OpenRaiser/Intern-Atlas huggingface dataset:
Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operation Orchestration
Venue: arXiv cs.SE 2025
Year: 2025

Workflow automation promises substantial productivity gains in everyday document-related tasks. While prior agentic systems can execute isolated instructions, they struggle with automating multi-step, session-level workflows due to limited control over the operational process. To this end, we introduce AutoDW, a novel execution framework that enables stepwise, rollback-enabled operation orchestration. AutoDW incrementally plans API actions conditioned on user instructions, intent-filtered API candidates, and the evolving states of the document. It further employs robust rollback mechanisms at both the argument and API levels, enabling dynamic correction and fault tolerance. These designs together ensure that the execution trajectory of AutoDW remains aligned with user intent and document context across long-horizon workflows. To assess its effectiveness, we construct a comprehensive benchmark of 250 sessions and 1,708 human-annotated instructions, reflecting realistic document processing scenarios with interdependent instructions. AutoDW achieves 90% and 62% completion rates on instruction- and session-level tasks, respectively, outperforming strong baselines by 40% and 76%. Moreover, AutoDW also remains robust for the decision of backbone LLMs and on tasks with varying difficulty. Code and data will be open-sourced. Code: https://github.com/YJett/AutoDW
Vision-Language-Action Models for Selective Robotic Disassembly: A Case Study on Critical Component Extraction from Desktops
Venue: arXiv cs.RO 2025
Year: 2025

Automating disassembly of critical components from end-of-life (EoL) desktops, such as high-value items like RAM modules and CPUs, as well as sensitive parts like hard disk drives, remains challenging due to the inherent variability and uncertainty of these products. Moreover, their disassembly requires sequential, precise, and dexterous operations, further increasing the complexity of automation. Current robotic disassembly processes are typically divided into several stages: perception, sequence planning, task planning, motion planning, and manipulation. Each stage requires explicit modeling, which limits generalization to unfamiliar scenarios. Recent development of vision-language-action (VLA) models has presented an end-to-end approach for general robotic manipulation tasks. Although VLAs have demonstrated promising performance on simple tasks, the feasibility of applying such models to complex disassembly remains largely unexplored. In this paper, we collected a customized dataset for robotic RAM and CPU disassembly and used it to fine-tune two well-established VLA approaches, OpenVLA and OpenVLA-OFT, as a case study. We divided the whole disassembly task into several small steps, and our preliminary experimental results indicate that the fine-tuned VLA models can faithfully complete multiple early steps but struggle with certain critical subtasks, leading to task failure. However, we observed that a simple hybrid strategy that combines VLA with a rule-based controller can successfully perform the entire disassembly operation. These findings highlight the current limitations of VLA models in handling the dexterity and precision required for robotic EoL product disassembly. By offering a detailed analysis of the observed results, this study provides insights that may inform future research to address current challenges and advance end-to-end robotic automated disassembly.
Offloading to CXL-based Computational Memory
Venue: arXiv cs.DC 2025
Year: 2025

CXL-based Computational Memory (CCM) enables near-memory processing within expanded remote memory, presenting opportunities to address data movement costs associated with disaggregated memory systems and to accelerate overall performance. However, existing operation offloading mechanisms are not capable of leveraging the trade-offs of different models based on different CXL protocols. This work first examines these tradeoffs and demonstrates their impact on end-to-end performance and system efficiency for workloads with diverse data and processing requirements. We propose a novel 'Asynchronous Back-Streaming' protocol by carefully layering data and control transfer operations on top of the underlying CXL protocols. We design KAI, a system that realizes the asynchronous back-streaming model that supports asynchronous data movement and lightweight pipelining in host-CCM interactions. Overall, KAI reduces end-to-end runtime by up to 50.4%, and CCM and host idle times by average 22.11x and 3.85x, respectively.
Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval
Venue: arXiv cs.LG 2025
Year: 2025

Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-Based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.
Prompt2Craft: Generating Functional Craft Assemblies with LLMs
Venue: arXiv cs.CV 2025
Year: 2025

Inspired by traditional handmade crafts, where a person improvises assemblies based on the available objects, we formally introduce the Craft Assembly Task. It is a robotic assembly task that involves building an accurate representation of a given target object using the available objects, which do not directly correspond to its parts. In this work, we focus on selecting the subset of available objects for the final craft, when the given input is an RGB image of the target in the wild. We use a mask segmentation neural network to identify visible parts, followed by retrieving labeled template meshes. These meshes undergo pose optimization to determine the most suitable template. Then, we propose to simplify the parts of the transformed template mesh to primitive shapes like cuboids or cylinders. Finally, we design a search algorithm to find correspondences in the scene based on local and global proportions. We develop baselines for comparison that consider all possible combinations, and choose the highest scoring combination for common metrics used in foreground maps and mask accuracy. Our approach achieves comparable results to the baselines for two different scenes, and we show qualitative results for an implementation in a real-world scenario.
Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Unmanned aerial vehicle (UAV) target tracking based on thermal infrared imaging has been one of the most important sensing technologies in anti-UAV applications. However, the infrared UAV targets often exhibit weak features and complex backgrounds, posing significant challenges to accurate tracking. To address these problems, we introduce SiamDFF, a novel dynamic feature fusion Siamese network that integrates feature enhancement and global contextual attention knowledge distillation for infrared UAV target (IRUT) tracking. The SiamDFF incorporates a selective target enhancement network (STEN), a dynamic spatial feature aggregation module (DSFAM), and a dynamic channel feature aggregation module (DCFAM). The STEN employs intensity-aware multi-head cross-attention to adaptively enhance important regions for both template and search branches. The DSFAM enhances multi-scale UAV target features by integrating local details with global features, utilizing spatial attention guidance within the search frame. The DCFAM effectively integrates the mixed template generated from STEN in the template branch and original template, avoiding excessive background interference with the template and thereby enhancing the emphasis on UAV target region features within the search frame. Furthermore, to enhance the feature extraction capabilities of the network for IRUT without adding extra computational burden, we propose a novel tracking-specific target-aware contextual attention knowledge distiller. It transfers the target prior from the teacher network to the student model, significantly improving the student network's focus on informative regions at each hierarchical level of the backbone network. Extensive experiments on real infrared UAV datasets demonstrate that the proposed approach outperforms state-of-the-art target trackers under complex backgrounds while achieving a real-time tracking speed.
The Ethics of Generative AI
Venue: arXiv cs.AI 2025
Year: 2025

This chapter discusses the ethics of generative AI. It provides a technical primer to show how generative AI affords experiencing technology as if it were human, and this affordance provides a fruitful focus for the philosophical ethics of generative AI. It then shows how generative AI can both aggravate and alleviate familiar ethical concerns in AI ethics, including responsibility, privacy, bias and fairness, and forms of alienation and exploitation. Finally, the chapter examines ethical questions that arise specifically from generative AI's mimetic generativity, such as debates about authorship and credit, the emergence of as-if social relationships with machines, and new forms of influence, persuasion, and manipulation.
Reflection-Satisfaction Tradeoff: Investigating Impact of Reflection on Student Engagement with AI-Generated Programming Hints
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI tools, such as AI-generated hints, are increasingly integrated into programming education to offer timely, personalized support. However, little is known about how to effectively leverage these hints while ensuring autonomous and meaningful learning. One promising approach involves pairing AI-generated hints with reflection prompts, asking students to review and analyze their learning, when they request hints. This study investigates the interplay between AI-generated hints and different designs of reflection prompts in an online introductory programming course. We conducted a two-trial field experiment. In Trial 1, students were randomly assigned to receive prompts either before or after receiving hints, or no prompt at all. Each prompt also targeted one of three SRL phases: planning, monitoring, and evaluation. In Trial 2, we examined two types of prompt guidance: directed (offering more explicit and structured guidance) and open (offering more general and less constrained guidance). Findings show that students in the before-hint (RQ1), planning (RQ2), and directed (RQ3) prompt groups produced higher-quality reflections but reported lower satisfaction with AI-generated hints than those in other conditions. Immediate performance did not differ across conditions. This negative relationship between reflection quality and hint satisfaction aligns with previous work on student mental effort and satisfaction. Our results highlight the need to reconsider how AI models are trained and evaluated for education, as prioritizing user satisfaction can undermine deeper learning.
Turbo-Muon: Accelerating Orthogonality-Based Optimization with Pre-Conditioning
Venue: arXiv cs.AI 2025
Year: 2025

Orthogonality-based optimizers, such as Muon, have recently shown strong performance across large-scale training and community-driven efficiency challenges. However, these methods rely on a costly gradient orthogonalization step. Even efficient iterative approximations such as Newton-Schulz remain expensive, typically requiring dozens of matrix multiplications to converge. We introduce a preconditioning procedure that accelerates Newton-Schulz convergence and reduces its computational cost. We evaluate its impact and show that the overhead of our preconditioning can be made negligible. Furthermore, the faster convergence it enables allows us to remove one iteration out of the usual five without degrading approximation quality. Our publicly available implementation achieves up to a 2.8x speedup in the Newton-Schulz approximation. We also show that this has a direct impact on end-to-end training runtime with 5-10% improvement in realistic training scenarios across two efficiency-focused tasks. On challenging language or vision tasks, we validate that our method maintains equal or superior model performance while improving runtime. Crucially, these improvements require no hyperparameter tuning and can be adopted as a simple drop-in replacement. Our code is publicly available on github.
When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit
Venue: arXiv cs.SI 2025
Year: 2025

AI-generated content and misinformation are increasingly prevalent on social networks. While prior research primarily examined textual misinformation, fewer studies have focused on visual content's role in virality. In this work, we present the first large-scale analysis of how misinformation and AI-generated images propagate through repost cascades across five ideologically diverse Reddit communities. By integrating textual sentiment, visual attributes, and diffusion metrics (e.g., time-to-first repost, community reach), our framework accurately predicts both immediate post-level virality (AUC=0.83) and long-term cascade-level spread (AUC=0.998). These findings offer essential insights for moderating synthetic and misleading visual content online.
Cross-Task Benchmarking and Evaluation of General-Purpose and Code-Specific Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have revolutionized both general natural language processing and domain-specific applications such as code synthesis, legal reasoning, and finance. However, while prior studies have explored individual model capabilities, a systematic cross-domain comparison that unifies linguistic, reasoning, and code understanding abilities remains underexplored. In this work, we present a comprehensive evaluation of five general-purpose and three code-specific state-of-the-art LLMs across six diverse benchmarks encompassing linguistic competence, mathematical reasoning, and trustworthiness. Additionally, we analyze model behavior on the CoNaLa dataset for code explanation, comparing natural language and code-specialized LLMs. Our findings reveal that models optimized for code (e.g., CodeLLaMA variants) exhibit strong reasoning and syntactic precision, that even for non-coding tasks can show measurable performance gains, in contrast to general-purpose models like Mistral-7B and Llama-3-8B.
Cryptanalysis of Gleeok-128
Venue: arXiv cs.CR 2025
Year: 2025

Gleeok is a family of low latency keyed pseudorandom functions (PRFs) consisting of three parallel SPN based permutations whose outputs are XORed to form the final value. Both Gleeok-128 and Gleeok-256 use a 256 bit key, with block sizes of 128 and 256 bits, respectively. Owing to its multi branch structure, evaluating security margins and mounting effective key recovery attacks present nontrivial challenges. This paper provides the first comprehensive third party cryptanalysis of Gleeok-128. We introduce a two stage MILP based framework for constructing branch wise and full cipher differential linear (DL) distinguishers, together with an integral based key recovery framework tailored to multi branch designs. Our DL analysis yields 7, 7, 8, and 4 round distinguishers for Branch 1, Branch 2, Branch 3, and Gleeok-128, respectively, with squared correlations approximately 2 to the power minus 88.12, 2 to the power minus 88.12, 2 to the power minus 38.73, and 2 to the power minus 49.04, outperforming those in the design document except for the full PRF case. By tightening algebraic degree bounds, we further derive 9, 9, and 7 round integral distinguishers for the three branches and a 7 round distinguisher for the full PRF, extending the designers results by 3, 3, and 2 rounds and by 2 rounds, respectively. These integral properties enable 7 round and 8 round key recovery attacks in the non full codebook and full codebook settings. In addition, we identify a flaw in the original linear security evaluation of Branch 3, showing that it can be distinguished over all 12 rounds with data complexity about 2 to the power 48. We also propose optimized linear layer parameters that significantly improve linear resistance without sacrificing diffusion. Our results advance the understanding of Gleeok-128 and provide general methods for analyzing multi branch symmetric designs.
Towards Cross-View Point Correspondence in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Cross-view correspondence is a fundamental capability for spatial understanding and embodied AI. However, it is still far from being realized in Vision-Language Models (VLMs), especially in achieving precise point-level correspondence, which is crucial for precise affordance interaction. So we propose the Cross-View Point Correspondence (CVPC) task and CrossPoint-Bench, a comprehensive benchmark with hierarchical design, inspired by the human cognitive process of "perceive", "reason", and "correspond". Our evaluation shows the state-of-the-art models (e.g., Gemini-2.5-Pro) still fall far behind humans, with a gap of over 54.65% in overall accuracy, exposing a challenge in transitioning from coarse-grained judgement to fine-grained coordinate prediction. To address this problem, we construct CrossPoint-378K, a dataset with 378K question-answering pairs across 900 scenes, focused on actionable affordance regions that better reflect real-world manipulation and interaction scenarios. Furthermore, we propose CroPond that trained on the CrossPoint-378K dataset. Our CroPond achieves state-of-the-art performance on CrossPoint-Bench, surpassing Gemini-2.5-Pro by 39.7% accuracy, which offers a foundation for advancing future work on cross-view correspondence. The benchmark, dataset, and model are publicly available at https://github.com/WangYipu2002/CrossPoint.
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Extreme low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2-bits and even 4-bits (e.g., MXFP4). We present SignRoundV2, a post-training quantization framework that is highly effective even without mixed-precision. SignRoundV2 introduces (1) a fast sensitivity metric that combines gradient information with quantization-induced deviations to guide layer-wise bit allocation, and (2) a lightweight pre-tuning search for quantization scales to improve extremely low-bit quantization. These components allow SignRoundV2 to close the gap with full-precision models. Extensive experiments indicate that our method sustains competitive accuracy for LLMs, achieving production-grade performance with about 1 percent variance at 4-5 bits and strong results even at 2 bits. The implementation is available at https://github.com/intel/auto-round.
Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations
Venue: arXiv cs.CE 2025
Year: 2025

The increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.
YingMusic-SVC: Real-World Robust Zero-Shot Singing Voice Conversion with Flow-GRPO and Singing-Specific Inductive Biases
Venue: arXiv cs.SD 2025
Year: 2025

Singing voice conversion (SVC) aims to render the target singer's timbre while preserving melody and lyrics. However, existing zero-shot SVC systems remain fragile in real songs due to harmony interference, F0 errors, and the lack of inductive biases for singing. We propose YingMusic-SVC, a robust zero-shot framework that unifies continuous pre-training, robust supervised fine-tuning, and Flow-GRPO reinforcement learning. Our model introduces a singing-trained RVC timbre shifter for timbre-content disentanglement, an F0-aware timbre adaptor for dynamic vocal expression, and an energy-balanced rectified flow matching loss to enhance high-frequency fidelity. Experiments on a graded multi-track benchmark show that YingMusic-SVC achieves consistent improvements over strong open-source baselines in timbre similarity, intelligibility, and perceptual naturalness, especially under accompanied and harmony-contaminated conditions, demonstrating its effectiveness for real-world SVC deployment.
DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors
Venue: arXiv cs.CL 2025
Year: 2025

We present an enhanced benchmark for evaluating linguistic acceptability in Danish. We first analyze the most common errors found in written Danish. Based on this analysis, we introduce a set of fourteen corruption functions that generate incorrect sentences by systematically introducing errors into existing correct Danish sentences. To ensure the accuracy of these corruptions, we assess their validity using both manual and automatic methods. The results are then used as a benchmark for evaluating Large Language Models on a linguistic acceptability judgement task. Our findings demonstrate that this extension is both broader and more comprehensive than the current state of the art. By incorporating a greater variety of corruption types, our benchmark provides a more rigorous assessment of linguistic acceptability, increasing task difficulty, as evidenced by the lower performance of LLMs on our benchmark compared to existing ones. Our results also suggest that our benchmark has a higher discriminatory power which allows to better distinguish well-performing models from low-performing ones.