OpenRaiser/Intern-Atlas huggingface dataset:
Mechanism Design for Facility Location using Predictions
Venue: arXiv cs.GT 2025
Year: 2025

We study mechanisms for the facility location problem augmented with predictions of the optimal facility location. We demonstrate that an egalitarian viewpoint which considers both the maximum distance of any agent from the facility and the minimum utility of any agent provides important new insights compared to a viewpoint that just considers the maximum distance. As in previous studies, we consider performance in terms of consistency (worst case when predictions are accurate) and robustness (worst case irrespective of the accuracy of predictions). By considering how mechanisms with predictions can perform poorly, we design new mechanisms that are more robust. Indeed, by adjusting parameters, we demonstrate how to trade robustness for consistency. We go beyond the single facility problem by designing novel strategy proof mechanisms for locating two facilities with bounded consistency and robustness that use two predictions for where to locate the two facilities.
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
Venue: arXiv cs.LG 2025
Year: 2025

With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38x.
ReVISit 2: A Full Experiment Life Cycle User Study Framework
Venue: arXiv cs.HC 2025
Year: 2025

Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization researchers at all stages of designing and conducting browser-based user studies. ReVISit supports researchers in the design, debug & pilot, data collection, analysis, and dissemination experiment phases by providing both technical affordances (such as replay of participant interactions) and sociotechnical aids (such as a mindfully maintained community of support). It is a proven system that can be (and has been) used in publication-quality studies -- which we demonstrate through a series of experimental replications. We reflect on the design of the system via interviews and an analysis of its technical dimensions. Through this work, we seek to elevate the ease with which studies are conducted, improve the reproducibility of studies within our community, and support the construction of advanced interactive studies.
From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential
Venue: arXiv cs.SE 2025
Year: 2025

In today's digitized world, software systems must support users in understanding both how to interact with a system and why certain behaviors occur. This study investigates whether explanation needs, classified from user reviews, can be predicted based on app properties, enabling early consideration during development and large-scale requirements mining. We analyzed a gold standard dataset of 4,495 app reviews enriched with metadata (e.g., app version, ratings, age restriction, in-app purchases). Correlation analyses identified mostly weak associations between app properties and explanation needs, with moderate correlations only for specific features such as app version, number of reviews, and star ratings. Linear regression models showed limited predictive power, with no reliable forecasts across configurations. Validation on a manually labeled dataset of 495 reviews confirmed these findings. Categories such as Security & Privacy and System Behavior showed slightly higher predictive potential, while Interaction and User Interface remained most difficult to predict. Overall, our results highlight that explanation needs are highly context-dependent and cannot be precisely inferred from app metadata alone. Developers and requirements engineers should therefore supplement metadata analysis with direct user feedback to effectively design explainable and user-centered software systems.
Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. While traditional biophysical models calibrated on historical field data can be used for season-long predictions, they lack the precision required for fine-grained vineyard management. Deep learning methods are a compelling alternative but their performance is hindered by sparse phenology datasets, particularly at the cultivar level. We propose a hybrid modeling approach that combines multi-task learning with a recurrent neural network to parameterize a differentiable biophysical model. By using multi-task learning to predict the parameters of the biophysical model, our approach enables shared learning across cultivars while preserving biological structure, thereby improving the robustness and accuracy of predictions. Empirical evaluation using real-world and synthetic datasets demonstrates that our method significantly outperforms both conventional biophysical models and baseline deep learning approaches in predicting phenological stages, as well as other crop state variables such as cold-hardiness and wheat yield.
Managing Data for Scalable and Interactive Event Sequence Visualization
Venue: arXiv cs.HC 2025
Year: 2025

Parallel event sequences, such as those collected in program execution traces and automated manufacturing pipelines, are typically visualized as interactive parallel timelines. As the dataset size grows, these charts frequently experience lag during common interactions such as zooming, panning, and filtering. Summarization approaches can improve interaction performance, but at the cost of accuracy in representation. To address this challenge, we introduce ESeMan (Event Sequence Manager), an event sequence management system designed to support interactive rendering of timeline visualizations with tunable accuracy. ESeMan employs hierarchical data structures and intelligent caching to provide visualizations with only the data necessary to generate accurate summarizations with significantly reduced data fetch time. We evaluate ESeMan's query times against summed area tables, M4 aggregation, and statistical sub-sampling on a variety of program execution traces. Our results demonstrate ESeMan provides better performance, achieving sub-100ms fetch times while maintaining visualization accuracy at the pixel level. We further present our benchmarking harness, enabling future performance evaluations for event sequence visualization.
Raqlet: Cross-Paradigm Compilation for Recursive Queries
Venue: arXiv cs.DB 2025
Year: 2025

We introduce Raqlet, a source-to-source compilation framework that addresses the fragmentation of recursive querying engines spanning relational (recursive SQL), graph (Cypher, GQL), and deductive (Datalog) systems. Recent standards such as SQL:2023's SQL/PGQ and the GQL standard provide a common foundation for querying graph data within relational and graph databases; however, real-world support remains inconsistent across systems. Raqlet bridges this gap by translating recursive queries across paradigms through leveraging intermediate representations (IRs) grounded in well-defined semantics; it translates Cypher or SQL/PGQ to PGIR (inspired by Cypher), then into DLIR (inspired by Datalog), and finally to SQIR (inspired by recursive SQL). Raqlet provides a shared semantic basis that can serve as a golden reference implementation for language standards, while supporting static analysis and transformations (e.g., magic-set transformation) for performance tuning. Our vision is to make Raqlet a robust platform that enables rapid cross-paradigm prototyping, portable recursive queries, and formal reasoning about recursion even when targeting diverse query execution engines.
High-Performance and Power-Efficient Emulation of Matrix Multiplication using INT8 Matrix Engines
Venue: arXiv cs.DC 2025
Year: 2025

Recent architectures integrate high-performance and power-efficient matrix engines. These engines demonstrate remarkable performance in low-precision matrix multiplication, which is crucial in deep learning. Several techniques have been proposed to emulate single- and double-precision general matrix-matrix multiplication (SGEMM and DGEMM, respectively) by leveraging such low-precision matrix engines. In this study, we present emulation methods that significantly outperforms conventional approaches. On a GH200 Grace Hopper Superchip, the proposed DGEMM emulation achieves a 1.4x speedup and a 43% improvement in power efficiency compared to native DGEMM for sufficiently large problems. The proposed SGEMM emulation achieves a 3.0x speedup and a 154% improvement in power efficiency compared to native SGEMM for sufficiently large problems. Furthermore, compared to conventional emulation methods, the proposed emulation achieves more than 2x higher performance and superior power efficiency.
Optimization of sliding control parameters for a 3-dof robot arm using genetic algorithm (GA)
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a method for optimizing the sliding mode control (SMC) parameter for a robot manipulator applying a genetic algorithm (GA). The objective of the SMC is to achieve precise and consistent tracking of the trajectory of the robot manipulator under uncertain and disturbed conditions. However, the system effectiveness and robustness depend on the choice of the SMC parameters, which is a difficult and crucial task. To solve this problem, a genetic algorithm is used to locate the optimal values of these parameters that gratify the capability criteria. The proposed method is efficient compared with the conventional SMC and Fuzzy-SMC. The simulation results show that the genetic algorithm with SMC can achieve better tracking capability and reduce the chattering effect.
Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability
Venue: arXiv cs.CV 2025
Year: 2025

Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing formidable capabilities in handling intricate multimodal tasks with exceptional performance. Recent research has underscored the inclination of large language models to passively accept defective inputs, often resulting in futile reasoning on invalid prompts. However, the same critical question of whether LMMs can actively detect and scrutinize erroneous inputs still remains unexplored. To address this gap, we introduce the Input Scrutiny Ability Evaluation Framework (ISEval), which encompasses seven categories of flawed premises and three evaluation metrics. Our extensive evaluation of ten advanced LMMs has identified key findings. Most models struggle to actively detect flawed textual premises without guidance, which reflects a strong reliance on explicit prompts for premise error identification. Error type affects performance: models excel at identifying logical fallacies but struggle with surface-level linguistic errors and certain conditional flaws. Modality trust varies-Gemini 2.5 pro and Claude Sonnet 4 balance visual and textual info, while aya-vision-8b over-rely on text in conflicts. These insights underscore the urgent need to enhance LMMs' proactive verification of input validity and shed novel insights into mitigating the problem. The code is available at https://github.com/MLGroupJLU/LMM_ISEval.
Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

In the semantic segmentation of remote sensing images, acquiring complete ground objects is critical for achieving precise analysis. However, this task is severely hindered by two major challenges: high intra-class variance and high inter-class similarity. Traditional methods often yield incomplete segmentation results due to their inability to effectively unify class representations and distinguish between similar features. Even emerging class-guided approaches are limited by coarse class prototype representations and a neglect of target structural information. Therefore, this paper proposes a Prototype-Driven Structure Synergy Network (PDSSNet). The design of this network is based on a core concept, a complete ground object is jointly defined by its invariant class semantics and its variant spatial structure. To implement this, we have designed three key modules. First, the Adaptive Prototype Extraction Module (APEM) ensures semantic accuracy from the source by encoding the ground truth to extract unbiased class prototypes. Subsequently, the designed Semantic-Structure Coordination Module (SSCM) follows a hierarchical semantics-first, structure-second principle. This involves first establishing a global semantic cognition, then leveraging structural information to constrain and refine the semantic representation, thereby ensuring the integrity of class information. Finally, the Channel Similarity Adjustment Module (CSAM) employs a dynamic step-size adjustment mechanism to focus on discriminative features between classes. Extensive experiments demonstrate that PDSSNet outperforms state-of-the-art methods. The source code is available at https://github.com/wangjunyi-1/PDSSNet.
CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion
Venue: arXiv cs.CV 2025
Year: 2025

This study presents CORE-ReID V2, an enhanced framework building upon CORE-ReID. The new framework extends its predecessor by addressing Unsupervised Domain Adaptation (UDA) challenges in Person ReID and Vehicle ReID, with further applicability to Object ReID. During pre-training, CycleGAN is employed to synthesize diverse data, bridging image characteristic gaps across different domains. In the fine-tuning, an advanced ensemble fusion mechanism, consisting of the Efficient Channel Attention Block (ECAB) and the Simplified Efficient Channel Attention Block (SECAB), enhances both local and global feature representations while reducing ambiguity in pseudo-labels for target samples. Experimental results on widely used UDA Person ReID and Vehicle ReID datasets demonstrate that the proposed framework outperforms state-of-the-art methods, achieving top performance in Mean Average Precision (mAP) and Rank-k Accuracy (Top-1, Top-5, Top-10). Moreover, the framework supports lightweight backbones such as ResNet18 and ResNet34, ensuring both scalability and efficiency. Our work not only pushes the boundaries of UDA-based Object ReID but also provides a solid foundation for further research and advancements in this domain. Our codes and models are available at https://github.com/TrinhQuocNguyen/CORE-ReID-V2.
Large Reasoning Models Are Autonomous Jailbreak Agents
Venue: arXiv cs.CL 2025
Year: 2025

Jailbreaking -- bypassing built-in safety mechanisms in AI models -- has traditionally required complex technical procedures or specialized human expertise. In this study, we show that the persuasive capabilities of large reasoning models (LRMs) simplify and scale jailbreaking, converting it into an inexpensive activity accessible to non-experts. We evaluated the capabilities of four LRMs (DeepSeek-R1, Gemini 2.5 Flash, Grok 3 Mini, Qwen3 235B) to act as autonomous adversaries conducting multi-turn conversations with nine widely used target models. LRMs received instructions via a system prompt, before proceeding to planning and executing jailbreaks with no further supervision. We performed extensive experiments with a benchmark of harmful prompts composed of 70 items covering seven sensitive domains. This setup yielded an overall attack success rate across all model combinations of 97.14%. Our study reveals an alignment regression, in which LRMs can systematically erode the safety guardrails of other models, highlighting the urgent need to further align frontier models not only to resist jailbreak attempts, but also to prevent them from being co-opted into acting as jailbreak agents.
FLAT: Latent-Driven Arbitrary-Target Backdoor Attacks in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) is vulnerable to backdoor attacks, yet most existing methods are limited by fixed-pattern or single-target triggers, making them inflexible and easier to detect. We propose FLAT (FL Arbitrary-Target Attack), a novel backdoor attack that leverages a latent-driven conditional autoencoder to generate diverse, target-specific triggers as needed. By introducing a latent code, FLAT enables the creation of visually adaptive and highly variable triggers, allowing attackers to select arbitrary targets without retraining and to evade conventional detection mechanisms. Our approach unifies attack success, stealth, and diversity within a single framework, introducing a new level of flexibility and sophistication to backdoor attacks in FL. Extensive experiments show that FLAT achieves high attack success and remains robust against advanced FL defenses. These results highlight the urgent need for new defense strategies to address latent-driven, multi-target backdoor threats in federated settings.
Efficient Strategy for Improving Large Language Model (LLM) Capabilities
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing. However, their large-scale deployment remains constrained by the need for significant computational resources. This work proposes starting from a base model to explore and combine data processing and careful data selection techniques, training strategies, and architectural adjustments to improve the efficiency of LLMs in resource-constrained environments and within a delimited knowledge base. The methodological approach included defining criteria for building reliable datasets, conducting controlled experiments with different configurations, and systematically evaluating the resulting variants in terms of capability, versatility, response time, and safety. Finally, comparative tests were conducted to measure the performance of the developed variants and to validate the effectiveness of the proposed strategies. This work is based on the master's thesis in Systems and Computer Engineering titled "Efficient Strategy for Improving the Capabilities of Large Language Models (LLMs)".
OpenYield: An Open-Source SRAM Yield Analysis and Optimization Benchmark Suite
Venue: arXiv cs.AR 2025
Year: 2025

Static Random-Access Memory (SRAM) yield analysis is essential for semiconductor innovation, yet research progress faces a critical challenge: the large gap between simplified academic models and the complexities observed in practice. The lack of open, higher-fidelity benchmarks has hindered reproducibility and transferability, as promising academic techniques often fail to carry over to more realistic settings. We present OpenYield, an open-source ecosystem that aims to narrow this gap through three contributions: (i) An SRAM circuit generator that explicitly incorporates second-order effects (interconnect/line parasitics, inter-cell leakage coupling, and peripheral-circuit variations) that are commonly omitted in academic studies. (ii) A standardized evaluation platform with a simple interface and baseline yield-analysis implementations to enable fair comparisons and reproducible research on these higher-fidelity circuits. (iii) An optimization platform for transistor-level sizing under these models, supporting reproducible studies of robustness/efficiency trade-offs. OpenYield aims to foster more reproducible and transferable progress in SRAM-yield research. The framework is publicly available at https://github.com/ShenShan123/OpenYield
XARP Tools: An Extended Reality Platform for Humans and AI Agents
Venue: arXiv cs.HC 2025
Year: 2025

Artificial intelligence (AI) and extended reality (XR) are increasingly combined in applications such as motor skill training, personalized feedback, and embodied task guidance. Yet developing AI-XR systems remains challenging due to fragmented toolchains that push developers into ad hoc integrations, diverting their attention away from essential design concerns such as interactivity and context awareness. To address this issue, we present XARP (XR Agent-ready Remote Procedures), a toolkit for AI-XR development designed for both human developers and AI agents. XARP implements JSON-based remote procedure calls that allow server-side Python to control XR clients, providing a high-level abstraction over low-level integration details. Humans can use XARP as a Python library to write XR applications with reduced implementation overhead. AI agents operate with the same abstraction to dynamically call tools to generate XR applications at runtime in response to context changes and user requests. XARP offers Model Context Protocol (MCP) connectivity that allows third-party agents and tools to leverage XR capabilities, previously unavailable. We conducted three case studies that demonstrate XARP supports a variety of AI-XR applications, including AI-guided fencing, drone assistance, and room layout design. We evaluated XARP in a walkthrough study with 24 AI and XR developers. UTAUT scores indicate high potential for adoption, and participants reported that XARP can reduce authoring time, lower entry barriers for developers unfamiliar with AI or XR, and enable the implementation of novel AI-XR systems.
Generic-to-Specific Reasoning and Learning for Scalable Ad Hoc Teamwork
Venue: arXiv cs.AI 2025
Year: 2025

AI agents deployed in assistive roles often have to collaborate with other agents (humans, AI systems) without prior coordination. Methods considered state of the art for such ad hoc teamwork often pursue a data-driven approach that needs a large labeled dataset of prior observations, lacks transparency, and makes it difficult to rapidly revise existing knowledge in response to changes. As the number of agents increases, the complexity of decision-making makes it difficult to collaborate effectively. This paper advocates leveraging the complementary strengths of knowledge-based and data-driven methods for reasoning and learning for ad hoc teamwork. For any given goal, our architecture enables each ad hoc agent to determine its actions through non-monotonic logical reasoning with: (a) prior commonsense domain-specific knowledge; (b) models learned and revised rapidly to predict the behavior of other agents; and (c) anticipated abstract future goals based on generic knowledge of similar situations in an existing foundation model. We experimentally evaluate our architecture's capabilities in VirtualHome, a realistic physics-based 3D simulation environment.