked with evaluating the harmfulness of UGC and filtering out content that violates established guidelines. However, such moderation inevitably gives rise to strategic responses from users, who strive to express themselves within the confines of guidelines. Such phenomena call for a careful balance between: 1. ensuring freedom of speech -- by minimizing the restriction of expression; and 2. reducing social distortion -- measured by the total amount of content manipulation. We tackle the problem of optimizing this balance through the lens of mechanism design, aiming at optimizing the trade-off between minimizing social distortion and maximizing free speech. Although determining the optimal trade-off is NP-hard, we propose practical methods to approximate the optimal solution. Additionally, we provide generalization guarantees determining the amount of finite offline data required to approximate the optimal moderator effectively.
ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Speech language models refer to language models with speech processing and understanding capabilities. One key desirable capability for speech language models is the ability to capture the intricate interdependency between content and prosody. The existing mainstream paradigm of training speech language models, which converts speech into discrete tokens before feeding them into LLMs, is sub-optimal in learning prosody information -- we find that the resulting LLMs do not exhibit obvious emerging prosody processing capabilities via pre-training alone. To overcome this, we propose ProsodyLM, which introduces a simple tokenization scheme amenable to learning prosody. Each speech utterance is first transcribed into text, followed by a sequence of word-level prosody tokens. Compared with conventional speech tokenization schemes, the proposed tokenization scheme retains more complete prosody information, and is more understandable to text-based LLMs. We find that ProsodyLM can learn surprisingly diverse emerging prosody processing capabilities through pre-training alone, ranging from harnessing the prosody nuances in generated speech, such as contrastive focus, understanding emotion and stress in an utterance, to maintaining prosody consistency in long contexts.
From First Use to Final Commit: Studying the Evolution of Multi-CI Service Adoption
Venue: arXiv cs.SE 2025
Year: 2025

Continuous Integration (CI) services, such as GitHub Actions and Travis CI, are widely adopted in open-source development to automate testing and deployment. Though existing research often examines individual services in isolation, it remains unclear how projects adopt and transition between multiple services over time. To understand how CI adoption is evolving across services, we present a preliminary study analyzing the historical CI adoption of 18,924 Java projects hosted on GitHub between January 2008 and December 2024, adopting at least one of eight CI services, namely Travis CI, AppVeyor, CircleCI, Azure Pipelines, GitHub Actions, Bitbucket, GitLab CI, and Cirrus CI. Specifically, we investigate: (1) how frequently CI services are co-adopted or replaced, and (2) how maintenance activity varies across different services. Our analysis shows that the use of multiple CI services within the same project is a recurring pattern observed in nearly one in five projects, often reflecting migration across CI services. Our study is among the first to examine multi-CI adoption in practice, offering new insights for future research and highlighting the need for strategies and tools to support service selection, coordination, and migration in evolving CI environments.
From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
Venue: arXiv cs.SE 2025
Year: 2025

Scientific Workflow Systems such as Galaxy and Nextflow are essential for scalable, reproducible, and automated bioinformatics analyses. However, developing and understanding scientific workflows remains challenging for many domain scientists due to the complexity of tool/module selection, infrastructure requirements, and limited programming expertise. This study explores whether state-of-the-art Large Language Models such as GPT-4o, Gemini 2.5 Flash, and DeepSeek-V3 can assist in generating accurate, complete, and usable bioinformatics workflows. We evaluate a set of representative workflows covering tasks such as RNA-seq, SNP analysis, and DNA methylation across both Galaxy (graphical) and Nextflow (script-based) platforms. To simulate realistic usage, we adopt a tiered prompting strategy: each workflow is first generated using an instruction-only prompt; if the output is incomplete or incorrect, we escalate to a role-based prompt, and finally to chain-of-thought prompting if needed. The generated workflows are evaluated against community-curated baselines from the Galaxy Training Network and nf-core, using criteria including correctness, completeness, tool appropriateness, and executability. Results show that LLMs exhibit strong potential in workflow development. Gemini 2.5 Flash produced the most accurate and user-friendly workflows in Galaxy, while DeepSeek-V3 excelled in Nextflow pipeline generation. GPT-4o performed nicely with structured prompts. Prompting strategy significantly influenced output quality, with role-based and chain-of-thought prompts enhancing correctness and completeness. Overall, LLMs can reduce the cognitive and technical barriers to workflow development, making SWSs more accessible to novice and expert users. This work highlights the practical utility of LLMs and provides actionable insights for integrating them into real-world bioinformatics workflow design.
Multi-output Deep-Supervised Classifier Chains for Plant Pathology
Venue: arXiv cs.CV 2025
Year: 2025

Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.
Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Session-based recommendation (SBR) is mainly based on anonymous user interaction sequences to recommend the items that the next user is most likely to click. Currently, the most popular and high-performing SBR methods primarily leverage graph neural networks (GNNs), which model session sequences as graph-structured data to effectively capture user intent. However, most GNNs-based SBR methods primarily focus on modeling the ID sequence information of session sequences, while neglecting the rich semantic information embedded within them. This limitation significantly hampers model's ability to accurately infer users' true intention. To address above challenge, this paper proposes a novel SBR approach called Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation (LLM-DMsRec). The method utilizes a pre-trained GNN model to select the top-k items as candidate item sets and designs prompts along with a large language model (LLM) to infer multi-semantic intents from these candidate items. Specifically, we propose an alignment mechanism that effectively integrates the semantic intent inferred by the LLM with the structural intent captured by GNNs. Extensive experiments conducted on the Beauty and ML-1M datasets demonstrate that the proposed method can be seamlessly integrated into GNNs framework, significantly enhancing its recommendation performance.
The Policy Cliff: A Theoretical Analysis of Reward-Policy Maps in Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Reinforcement learning (RL) plays a crucial role in shaping the behavior of large language and reasoning models (LLMs/LRMs). However, it often produces brittle and unstable policies, leading to critical failures such as spurious reasoning, deceptive alignment, and instruction disobedience that undermine the trustworthiness and safety of LLMs/LRMs. Currently, these issues lack a unified theoretical explanation and are typically addressed using ad-hoc heuristics. This paper presents a rigorous mathematical framework for analyzing the stability of the mapping from a reward function to the optimal policy. We show that policy brittleness often stems from non-unique optimal actions, a common occurrence when multiple valid traces exist in a reasoning task. This theoretical lens provides a unified explanation for a range of seemingly disparate failures, reframing them as rational outcomes of optimizing rewards that may be incomplete or noisy, especially in the presence of action degeneracy. We extend this analysis from the fundamental single-reward setting to the more realistic multi-reward RL across diverse domains, showing how stability is governed by an "effective reward" aggregation mechanism. We also prove that entropy regularization restores policy stability at the cost of increased stochasticity. Our framework provides a unified explanation for recent empirical findings on deceptive reasoning, instruction-following trade-offs, and RLHF-induced sophistry, and is further validated through perturbation experiments in multi-reward RL. This work advances policy-stability analysis from empirical heuristics towards a principled theory, offering essential insights for designing safer and more trustworthy AI systems.
Sparse Regression Codes for Secret Key Agreement: Achieving Strong Secrecy and Near-Optimal Rates for Gaussian Sources
Venue: arXiv cs.IT 2025
Year: 2025

Secret key agreement from correlated physical layer observations is a cornerstone of information-theoretic security. This paper proposes and rigorously analyzes a complete, constructive protocol for secret key agreement from Gaussian sources using Sparse Regression Codes (SPARCs). Our protocol systematically leverages the known optimality of SPARCs for both rate-distortion and Wyner-Ziv (WZ) coding, facilitated by their inherent nested structure. The primary contribution of this work is a comprehensive end-to-end analysis demonstrating that the proposed scheme achieves near-optimal secret key rates with strong secrecy guarantees, as quantified by a vanishing variational distance. We explicitly characterize the gap to the optimal rate, revealing a fundamental trade-off between the key rate and the required public communication overhead, which is governed by a tunable quantization parameter. Furthermore, we uncover a non-trivial constrained optimization for this parameter, showing that practical constraints on the SPARC code parameters induce a peak in the achievable secret key rate. This work establishes SPARCs as a viable and theoretically sound framework for secure key generation, providing a compelling low-complexity alternative to existing schemes and offering new insights into the practical design of such protocols.
AnimeColor: Reference-based Animation Colorization with Diffusion Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Animation colorization plays a vital role in animation production, yet existing methods struggle to achieve color accuracy and temporal consistency. To address these challenges, we propose \textbf{AnimeColor}, a novel reference-based animation colorization framework leveraging Diffusion Transformers (DiT). Our approach integrates sketch sequences into a DiT-based video diffusion model, enabling sketch-controlled animation generation. We introduce two key components: a High-level Color Extractor (HCE) to capture semantic color information and a Low-level Color Guider (LCG) to extract fine-grained color details from reference images. These components work synergistically to guide the video diffusion process. Additionally, we employ a multi-stage training strategy to maximize the utilization of reference image color information. Extensive experiments demonstrate that AnimeColor outperforms existing methods in color accuracy, sketch alignment, temporal consistency, and visual quality. Our framework not only advances the state of the art in animation colorization but also provides a practical solution for industrial applications. The code will be made publicly available at \href{https://github.com/IamCreateAI/AnimeColor}{https://github.com/IamCreateAI/AnimeColor}.
Practical Multi-Task Learning for Rare Conversions in Ad Tech
Venue: arXiv cs.IR 2025
Year: 2025

We present a Multi-Task Learning (MTL) approach for improving predictions for rare (e.g., <1%) conversion events in online advertising. The conversions are classified into "rare" or "frequent" types based on historical statistics. The model learns shared representations across all signals while specializing through separate task towers for each type. The approach was tested and fully deployed to production, demonstrating consistent improvements in both offline (0.69% AUC lift) and online KPI performance metric (2% Cost per Action reduction).