OpenRaiser/Intern-Atlas huggingface dataset:
Reallocating Wasted Votes in Proportional Parliamentary Elections with Thresholds
Venue: arXiv cs.GT 2025
Year: 2025

In many proportional parliamentary elections, electoral thresholds (typically 3-5%) are used to promote stability and governability by preventing the election of parties with very small representation. However, these thresholds often result in a significant number of "wasted votes" cast for parties that fail to meet the threshold, which reduces representativeness. One proposal is to allow voters to specify replacement votes, by either indicating a second choice party or by ranking a subset of the parties, but there are several ways of deciding on the scores of the parties (and thus the composition of the parliament) given those votes. We introduce a formal model of party voting with thresholds, and compare a variety of party selection rules axiomatically, and experimentally using a dataset we collected during the 2024 European election in France. We identify three particularly attractive rules, called Direct Winners Only (DO), Single Transferable Vote (STV) and Greedy Plurality (GP).
CoRLD: Contrastive Representation Learning Of Deformable Shapes In Images
Venue: arXiv cs.CV 2025
Year: 2025

Deformable shape representations, parameterized by deformations relative to a given template, have proven effective for improved image analysis tasks. However, their broader applicability is hindered by two major challenges. First, existing methods mainly rely on a known template during testing, which is impractical and limits flexibility. Second, they often struggle to capture fine-grained, voxel-level distinctions between similar shapes (e.g., anatomical variations among healthy individuals, those with mild cognitive impairment, and diseased states). To address these limitations, we propose a novel framework - Contrastive Representation Learning of Deformable shapes (CoRLD) in learned deformation spaces and demonstrate its effectiveness in the context of image classification. Our CoRLD leverages a class-aware contrastive supervised learning objective in latent deformation spaces, promoting proximity among representations of similar classes while ensuring separation of dissimilar groups. In contrast to previous deep learning networks that require a reference image as input to predict deformation changes, our approach eliminates this dependency. Instead, template images are utilized solely as ground truth in the loss function during the training process, making our model more flexible and generalizable to a wide range of medical applications. We validate CoRLD on diverse datasets, including real brain magnetic resonance imaging (MRIs) and adrenal shapes derived from computed tomography (CT) scans. Experimental results show that our model effectively extracts deformable shape features, which can be easily integrated with existing classifiers to substantially boost the classification accuracy. Our code is available at GitHub.
Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Light Detection and Ranging (LiDAR) is an essential sensor technology for autonomous driving as it can capture high-resolution 3D data. As 3D object detection systems (OD) can interpret such point cloud data, they play a key role in the driving decisions of autonomous vehicles. Consequently, such 3D OD must be robust against all types of perturbations and must therefore be extensively tested. One approach is the use of adversarial examples, which are small, sometimes sophisticated perturbations in the input data that change, i.e., falsify, the prediction of the OD. These perturbations are carefully designed based on the weaknesses of the OD. The robustness of the OD cannot be quantified with adversarial examples in general, because if the OD is vulnerable to a given attack, it is unclear whether this is due to the robustness of the OD or whether the attack algorithm produces particularly strong adversarial examples. The contribution of this work is Hi-ALPS -- Hierarchical Adversarial-example-based LiDAR Perturbation Level System, where higher robustness of the OD is required to withstand the perturbations as the perturbation levels increase. In doing so, the Hi-ALPS levels successively implement a heuristic followed by established adversarial example approaches. In a series of comprehensive experiments using Hi-ALPS, we quantify the robustness of six state-of-the-art 3D OD under different types of perturbations. The results of the experiments show that none of the OD is robust against all Hi-ALPS levels; an important factor for the ranking is that human observers can still correctly recognize the perturbed objects, as the respective perturbations are small. To increase the robustness of the OD, we discuss the applicability of state-of-the-art countermeasures. In addition, we derive further suggestions for countermeasures based on our experimental results.
A Study of LLMs' Preferences for Libraries and Programming Languages
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are increasingly used to generate code, influencing users' choices of libraries and programming languages in critical real-world projects. However, little is known about their systematic biases or preferences toward certain libraries and programming languages, which can significantly impact software development practices. To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight diverse LLMs. Our results reveal that LLMs exhibit a strong tendency to overuse widely adopted libraries such as NumPy; in up to 48% of cases, this usage is unnecessary and deviates from the ground-truth solutions. LLMs also exhibit a significant preference toward Python as their default language. For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58% of cases, and Rust is not used a single time. These results indicate that LLMs may prioritise familiarity and popularity over suitability and task-specific optimality. This will introduce security vulnerabilities and technical debt, and limit exposure to newly developed, better-suited tools and languages. Understanding and addressing these biases is essential for the responsible integration of LLMs into software development workflows.
The EnviroMapper Toolkit: an Input Physicalisation that Captures the Situated Experience of Environmental Comfort in Offices
Venue: arXiv cs.HC 2025
Year: 2025

The environmental comfort in offices is traditionally captured by surveying an entire workforce simultaneously, which yet fails to capture the situatedness of the different personal experiences. To address this limitation, we developed the EnviroMapper Toolkit, a data physicalisation toolkit that allows individual office workers to record their personal experiences of environmental comfort by mapping the actual moments and locations these occurred. By analysing two in-the-wild studies in existing open-plan office environments (N=14), we demonstrate how this toolkit acts like a situated input visualisation that can be interpreted by domain experts who were not present during its construction. This study therefore offers four key contributions: (1) the iterative design process of the physicalisation toolkit; (2) its preliminary deployment in two real-world office contexts; (3) the decoding of the resulting artefacts by domain experts; and (4) design considerations to support future input physicalisation and visualisation constructions that capture and synthesise data from multiple individuals.
Semi-Automated Design of Data-Intensive Architectures
Venue: arXiv cs.SE 2025
Year: 2025

Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate at which it changes, and the heterogeneity of formats. To address this challenge, software architects resort to build complex data-intensive architectures that integrate highly heterogeneous software systems, each offering vertically specialized functionalities. Designing a suitable architecture for the application at hand is crucial to enable high quality of service and efficient exploitation of resources. However, the design process entails a series of decisions that demand technical expertise and in-depth knowledge of individual systems and their synergies. To assist software architects in this task, this paper introduces a development methodology for data-intensive architectures, which guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application. To do so, the methodology grounds on (1) a language to precisely define an application scenario in terms of characteristics of data and requirements of stakeholders; (2) an architecture description language for data-intensive architectures; (3) a classification of systems based on the functionalities they offer and their performance trade-offs. We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners, and we validate our methodology by applying it to real-world case studies documented in literature.
Dereflection Any Image with Diffusion Priors and Diversified Data
Venue: arXiv cs.CV 2025
Year: 2025

Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections. Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios. In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal. First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes, enabling variation of reflection angles and intensities, and setting a new benchmark in scale, quality, and diversity. Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference. To ensure stable learning, we design a three-stage progressive training strategy, including reflection-invariant finetuning to encourage consistent outputs across varying reflection patterns that characterize our dataset. Extensive experiments show that our method achieves SOTA performance on both common benchmarks and challenging in-the-wild images, showing superior generalization across diverse real-world scenes.
NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

In deep learning, processing multidimensional inputs (e.g., images, medical scans, and time series) is an important task that often requires flattening the inputs. We introduce $\mathit{NdLinear}$, a drop-in replacement for linear layers that operates directly on tensors, requiring no flattening. By applying transformations separately along each dimension, NdLinear preserves native data structure while achieving dramatic parameter reductions, often by orders of magnitude, with minimal memory overhead. We prove NdLinear maintains expressivity through structured Tucker decomposition while preserving VC-dimension scaling. Extensive experiments demonstrate NdLinear's capacity to achieve significant parameter reductions with substantial wall-clock efficiency gains and minimal memory overhead. For instance, our $\mathit{NdLinear-LoRA}$ matches or exceeds standard LoRA on language reasoning tasks using up to $9\times$ fewer parameters. Experiments across CNNs, RNNs, Transformers, and MLPs on vision, language, time-series, and tabular tasks consistently demonstrate NdLinear's efficiency gains. While excelling at axis-separable tasks, NdLinear has limitations with entangled spatial interactions. By processing data in its original N-dimensional form, NdLinear provides a theoretically grounded, practical component for building more efficient neural architectures.
HCAST: Human-Calibrated Autonomy Software Tasks
Venue: arXiv cs.AI 2025
Year: 2025

To understand and predict the societal impacts of highly autonomous AI systems, we need benchmarks with grounding, i.e., metrics that directly connect AI performance to real-world effects we care about. We present HCAST (Human-Calibrated Autonomy Software Tasks), a benchmark of 189 machine learning engineering, cybersecurity, software engineering, and general reasoning tasks. We collect 563 human baselines (totaling over 1500 hours) from people skilled in these domains, working under identical conditions as AI agents, which lets us estimate that HCAST tasks take humans between one minute and 8+ hours. Measuring the time tasks take for humans provides an intuitive metric for evaluating AI capabilities, helping answer the question "can an agent be trusted to complete a task that would take a human X hours?" We evaluate the success rates of AI agents built on frontier foundation models, and we find that current agents succeed 70-80% of the time on tasks that take humans less than one hour, and less than 20% of the time on tasks that take humans more than 4 hours.
Position: Interactive Generative Video as Next-Generation Game Engine
Venue: arXiv cs.CV 2025
Year: 2025

Modern game development faces significant challenges in creativity and cost due to predetermined content in traditional game engines. Recent breakthroughs in video generation models, capable of synthesizing realistic and interactive virtual environments, present an opportunity to revolutionize game creation. In this position paper, we propose Interactive Generative Video (IGV) as the foundation for Generative Game Engines (GGE), enabling unlimited novel content generation in next-generation gaming. GGE leverages IGV's unique strengths in unlimited high-quality content synthesis, physics-aware world modeling, user-controlled interactivity, long-term memory capabilities, and causal reasoning. We present a comprehensive framework detailing GGE's core modules and a hierarchical maturity roadmap (L0-L4) to guide its evolution. Our work charts a new course for game development in the AI era, envisioning a future where AI-powered generative systems fundamentally reshape how games are created and experienced.
Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique
Venue: arXiv cs.CL 2025
Year: 2025

Enhancing the reasoning capabilities of large language models (LLMs), particularly for complex tasks requiring multi-step logical deductions, remains a significant challenge. Traditional inference time scaling methods utilize scalar reward signals from process reward models to evaluate candidate reasoning steps, but these scalar rewards lack the nuanced qualitative information essential for understanding and justifying each step. In this paper, we propose a novel inference-time scaling approach -- stepwise natural language self-critique (PANEL), which employs self-generated natural language critiques as feedback to guide the step-level search process. By generating rich, human-readable critiques for each candidate reasoning step, PANEL retains essential qualitative information, facilitating better-informed decision-making during inference. This approach bypasses the need for task-specific verifiers and the associated training overhead, making it broadly applicable across diverse tasks. Experimental results on challenging reasoning benchmarks, including AIME and GPQA, demonstrate that PANEL significantly enhances reasoning performance, outperforming traditional scalar reward-based methods. Our code is available at https://github.com/puddingyeah/PANEL to support and encourage future research in this promising field.
Intanify AI Platform: Embedded AI for Automated IP Audit and Due Diligence
Venue: arXiv cs.CY 2025
Year: 2025

In this paper we introduce a Platform created in order to support SMEs' endeavor to extract value from their intangible assets effectively. To implement the Platform, we developed five knowledge bases using a knowledge-based ex-pert system shell that contain knowledge from intangible as-set consultants, patent attorneys and due diligence lawyers. In order to operationalize the knowledge bases, we developed a "Rosetta Stone", an interpreter unit for the knowledge bases outside the shell and embedded in the plat-form. Building on the initial knowledge bases we have created a system of red flags, risk scoring, and valuation with the involvement of the same experts; these additional systems work upon the initial knowledge bases and therefore they can be regarded as meta-knowledge-representations that take the form of second-order knowledge graphs. All this clever technology is dressed up in an easy-to-handle graphical user interface that we will showcase at the conference. The initial platform was finished mid-2024; therefore, it qualifies as an "emerging application of AI" and "deployable AI", while development continues. The two firms that provided experts for developing the knowledge bases obtained a white-label version of the product (i.e. it runs under their own brand "powered by Intanify"), and there are two completed cases.
Leveraging OpenFlamingo for Multimodal Embedding Analysis of C2C Car Parts Data
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we aim to investigate the capabilities of multimodal machine learning models, particularly the OpenFlamingo model, in processing a large-scale dataset of consumer-to-consumer (C2C) online posts related to car parts. We have collected data from two platforms, OfferUp and Craigslist, resulting in a dataset of over 1.2 million posts with their corresponding images. The OpenFlamingo model was used to extract embeddings for the text and image of each post. We used $k$-means clustering on the joint embeddings to identify underlying patterns and commonalities among the posts. We have found that most clusters contain a pattern, but some clusters showed no internal patterns. The results provide insight into the fact that OpenFlamingo can be used for finding patterns in large datasets but needs some modification in the architecture according to the dataset.
Reward Redistribution via Gaussian Process Likelihood Estimation
Venue: arXiv cs.LG 2025
Year: 2025

In many practical reinforcement learning tasks, feedback is only provided at the end of a long horizon, leading to sparse and delayed rewards. Existing reward redistribution methods typically assume that per-step rewards are independent, thus overlooking interdependencies among state-action pairs. In this paper, we propose a Gaussian process based Likelihood Reward Redistribution (GP-LRR) framework that addresses this issue by modeling the reward function as a sample from a Gaussian process, which explicitly captures dependencies between state-action pairs through the kernel function. By maximizing the likelihood of the observed episodic return via a leave-one-out strategy that leverages the entire trajectory, our framework inherently introduces uncertainty regularization. Moreover, we show that conventional mean-squared-error (MSE) based reward redistribution arises as a special case of our GP-LRR framework when using a degenerate kernel without observation noise. When integrated with an off-policy algorithm such as Soft Actor-Critic, GP-LRR yields dense and informative reward signals, resulting in superior sample efficiency and policy performance on several MuJoCo benchmarks.
Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory
Venue: arXiv cs.CE 2025
Year: 2025

Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models - melding data-driven and mechanistic models - to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform's capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development.
Opportunities and Challenges of Frontier Data Governance With Synthetic Data
Venue: arXiv cs.CY 2025
Year: 2025

Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift. We thus craft 3 technical mechanisms to address these specific challenges, finding applications for synthetic data towards adversarial training, bias mitigation and value reinforcement. These could not only counteract the risks of synthetic data, but serve as critical levers for governance of the frontier in the future.
Enhancing Subsequent Video Retrieval via Vision-Language Models (VLMs)
Venue: arXiv cs.CV 2025
Year: 2025

The rapid growth of video content demands efficient and precise retrieval systems. While vision-language models (VLMs) excel in representation learning, they often struggle with adaptive, time-sensitive video retrieval. This paper introduces a novel framework that combines vector similarity search with graph-based data structures. By leveraging VLM embeddings for initial retrieval and modeling contextual relationships among video segments, our approach enables adaptive query refinement and improves retrieval accuracy. Experiments demonstrate its precision, scalability, and robustness, offering an effective solution for interactive video retrieval in dynamic environments.