OpenRaiser/Intern-Atlas huggingface dataset:
On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models
Venue: arXiv cs.HC 2025
Year: 2025

Music and language are structurally similar. Such structural similarity is often explained by generative processes. This paper describes the recent development of probabilistic generative models (PGMs) for language learning and symbol emergence in robotics. Symbol emergence in robotics aims to develop a robot that can adapt to real-world environments and human linguistic communications and acquire language from sensorimotor information alone (i.e., in an unsupervised manner). This is regarded as a constructive approach to symbol emergence systems. To this end, a series of PGMs have been developed, including those for simultaneous phoneme and word discovery, lexical acquisition, object and spatial concept formation, and the emergence of a symbol system. By extending the models, a symbol emergence system comprising a multi-agent system in which a symbol system emerges is revealed to be modeled using PGMs. In this model, symbol emergence can be regarded as collective predictive coding. This paper expands on this idea by combining the theory that ''emotion is based on the predictive coding of interoceptive signals'' and ''symbol emergence systems,'' and describes the possible hypothesis of the emergence of meaning in music.
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions.
Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways
Venue: arXiv cs.IT 2025
Year: 2025

5G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non stationarity. Key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes are also obtained. Finally, accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design, optimization, and link-level simulation and validation of 5G-R systems.
Prioritized Value-Decomposition Network for Explainable AI-Enabled Network Slicing
Venue: arXiv cs.NI 2025
Year: 2025

Network slicing aims to enhance flexibility and efficiency in next-generation wireless networks by allocating the right resources to meet the diverse requirements of various applications. Managing these slices with machine learning (ML) algorithms has emerged as a promising approach however explainability has been a challenge. To this end, several Explainable Artificial Intelligence (XAI) frameworks have been proposed to address the opacity in decision-making in many ML methods. In this paper, we propose a Prioritized Value-Decomposition Network (PVDN) as an XAI-driven approach for resource allocation in a multi-agent network slicing system. The PVDN method decomposes the global value function into individual contributions and prioritizes slice outputs, providing an explanation of how resource allocation decisions impact system performance. By incorporating XAI, PVDN offers valuable insights into the decision-making process, enabling network operators to better understand, trust, and optimize slice management strategies. Through simulations, we demonstrate the effectiveness of the PVDN approach with improving the throughput by 67% and 16%, while reducing latency by 35% and 22%, compared to independent and VDN-based resource allocation methods.
LemmaHead: RAG Assisted Proof Generation Using Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Developing the logic necessary to solve mathematical problems or write mathematical proofs is one of the more difficult objectives for large language models (LLMS). Currently, the most popular methods in literature consists of fine-tuning the model on written mathematical content such as academic publications and textbooks, so that the model can learn to emulate the style of mathematical writing. In this project, we explore the effectiveness of using retrieval augmented generation (RAG) to address gaps in the mathematical reasoning of LLMs. We develop LemmaHead, a RAG knowledge base that supplements queries to the model with relevant mathematical context, with particular focus on context from published textbooks. To measure our model's performance in mathematical reasoning, our testing paradigm focuses on the task of automated theorem proving via generating proofs to a given mathematical claim in the Lean formal language.
CodeImprove: Program Adaptation for Deep Code Models
Venue: arXiv cs.SE 2025
Year: 2025

Leveraging deep learning (DL)-based code analysis tools to solve software engineering tasks is becoming increasingly popular. Code models often suffer performance degradation due to various reasons (e.g., code data shifts). Retraining is often required to address these issues, but frequent model updates are costly in labeling and deployment. In this paper, we explore an alternative solution: Adapting the program inputs to the code models. This can be achieved by two steps: 1) input validation that focuses on identifying whether an input is an out-of-scope input program that are beyond a model's handling capability, and 2) input adaptation that adapts out-of-scope inputs to become in-scope inputs. Validating program input is challenging, as current techniques focus on continuous inputs such as image data and fail with discrete inputs like code data, which have unique characteristics and are processed differently by deep learning models. Adapting out-of-scope programs is also challenging due to their vast search spaces. Therefore, in this paper, we propose CodeImprove, which distinguishes out-of-scope from normal inputs and converts such out-of-scope inputs back to in-scope inputs through program transformation. In particular, we propose a validity score metric to identify out-of-scope inputs and leverage genetic algorithms to apply semantic preserving program transformation to convert out-of-scope inputs to in-scope inputs. Our experimental results show CodeImprove can enhance up to 8.78% of accuracy, and 51.28% of relative improvements in three code models on two SE tasks. Additionally, our input validation is promising in detecting out-of-scope inputs (AUC score of 0.924).
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundation model. Specifically, we introduce Ego3D Position Encoding to inject 3D information into the input observations of the visual-language-action model, and propose Adaptive Action Grids to represent spatial robot movement actions with adaptive discretized action grids, facilitating learning generalizable and transferrable spatial action knowledge for cross-robot control. SpatialVLA is first pre-trained on top of a vision-language model with 1.1 Million real-world robot episodes, to learn a generalist manipulation policy across multiple robot environments and tasks. After pre-training, SpatialVLA is directly applied to perform numerous tasks in a zero-shot manner. The superior results in both simulation and real-world robots demonstrate its advantage of inferring complex robot motion trajectories and its strong in-domain multi-task generalization ability. We further show the proposed Adaptive Action Grids offer a new and effective way to fine-tune the pre-trained SpatialVLA model for new simulation and real-world setups, where the pre-learned action grids are re-discretized to capture robot-specific spatial action movements of new setups. The superior results from extensive evaluations demonstrate the exceptional in-distribution generalization and out-of-distribution adaptation capability, highlighting the crucial benefit of the proposed spatial-aware representations for generalist robot policy learning. All the details and codes will be open-sourced.
Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness
Venue: arXiv cs.LG 2025
Year: 2025

We study the Out-of-Distribution (OoD) generalization ability of three SotA trajectory prediction models with comparable In-Distribution (ID) performance but different model designs. We investigate the influence of inductive bias, size of training data and data augmentation strategy by training the models on Argoverse 2 (A2) and testing on Waymo Open Motion (WO) and vice versa. We find that the smallest model with highest inductive bias exhibits the best OoD generalization across different augmentation strategies when trained on the smaller A2 dataset and tested on the large WO dataset. In the converse setting, training all models on the larger WO dataset and testing on the smaller A2 dataset, we find that all models generalize poorly, even though the model with the highest inductive bias still exhibits the best generalization ability. We discuss possible reasons for this surprising finding and draw conclusions about the design and test of trajectory prediction models and benchmarks.
rcpptimer: Rcpp Tic-Toc Timer with OpenMP Support
Venue: arXiv cs.MS 2025
Year: 2025

Efficient code writing is both a critical and challenging task, especially with the growing demand for computationally intensive algorithms in statistical and machine-learning applications. Despite the availability of significant computational power today, the need for optimized algorithm implementations remains crucial. Many R users rely on Rcpp to write performant code in C++, but writing and benchmarking C++ code presents its own difficulties. While R's benchmarking tools are insufficient for measuring the execution times of C++ code segments, C++'s native profiling tools often come with a steep learning curve. The rcpptimer package bridges this gap by offering a simple and efficient solution for timing C++ code within the Rcpp ecosystem. This novel package introduces a user-friendly tic-toc class that supports overlapping and nested timers and OpenMP parallelism, providing nanosecond-level time resolution. Results, including summary statistics, are seamlessly passed back to R without requiring users to write any C++ code. This paper contextualizes the rcpptimer package within the broader ecosystem of R and C++ profiling tools, explains the motivation behind its development, and offers a comprehensive overview of its implementation. Supplementary to this paper, we provide multiple vignettes that thoroughly explain this package's usage.
Web Execution Bundles: Reproducible, Accurate, and Archivable Web Measurements
Venue: arXiv cs.CR 2025
Year: 2025

Recently, reproducibility has become a cornerstone in the security and privacy research community, including artifact evaluations and even a new symposium topic. However, Web measurements lack tools that can be reused across many measurement tasks without modification, while being robust to circumvention, and accurate across the wide range of behaviors in the Web. As a result, most measurement studies use custom tools and varied archival formats, each of unknown correctness and significant limitations, systematically affecting the research's accuracy and reproducibility. To address these limitations, we present WebREC, a Web measurement tool that is, compared against the current state-of-the-art, accurate (i.e., correctly measures and attributes events not possible with existing tools), general (i.e., reusable without modification for a broad range of measurement tasks), and comprehensive (i.e., handling events from all relevant browser behaviors). We also present .web, an archival format for the accurate and reproducible measurement of a wide range of website behaviors. We empirically evaluate WebREC's accuracy by replicating well-known Web measurement studies and showing that WebREC's results more accurately match our baseline. We then assess if WebREC and .web succeed as general-purpose tools, which could be used to accomplish many Web measurement tasks without modification. We find that this is so: 70% of papers discussed in a 2024 web crawling SoK paper could be conducted using WebREC as is, and a larger number (48%) could be leveraged against .web archives without requiring any new crawling.
Provisioning Time-Based Subscription in NDN: A Secure and Efficient Access Control Scheme
Venue: arXiv cs.CR 2025
Year: 2025

This paper proposes a novel encryption-based access control mechanism for Named Data Networking (NDN). The scheme allows data producers to share their content in encrypted form before transmitting it to consumers. The encryption mechanism incorporates time-based subscription access policies directly into the encrypted content, enabling only consumers with valid subscriptions to decrypt it. This makes the scheme well-suited for real-world, subscription-based applications like Netflix. Additionally, the scheme introduces an anonymous and unlinkable signature-based authentication mechanism that empowers edge routers to block bogus content requests at the network's entry point, thereby mitigating Denial of Service (DoS) attacks. A formal security proof demonstrates the scheme's resistance to Chosen Plaintext Attacks (CPA). Performance analysis, using Mini-NDN-based emulation and a Charm library implementation, further confirms the practicality of the scheme. Moreover, it outperforms closely related works in terms of functionality, security, and communication overhead.
Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording
Venue: arXiv cs.AR 2025
Year: 2025

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system's noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers.
RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records
Venue: arXiv cs.CL 2025
Year: 2025

This study introduces RelCAT (Relation Concept Annotation Toolkit), an interactive tool, library, and workflow designed to classify relations between entities extracted from clinical narratives. Building upon the CogStack MedCAT framework, RelCAT addresses the challenge of capturing complete clinical relations dispersed within text. The toolkit implements state-of-the-art machine learning models such as BERT and Llama along with proven evaluation and training methods. We demonstrate a dataset annotation tool (built within MedCATTrainer), model training, and evaluate our methodology on both openly available gold-standard and real-world UK National Health Service (NHS) hospital clinical datasets. We perform extensive experimentation and a comparative analysis of the various publicly available models with varied approaches selected for model fine-tuning. Finally, we achieve macro F1-scores of 0.977 on the gold-standard n2c2, surpassing the previous state-of-the-art performance, and achieve performance of >=0.93 F1 on our NHS gathered datasets.
Integration of LLM Quality Assurance into an NLG System
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we present a system that uses a Large Language Model (LLM) to perform grammar and spelling correction as a component of Quality Assurance (QA) for texts generated by NLG systems, which is important for text production in real-world scenarios. Evaluating the results of the system on work-in-progress sports news texts in three languages, we show that it is able to deliver acceptable corrections.
Options-Aware Dense Retrieval for Multiple-Choice query Answering
Venue: arXiv cs.IR 2025
Year: 2025

Long-context multiple-choice question answering tasks require robust reasoning over extensive text sources. Since most of the pre-trained transformer models are restricted to processing only a few hundred words at a time, successful completion of such tasks often relies on the identification of evidence spans, such as sentences, that provide supporting evidence for selecting the correct answer. Prior research in this domain has predominantly utilized pre-trained dense retrieval models, given the absence of supervision to fine-tune the retrieval process. This paper proposes a novel method called Options Aware Dense Retrieval (OADR) to address these challenges. ORDA uses an innovative approach to fine-tuning retrieval by leveraging query-options embeddings, which aim to mimic the embeddings of the oracle query (i.e., the query paired with the correct answer) for enhanced identification of supporting evidence. Through experiments conducted on the QuALITY benchmark dataset, we demonstrate that our proposed model surpasses existing baselines in terms of performance and accuracy.
Cryptographic Compression
Venue: arXiv cs.CR 2025
Year: 2025

We introduce a protocol called ENCORE which simultaneously compresses and encrypts data in a one-pass process that can be implemented efficiently and possesses a number of desirable features as a streaming encoder/decoder. Motivated by the observation that both lossless compression and encryption consist of performing an invertible transformation whose output is close to a uniform distribution over bit streams, we show that these can be done simultaneously, at least for ``typical'' data with a stable distribution, i.e., approximated reasonably well by the output of a Markov model. The strategy is to transform the data into a dyadic distribution whose Huffman encoding is close to uniform, and then store the transformations made to said data in a compressed secondary stream interwoven into the first with a user-defined encryption protocol. The result is an encoding which we show exhibits a modified version of Yao's ``next-bit test'' while requiring many fewer bits of entropy than standard encryption. Numerous open questions remain, particularly regarding results that we suspect can be strengthened considerably.
HERITRACE: A User-Friendly Semantic Data Editor with Change Tracking and Provenance Management for Cultural Heritage Institutions
Venue: arXiv cs.DL 2025
Year: 2025

HERITRACE is a data editor designed for galleries, libraries, archives and museums, aimed at simplifying data curation while enabling non-technical domain experts to manage data intuitively without losing its semantic integrity. While the semantic nature of RDF can pose a barrier to data curation due to its complexity, HERITRACE conceals this intricacy while preserving the advantages of semantic representation. The system natively supports provenance management and change tracking, ensuring transparency and accountability throughout the curation process. Although HERITRACE functions effectively out of the box, it offers a straightforward customization interface for technical staff, enabling adaptation to the specific data model required by a given collection. Current applications include the ParaText project, and its adoption is already planned for OpenCitations. Future developments will focus on integrating the RDF Mapping Language (RML) to enhance compatibility with non-RDF data formats, further expanding its applicability in digital heritage management.
New Frontiers in Fighting Misinformation
Venue: arXiv cs.SI 2025
Year: 2025

Despite extensive research and development of tools and technologies for misinformation tracking and detection, we often find ourselves largely on the losing side of the battle against misinformation. In an era where misinformation poses a substantial threat to public discourse, trust in information sources, and societal and political stability, it is imperative that we regularly revisit and reorient our work strategies. While we have made significant strides in understanding how and why misinformation spreads, we must now broaden our focus and explore how technology can help realise new approaches to address this complex challenge more efficiently.
An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance
Venue: arXiv cs.RO 2025
Year: 2025

Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.