OpenRaiser/Intern-Atlas huggingface dataset:
Large language models in climate and sustainability policy: limits and opportunities
Venue: arXiv cs.CY 2025
Year: 2025

As multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, complex challenges require timely, updated, and usable information. Natural-language processing (NLP) tools enhance and expand data collection and processing and knowledge utilization capabilities to support the definition of an inclusive, sustainable future. In this work, we apply different NLP techniques, tools and approaches to climate and sustainability documents to derive policy-relevant and actionable measures. We focus on general and domain-specific large language models (LLMs) using a combination of static and prompt-based methods. We find that the use of LLMs is successful at processing, classifying and summarizing heterogeneous text-based data. However, we also encounter challenges related to human intervention across different workflow stages and knowledge utilization for policy processes. Our work presents a critical but empirically grounded application of LLMs to complex policy problems and suggests avenues to further expand Artificial Intelligence-powered computational social sciences.
Understanding User Mental Models in AI-Driven Code Completion Tools: Insights from an Elicitation Study
Venue: arXiv cs.HC 2025
Year: 2025

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers' mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers' coding preferences and environments, ensuring seamless integration into diverse workflows.
Can You Move These Over There? An LLM-based VR Mover for Supporting Object Manipulation
Venue: arXiv cs.HC 2025
Year: 2025

In our daily lives, we can naturally convey instructions for the spatial manipulation of objects using words and gestures. Transposing this form of interaction into virtual reality (VR) object manipulation can be beneficial. We propose VR Mover, an LLM-empowered solution that can understand and interpret the user's vocal instruction to support object manipulation. By simply pointing and speaking, the LLM can manipulate objects without structured input. Our user study demonstrates that VR Mover enhances user usability, overall experience and performance on multi-object manipulation, while also reducing workload and arm fatigue. Users prefer the proposed natural interface for broad movements and may complementarily switch to gizmos or virtual hands for finer adjustments. These findings are believed to contribute to design implications for future LLM-based object manipulation interfaces, highlighting the potential for more intuitive and efficient user interactions in VR environments.
On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in efficient sequence modeling have introduced selective state-space layers, a key component of the Mamba architecture, which have demonstrated remarkable success in a wide range of NLP and vision tasks. While Mamba's empirical performance has matched or surpassed SoTA transformers on such diverse benchmarks, the theoretical foundations underlying its powerful representational capabilities remain less explored. In this work, we investigate the expressivity of selective state-space layers using multivariate polynomials, and prove that they surpass linear transformers in expressiveness. Consequently, our findings reveal that Mamba offers superior representational power over linear attention-based models for long sequences, while not sacrificing their generalization. Our theoretical insights are validated by a comprehensive set of empirical experiments on various datasets.
Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

In real-world recommendation scenarios, users engage with items through various types of behaviors. Leveraging diversified user behavior information for learning can enhance the recommendation of target behaviors (e.g., buy), as demonstrated by recent multi-behavior methods. The mainstream multi-behavior recommendation framework consists of two steps: fusion and prediction. Recent approaches utilize graph neural networks for multi-behavior fusion and employ multi-task learning paradigms for joint optimization in the prediction step, achieving significant success. However, these methods have limited perspectives on multi-behavior fusion, which leads to inaccurate capture of user behavior patterns in the fusion step. Moreover, when using multi-task learning for prediction, the relationship between the target task and auxiliary tasks is not sufficiently coordinated, resulting in negative information transfer. To address these problems, we propose a novel multi-behavior recommendation framework based on the combinatorial optimization perspective, named COPF. Specifically, we treat multi-behavior fusion as a combinatorial optimization problem, imposing different constraints at various stages of each behavior to restrict the solution space, thus significantly enhancing fusion efficiency (COGCN). In the prediction step, we improve both forward and backward propagation during the generation and aggregation of multiple experts to mitigate negative transfer caused by differences in both feature and label distributions (DFME). Comprehensive experiments on three real-world datasets indicate the superiority of COPF. Further analyses also validate the effectiveness of the COGCN and DFME modules. Our code is available at https://github.com/1918190/COPF.
Network Digital Twin for 5G-Enabled Mobile Robots
Venue: arXiv cs.NI 2025
Year: 2025

The maturity and commercial roll-out of 5G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and ubiquitous coverage and wireless connectivity, 5G fully unlocks the potential of robot autonomy and boosts emerging robotic applications, particularly in the domain of autonomous mobile robots. Ensuring seamless, efficient, and reliable navigation and operation of robots within a 5G network requires a clear understanding of the expected network quality in the deployment environment. However, obtaining real-time insights into network conditions, particularly in highly dynamic environments, presents a significant and practical challenge. In this paper, we present a novel framework for building a Network Digital Twin (NDT) using real-time data collected by robots. This framework provides a comprehensive solution for monitoring, controlling, and optimizing robotic operations in dynamic network environments. We develop a pipeline integrating robotic data into the NDT, demonstrating its evolution with real-world robotic traces. We evaluate its performances in radio-aware navigation use case, highlighting its potential to enhance energy efficiency and reliability for 5Genabled robotic operations.
Evalita-LLM: Benchmarking Large Language Models on Italian
Venue: arXiv cs.CL 2025
Year: 2025

We describe Evalita-LLM, a new benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing and innovative features of Evalita-LLM are the following: (i) all tasks are native Italian, avoiding issues of translating from Italian and potential cultural biases; (ii) in addition to well established multiple-choice tasks, the benchmark includes generative tasks, enabling more natural interaction with LLMs; (iii) all tasks are evaluated against multiple prompts, this way mitigating the model sensitivity to specific prompts and allowing a fairer and objective evaluation. We propose an iterative methodology, where candidate tasks and candidate prompts are validated against a set of LLMs used for development. We report experimental results from the benchmark's development phase, and provide performance statistics for several state-of-the-art LLMs.
EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in recent years. The framework of GNN models mainly includes the information propagation phase and the aggregation phase, treating nodes and edges as information entities and propagation channels, respectively. However, most existing GNN models face the challenge of disconnection between node and edge feature information, as these models typically treat the learning of edge and node features as independent tasks. To address this limitation, we aim to develop an edge-empowered graph feature preference learning framework that can capture edge embeddings to assist node embeddings. By leveraging the learned multidimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features, thereby obtaining the non-local structural characteristics and fine-grained high-order node features. Specifically, the inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model, enabling it to handle complex and diverse graph data more effectively. Additionally, integrating relational representation learning into the message passing framework allows graph nodes to receive more useful information, thereby facilitating node representation learning. Finally, experiments on four real-world heterogeneous graphs demonstrate the effectiveness of theproposed model.
Fostering Data Communities -- perspective from a Data Archive Service Provider
Venue: arXiv cs.DL 2025
Year: 2025

This paper aims to bridge between the current scientific discourse about the dynamics of data communities in research infrastructures and practical experiences at a data archive which provides services for such data communities. We describe and analyse policies and practices within DANS-KNAW, the Dutch national centre of expertise and repository for research data concerning the interaction with communities in general. We take the case of the emerging DANS Data Station Life Sciences to study how a data archive navigates between observation of data research needs and anticipation of research data archival solutions. This paper offers a unique view of the complex dynamics between data communities (including lay experts) and data service providers. It adds nuances to understanding the emergence of a data community and the role of data service providers, both supporting and shaping, in this process.
NoteFlow: Recommending Charts as Sight Glasses for Tracing Data Flow in Computational Notebooks
Venue: arXiv cs.HC 2025
Year: 2025

Exploratory Data Analysis (EDA) is a routine task for data analysts, often conducted using flexible computational notebooks. During EDA, data workers process, visualize, and interpret data tables, making decisions about subsequent analysis. However, the cell-by-cell programming approach, while flexible, can lead to disorganized code, making it difficult to trace the state of data tables across cells and increasing the cognitive load on data workers. This paper introduces NoteFlow, a notebook library that recommends charts as ``sight glasses'' for data tables, allowing users to monitor their dynamic updates throughout the EDA process. To ensure visual consistency and effectiveness, NoteFlow adapts chart encodings in response to data transformations, maintaining a coherent and insightful representation of the data. The proposed method was evaluated through user studies, demonstrating its ability to provide an overview of the EDA process and convey critical insights in the data tables.
SHIELD: APT Detection and Intelligent Explanation Using LLM
Venue: arXiv cs.CR 2025
Year: 2025

Advanced persistent threats (APTs) are sophisticated cyber attacks that can remain undetected for extended periods, making their mitigation particularly challenging. Given their persistence, significant effort is required to detect them and respond effectively. Existing provenance-based attack detection methods often lack interpretability and suffer from high false positive rates, while investigation approaches are either supervised or limited to known attacks. To address these challenges, we introduce SHIELD, a novel approach that combines statistical anomaly detection and graph-based analysis with the contextual analysis capabilities of large language models (LLMs). SHIELD leverages the implicit knowledge of LLMs to uncover hidden attack patterns in provenance data, while reducing false positives and providing clear, interpretable attack descriptions. This reduces analysts' alert fatigue and makes it easier for them to understand the threat landscape. Our extensive evaluation demonstrates SHIELD's effectiveness and computational efficiency in real-world scenarios. SHIELD was shown to outperform state-of-the-art methods, achieving higher precision and recall. SHIELD's integration of anomaly detection, LLM-driven contextual analysis, and advanced graph-based correlation establishes a new benchmark for APT detection.
Random Adaptive Cache Placement Policy
Venue: arXiv cs.AR 2025
Year: 2025

This paper presents a new hybrid cache replacement algorithm that combines random allocation with a modified V-Way cache implementation. Our RAC adapts to complex cache access patterns and optimizes cache usage by improving the utilization of cache sets, unlike traditional cache policies. The algorithm utilizes a 16-way set-associative cache with 2048 sets, incorporating dynamic allocation and flexible tag management. RAC extends the V-Way cache design and its variants by optimizing tag and data storage for enhanced efficiency. We evaluated the algorithm using the ChampSim simulator with four diverse benchmark traces and observed significant improvements in cache hit rates up to 80.82% hit rate. Although the improvements in the instructions per cycle (IPC) were moderate, our findings emphasize the algorithm's potential to enhance cache utilization and reduce memory access times.
A Fast Decoding Algorithm for Generalized Reed-Solomon Codes and Alternant Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, it is shown that the syndromes of generalized Reed-Solomon (GRS) codes and alternant codes can be characterized in terms of inverse fast Fourier transform, regardless of code definitions. Then a fast decoding algorithm is proposed, which has a computational complexity of $O(n\log(n-k) + (n-k)\log^2(n-k))$ for all $(n,k)$ GRS codes and $(n,k)$ alternant codes. Particularly, this provides a new decoding method for Goppa codes, which is an important subclass of alternant codes. When decoding the binary Goppa code with length $8192$ and correction capability $128$, the new algorithm is nearly 10 times faster than traditional methods. The decoding algorithm is suitable for the McEliece cryptosystem, which is a candidate for post-quantum cryptography techniques.
SoK: Understanding zk-SNARKs: The Gap Between Research and Practice
Venue: arXiv cs.CR 2025
Year: 2025

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are a powerful tool for proving computation correctness, attracting significant interest from researchers, developers, and users. However, the complexity of zk-SNARKs has created gaps between these groups, hindering progress. Researchers focus on constructing efficient proving systems with stronger security and new properties, while developers and users prioritize toolchains, usability, and compatibility. In this work, we provide a comprehensive study of zk-SNARK, from theory to practice, pinpointing gaps and limitations. We first present a master recipe that unifies the main steps in converting a program into a zk-SNARK. We then classify existing zk-SNARKs according to their key techniques. Our classification addresses the main difference in practically valuable properties between existing zk-SNARK schemes. We survey over 40 zk-SNARKs since 2013 and provide a reference table listing their categories and properties. Following the steps in master recipe, we then survey 11 general-purpose popular used libraries. We elaborate on these libraries' usability, compatibility, efficiency and limitations. Since installing and executing these zk-SNARK systems is challenging, we also provide a completely virtual environment in which to run the compiler for each of them. We identify that the proving system is the primary focus in cryptography academia. In contrast, the constraint system presents a bottleneck in industry. To bridge this gap, we offer recommendations and advocate for the opensource community to enhance documentation, standardization and compatibility.
mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks are increasingly used in safety-critical domains such as robotics and scientific modeling, where strict adherence to output constraints is essential. Methods like POLICE, which are tailored for single convex regions, face challenges when extended to multiple disjoint regions, often leading to constraint violations or unwanted affine behavior across regions. This paper proposes mPOLICE, a new approach that generalizes POLICE to provably enforce affine constraints over multiple disjoint convex regions. At its core, mPOLICE assigns distinct neuron activation patterns to each constrained region, enabling localized affine behavior and avoiding unintended generalization. This is implemented through a layer-wise optimization of the network parameters. Additionally, we introduce a training algorithm that incorporates mPOLICE into conventional deep learning pipelines, balancing task-specific performance with constraint enforcement using periodic sign pattern enforcement. We validate the flexibility and effectiveness of mPOLICE through experiments across various applications, including safety-critical reinforcement learning, implicit 3D shape representation with geometric constraints, and fluid dynamics simulations with boundary condition enforcement. Importantly, mPOLICE incurs no runtime overhead during inference, making it a practical and reliable solution for constraint handling in deep neural networks.
LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models
Venue: arXiv cs.MM 2025
Year: 2025

The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization.
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in foundation models have transformed computer vision, driving significant performance improvements across diverse domains, including digital histopathology. However, the advantages of domain-specific histopathology foundation models over general-purpose models for specialized tasks such as cell analysis remain underexplored. This study investigates the representation learning gap between these two categories by analyzing multi-level patch embeddings applied to cell instance segmentation and classification. We implement an encoder-decoder architecture with a consistent decoder and various encoders. These include convolutional, vision transformer (ViT), and hybrid encoders pre-trained on ImageNet-22K or LVD-142M, representing general-purpose foundation models. These are compared against ViT encoders from the recently released UNI, Virchow2, and Prov-GigaPath foundation models, trained on patches extracted from hundreds of thousands of histopathology whole-slide images. The decoder integrates patch embeddings from different encoder depths via skip connections to generate semantic and distance maps. These maps are then post-processed to create instance segmentation masks where each label corresponds to an individual cell and to perform cell-type classification. All encoders remain frozen during training to assess their pre-trained feature extraction capabilities. Using the PanNuke and CoNIC histopathology datasets, and the newly introduced Nissl-stained CytoDArk0 dataset for brain cytoarchitecture studies, we evaluate instance-level detection, segmentation accuracy, and cell-type classification. This study provides insights into the comparative strengths and limitations of general-purpose vs. histopathology foundation models, offering guidance for model selection in cell-focused histopathology and brain cytoarchitecture analysis workflows.