OpenRaiser/Intern-Atlas huggingface dataset:
Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection
Venue: arXiv cs.LG 2025
Year: 2025

Selecting high-quality pre-training data for large language models (LLMs) is crucial for enhancing their overall performance under limited computation budget, improving both training and sample efficiency. Recent advancements in file selection primarily rely on using an existing or trained proxy model to assess the similarity of samples to a target domain, such as high quality sources BookCorpus and Wikipedia. However, upon revisiting these methods, the domain-similarity selection criteria demonstrates a diversity dilemma, i.e.dimensional collapse in the feature space, improving performance on the domain-related tasks but causing severe degradation on generic performance. To prevent collapse and enhance diversity, we propose a DiverSified File selection algorithm (DiSF), which selects the most decorrelated text files in the feature space. We approach this with a classical greedy algorithm to achieve more uniform eigenvalues in the feature covariance matrix of the selected texts, analyzing its approximation to the optimal solution under a formulation of $γ$-weakly submodular optimization problem. Empirically, we establish a benchmark and conduct extensive experiments on the TinyLlama architecture with models from 120M to 1.1B parameters. Evaluating across nine tasks from the Harness framework, DiSF demonstrates a significant improvement on overall performance. Specifically, DiSF saves 98.5% of 590M training files in SlimPajama, outperforming the full-data pre-training within a 50B training budget, and achieving about 1.5x training efficiency and 5x data efficiency.
Image deidentification in the XNAT ecosystem: use cases and solutions
Venue: arXiv cs.CV 2025
Year: 2025

XNAT is a server-based data management platform widely used in academia for curating large databases of DICOM images for research projects. We describe in detail a deidentification workflow for DICOM data using facilities in XNAT, together with independent tools in the XNAT "ecosystem". We list different contexts in which deidentification might be needed, based on our prior experience. The starting point for participation in the Medical Image De-Identification Benchmark (MIDI-B) challenge was a set of pre-existing local methodologies, which were adapted during the validation phase of the challenge. Our result in the test phase was 97.91\%, considerably lower than our peers, due largely to an arcane technical incompatibility of our methodology with the challenge's Synapse platform, which prevented us receiving feedback during the validation phase. Post-submission, additional discrepancy reports from the organisers and via the MIDI-B Continuous Benchmarking facility, enabled us to improve this score significantly to 99.61\%. An entirely rule-based approach was shown to be capable of removing all name-related information in the test corpus, but exhibited failures in dealing fully with address data. Initial experiments using published machine-learning models to remove addresses were partially successful but showed the models to be "over-aggressive" on other types of free-text data, leading to a slight overall degradation in performance to 99.54\%. Future development will therefore focus on improving address-recognition capabilities, but also on better removal of identifiable data burned into the image pixels. Several technical aspects relating to the "answer key" are still under discussion with the challenge organisers, but we estimate that our percentage of genuine deidentification failures on the MIDI-B test corpus currently stands at 0.19\%. (Abridged from original for arXiv submission)
Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.
On the Optimal Source Key Size of Secure Gradient Coding
Venue: arXiv cs.IT 2025
Year: 2025

With gradient coding, a user node can efficiently aggregate gradients from server nodes processing local datasets, achieving low communication costs and maintaining resilience against straggling servers. This paper considers a secure gradient coding problem, where a user aims to compute the sum of the gradients from $K$ datasets with the assistance of $N$ distributed servers. The user should recover the sum of gradients by receiving transmissions from any $N_r$ servers, and each dataset is assigned to $N - N_r + m$ servers. The security constraint guarantees that even if the user receives transmissions from all servers, it cannot obtain any additional information about the datasets beyond the sum of gradients. It has been shown in the literature that this security constraint does not increase the optimal communication cost of the gradient coding problem, provided enough source keys are shared among the servers. However, the minimum required source key size that ensures security while maintaining this optimal communication cost has only been studied for the special case $m = 1$. In this paper, we focus on the more general case $m \geq 1$ and aim to determine the minimum required source key size for this purpose. We propose a new information-theoretic converse bound on the source key size, as well as a new achievable scheme with carefully designed data assignments. Our scheme outperforms the existing optimal scheme based on the widely used cyclic data assignment and coincides with the converse bound under certain system parameters.
Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a robust, occlusion-aware driver monitoring system (DMS) utilizing the Driver Monitoring Dataset (DMD). The system performs driver identification, gaze estimation by regions, and face occlusion detection under varying lighting conditions, including challenging low-light scenarios. Aligned with EuroNCAP recommendations, the inclusion of occlusion detection enhances situational awareness and system trustworthiness by indicating when the system's performance may be degraded. The system employs separate algorithms trained on RGB and infrared (IR) images to ensure reliable functioning. We detail the development and integration of these algorithms into a cohesive pipeline, addressing the challenges of working with different sensors and real-car implementation. Evaluation on the DMD and in real-world scenarios demonstrates the effectiveness of the proposed system, highlighting the superior performance of RGB-based models and the pioneering contribution of robust occlusion detection in DMS.
Overcoming Quadratic Hardware Scaling for a Fully Connected Digital Oscillatory Neural Network
Venue: arXiv cs.AR 2025
Year: 2025

Computing with coupled oscillators or oscillatory neural networks (ONNs) has recently attracted a lot of interest due to their potential for massive parallelism and energy-efficient computing. However, to date, ONNs have primarily been explored either analytically or through analog circuit implementations. This paper shifts the focus to the digital implementation of ONNs, examining various design architectures. We first report on an existing digital ONN design based on a recurrent architecture. The major challenge for scaling such recurrent architectures is the quadratic increase in coupling hardware with the network size. To overcome this challenge, we introduce a novel hybrid architecture that balances serialization and parallelism in the coupling elements that shows near-linear hardware scaling, on the order of about 1.2 with the network size. Furthermore, we evaluate the benefits and costs of these different digital ONN architectures in terms time to solution and resource usage on FPGA emulation. The proposed hybrid architecture allows for a 10.5$\times$ increase in the number of oscillators while using 5-bits to represent the coupling weights and 4-bits to represent the oscillator phase on a Zynq-7020 FPGA board. The near-linear scaling is a major step towards implementing large scale ONN architectures. To the best of our knowledge, this work presents the largest fully connected digital ONN architecture implemented thus far with a total of 506 fully connected oscillators.
Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Future self-adaptive robots are expected to operate in highly dynamic environments while effectively managing uncertainties. However, identifying the sources and impacts of uncertainties in such robotic systems and defining appropriate mitigation strategies is challenging due to the inherent complexity of self-adaptive robots and the lack of comprehensive knowledge about the various factors influencing uncertainty. Hence, practitioners often rely on intuition and past experiences from similar systems to address uncertainties. In this article, we evaluate the potential of large language models (LLMs) in enabling a systematic and automated approach to identify uncertainties in self-adaptive robotics throughout the software engineering lifecycle. For this evaluation, we analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotics case studies, gathering the practitioners' perspectives on the LLM-generated responses related to uncertainties. Results showed that practitioners agreed with 63-88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.
LakeVilla: A Modular and Non-Invasive Toolbox for Lakehouse Transactions
Venue: arXiv cs.DB 2025
Year: 2025

Data lakehouses (LHs) are at the core of current cloud analytics stacks by providing elastic, relational compute on data in cloud data lakes across vendors. For relational semantics, they rely on open table formats (OTFs). Unfortunately, they have many missing features inherent to their metadata designs, like no support for multi-table transactions and recovery in case of an abort in concurrent, multi-query workloads. This, in turn, can lead to non-repeatable reads, stale data, and high costs in production cloud systems. In this work, we introduce LakeVilla, a modular toolbox that introduces recovery, complex transactions, and transaction isolation to state-of-the-art OTFs like Apache Iceberg and Delta Lake tables. We investigate its transactional guarantees and show it has minimal impact on performance (2% YCSB writes, 2.5% TPC-DS reads) and provides concurrency control for multiple readers and writers for arbitrary long transactions in OTFs in a non-invasive way.
ECOSoundSet: a finely annotated dataset for the automated acoustic identification of Orthoptera and Cicadidae in North, Central and temperate Western Europe
Venue: arXiv cs.SD 2025
Year: 2025

Currently available tools for the automated acoustic recognition of European insects in natural soundscapes are limited in scope. Large and ecologically heterogeneous acoustic datasets are currently needed for these algorithms to cross-contextually recognize the subtle and complex acoustic signatures produced by each species, thus making the availability of such datasets a key requisite for their development. Here we present ECOSoundSet (European Cicadidae and Orthoptera Sound dataSet), a dataset containing 10,653 recordings of 200 orthopteran and 24 cicada species (217 and 26 respective taxa when including subspecies) present in North, Central, and temperate Western Europe (Andorra, Belgium, Denmark, mainland France and Corsica, Germany, Ireland, Luxembourg, Monaco, Netherlands, United Kingdom, Switzerland), collected partly through targeted fieldwork in South France and Catalonia and partly through contributions from various European entomologists. The dataset is composed of a combination of coarsely labeled recordings, for which we can only infer the presence, at some point, of their target species (weak labeling), and finely annotated recordings, for which we know the specific time and frequency range of each insect sound present in the recording (strong labeling). We also provide a train/validation/test split of the strongly labeled recordings, with respective approximate proportions of 0.8, 0.1 and 0.1, in order to facilitate their incorporation in the training and evaluation of deep learning algorithms. This dataset could serve as a meaningful complement to recordings already available online for the training of deep learning algorithms for the acoustic classification of orthopterans and cicadas in North, Central, and temperate Western Europe.
Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces
Venue: arXiv cs.HC 2025
Year: 2025

Adaptive User Interfaces (AUI) play a crucial role in modern software applications by dynamically adjusting interface elements to accommodate users' diverse and evolving needs. However, existing adaptation strategies often lack real-time responsiveness. Reinforcement Learning (RL) has emerged as a promising approach for addressing complex, sequential adaptation challenges, enabling adaptive systems to learn optimal policies based on previous adaptation experiences. Although RL has been applied to AUIs,integrating RL agents effectively within user interactions remains a challenge. In this paper, we enhance a RL-based Adaptive User Interface adaption framework by incorporating personalized human feedback directly into the leaning process. Unlike prior approaches that rely on a single pre-trained RL model, our approach trains a unique RL agent for each user, allowing individuals to actively shape their personal RL agent's policy, potentially leading to more personalized and responsive UI adaptations. To evaluate this approach, we conducted an empirical study to assess the impact of integrating human feedback into the RL-based Adaptive User Interface adaption framework and its effect on User Experience (UX). The study involved 33 participants interacting with AUIs incorporating human feedback and non-adaptive user interfaces in two domains: an e-learning platform and a trip-planning application. The results suggest that incorporating human feedback into RL-driven adaptations significantly enhances UX, offering promising directions for advancing adaptive capabilities and user-centered design in AUIs.
Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
Venue: arXiv cs.LG 2025
Year: 2025

Identifying molecular properties, including side effects, is a critical yet time-consuming step in drug development. Failing to detect these side effects before regulatory submission can result in significant financial losses and production delays, and overlooking them during the regulatory review can lead to catastrophic consequences. This challenge presents an opportunity for innovative machine learning approaches, particularly hybrid quantum-classical models like the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network. The QK-LSTM integrates quantum kernel functions into the classical LSTM framework, enabling the capture of complex, non-linear patterns in sequential data. By mapping input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the need for large parameter sets, allowing for model compression without sacrificing accuracy in sequence-based tasks. Recent advancements have been made in the classical domain using augmented variations of the Simplified Molecular Line-Entry System (SMILES). However, to the best of our knowledge, no research has explored the impact of augmented SMILES in the quantum domain, nor the role of augmented Self-Referencing Embedded Strings (SELFIES) in either classical or hybrid quantum-classical settings. This study presents the first analysis of these approaches, providing novel insights into their potential for enhancing molecular property prediction and side effect identification. Results reveal that augmenting SELFIES yields in statistically significant improvements from SMILES by a 5.97% improvement for the classical domain and a 5.91% improvement for the hybrid quantum-classical domain.
SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces SoccerDiffusion, a transformer-based diffusion model designed to learn end-to-end control policies for humanoid robot soccer directly from real-world gameplay recordings. Using data collected from RoboCup competitions, the model predicts joint command trajectories from multi-modal sensor inputs, including vision, proprioception, and game state. We employ a distillation technique to enable real-time inference on embedded platforms that reduces the multi-step diffusion process to a single step. Our results demonstrate the model's ability to replicate complex motion behaviors such as walking, kicking, and fall recovery both in simulation and on physical robots. Although high-level tactical behavior remains limited, this work provides a robust foundation for subsequent reinforcement learning or preference optimization methods. We release the dataset, pretrained models, and code under: https://bit-bots.github.io/SoccerDiffusion
Bitcoin, a DAO?
Venue: arXiv cs.CY 2025
Year: 2025

This paper investigates whether Bitcoin can be regarded as a decentralized autonomous organization (DAO), what insights it may offer for the broader DAO ecosystem, and how Bitcoin governance can be improved. First, a quantitative literature analysis reveals that Bitcoin is increasingly overlooked in DAO research, even though early works often classified it as a DAO. Next, the paper applies a DAO viability framework - centering on collective intelligence, digital democracy, and adaptation - to examine Bitcoin's organizational and governance mechanisms. Findings suggest that Bitcoin instantitates key DAO principles by enabling open participation, and employing decentralized decision-making through Bitcoin Improvement Proposals (BIPs), miner signaling, and user-activated soft forks. However, this governance carries potential risks, including reduced clarity on who truly 'votes' due to the concentration of economic power among large stakeholders. The paper concludes by highlighting opportunities to refine Bitcoin's deliberation process and reflecting on broader implications for DAO design, such as the absence of a legal entity. In doing so, it underscores Bitcoin's continued relevance as an archetype for decentralized governance, offering important findings for future DAO implementations.
Online General Knapsack with Reservation Costs
Venue: arXiv cs.DS 2025
Year: 2025

In the online general knapsack problem, an algorithm is presented with an item $x=(s,v)$ of size $s$ and value $v$ and must irrevocably choose to pack such an item into the knapsack or reject it before the next item appears. The goal is to maximize the total value of the packed items without overflowing the knapsack's capacity. As this classical setting is way too harsh for many real-life applications, we will analyze the online general knapsack problem under the reservation model. Here, instead of accepting or rejecting an item immediately, an algorithm can delay the decision of whether to pack the item by paying a fraction $0\le α$ of the size or the value of the item. This models many practical applications, where, for example, decisions can be delayed for some costs e.g. cancellation fees. We present results for both variants: First, for costs depending on the size of the items and then for costs depending on the value of the items. If the reservation costs depend on the size of the items, we find a matching upper and lower bound of $2$ for every $α$. On the other hand, if the reservation costs depend on the value of the items, we find that no algorithm is competitive for reservation costs larger than $1/2$ of the item value, and we find upper and lower bounds for the rest of the reservation range $0\leα< 1/2$.
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
Venue: arXiv cs.LG 2025
Year: 2025

The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, one frequently faces challenges arising from structural differences in the datasets, domain shift, and the lack of labels. In this work, we introduce Tabular Data Adapters (TDA), a novel method for generating soft labels for unlabeled tabular data in outlier detection tasks. By identifying statistically similar public datasets and transforming private data (based on a shared autoencoder) into a format compatible with state-of-the-art public models, our approach enables the generation of weak labels. It thereby can help to mitigate the cold start problem of labeling by basing on existing outlier detection models for public datasets. In experiments on 50 tabular datasets across different domains, we demonstrate that our method is able to provide more accurate annotations than baseline approaches while reducing computational time. Our approach offers a scalable, efficient, and cost-effective solution, to bridge the gap between public research models and real-world industrial applications.
Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data
Venue: arXiv cs.RO 2025
Year: 2025

This work presents a methodology to estimate tire parameters and their uncertainty using a Bayesian optimization approach. The literature mainly considers the estimation of tire parameters but lacks an evaluation of the parameter identification quality and the required slip ratios for an adequate model fit. Therefore, we examine the use of Stochastical Variational Inference as a methodology to estimate both - the parameters and their uncertainties. We evaluate the method compared to a state-of-the-art Nelder-Mead algorithm for theoretical and real-world application. The theoretical study considers parameter fitting at different slip ratios to evaluate the required excitation for an adequate fitting of each parameter. The results are compared to a sensitivity analysis for a Pacejka Magic Formula tire model. We show the application of the algorithm on real-world data acquired during the Abu Dhabi Autonomous Racing League and highlight the uncertainties in identifying the curvature and shape parameters due to insufficient excitation. The gathered insights can help assess the acquired data's limitations and instead utilize standardized parameters until higher slip ratios are captured. We show that our proposed method can be used to assess the mean values and the uncertainties of tire model parameters in real-world conditions and derive actions for the tire modeling based on our simulative study.