OpenRaiser/Intern-Atlas huggingface dataset:
Every Software as an Agent: Blueprint and Case Study
Venue: arXiv cs.SE 2025
Year: 2025

The rise of (multimodal) large language models (LLMs) has shed light on software agent -- where software can understand and follow user instructions in natural language. However, existing approaches such as API-based and GUI-based agents are far from satisfactory at accuracy and efficiency aspects. Instead, we advocate to endow LLMs with access to the software internals (source code and runtime context) and the permission to dynamically inject generated code into software for execution. In such a whitebox setting, one may better leverage the software context and the coding ability of LLMs. We then present an overall design architecture and case studies on two popular web-based desktop applications. We also give in-depth discussion of the challenges and future directions. We deem that such a new paradigm has the potential to fundamentally overturn the existing software agent design, and finally creating a digital world in which software can comprehend, operate, collaborate, and even think to meet complex user needs.
Autoregressive Generation of Static and Growing Trees
Venue: arXiv cs.CV 2025
Year: 2025

We propose a transformer architecture and training strategy for tree generation. The architecture processes data at multiple resolutions and has an hourglass shape, with middle layers processing fewer tokens than outer layers. Similar to convolutional networks, we introduce longer range skip connections to completent this multi-resolution approach. The key advantage of this architecture is the faster processing speed and lower memory consumption. We are therefore able to process more complex trees than would be possible with a vanilla transformer architecture. Furthermore, we extend this approach to perform image-to-tree and point-cloud-to-tree conditional generation and to simulate the tree growth processes, generating 4D trees. Empirical results validate our approach in terms of speed, memory consumption, and generation quality.
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) has garnered significant attention as a prominent privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL) eschews traditional FL's centralized server architecture, enhancing the system's robustness and scalability. However, these advantages of DFL also create new vulnerabilities for malicious participants to execute adversarial attacks, especially model poisoning attacks. In model poisoning attacks, malicious participants aim to diminish the performance of benign models by creating and disseminating the compromised model. Existing research on model poisoning attacks has predominantly concentrated on undermining global models within the Centralized FL (CFL) paradigm, while there needs to be more research in DFL. To fill the research gap, this paper proposes an innovative model poisoning attack called DMPA. This attack calculates the differential characteristics of multiple malicious client models and obtains the most effective poisoning strategy, thereby orchestrating a collusive attack by multiple participants. The effectiveness of this attack is validated across multiple datasets, with results indicating that the DMPA approach consistently surpasses existing state-of-the-art FL model poisoning attack strategies.
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Multi-Agent Reinforcement Learning (MARL) has recently emerged as a significant area of research. However, MARL evaluation often lacks systematic diversity, hindering a comprehensive understanding of algorithms' capabilities. In particular, cooperative MARL algorithms are predominantly evaluated on benchmarks such as SMAC and GRF, which primarily feature team game scenarios without assessing adequately various aspects of agents' capabilities required in fully cooperative real-world tasks such as multi-robot cooperation and warehouse, resource management, search and rescue, and human-AI cooperation. Moreover, MARL algorithms are mainly evaluated on low dimensional state spaces, and thus their performance on high-dimensional (e.g., image) observations is not well-studied. To fill this gap, this paper highlights the crucial need for expanding systematic evaluation across a wider array of existing benchmarks. To this end, we conduct extensive evaluation and comparisons of well-known MARL algorithms on complex fully cooperative benchmarks, including tasks with images as agents' observations. Interestingly, our analysis shows that many algorithms, hailed as state-of-the-art on SMAC and GRF, may underperform standard MARL baselines on fully cooperative benchmarks. Finally, towards more systematic and better evaluation of cooperative MARL algorithms, we have open-sourced PyMARLzoo+, an extension of the widely used (E)PyMARL libraries, which addresses an open challenge from [TBG++21], facilitating seamless integration and support with all benchmarks of PettingZoo, as well as Overcooked, PressurePlate, Capture Target and Box Pushing.
S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing (NLP) scenarios, but they still face challenges when handling complex arithmetic and logical reasoning tasks. While Chain-Of-Thought (CoT) reasoning, self-consistency (SC) and self-correction strategies have attempted to guide models in sequential, multi-step reasoning, Multi-agent Debate (MAD) has emerged as a viable approach for enhancing the reasoning capabilities of LLMs. By increasing both the number of agents and the frequency of debates, the performance of LLMs improves significantly. However, this strategy results in a significant increase in token costs, presenting a barrier to scalability. To address this challenge, we introduce a novel sparsification strategy designed to reduce token costs within MAD. This approach minimizes ineffective exchanges of information and unproductive discussions among agents, thereby enhancing the overall efficiency of the debate process. We conduct comparative experiments on multiple datasets across various models, demonstrating that our approach significantly reduces the token costs in MAD to a considerable extent. Specifically, compared to MAD, our approach achieves an impressive reduction of up to 94.5\% in token costs while maintaining performance degradation below 2.0\%.
Lightweight Operations for Visual Speech Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Visual speech recognition (VSR), which decodes spoken words from video data, offers significant benefits, particularly when audio is unavailable. However, the high dimensionality of video data leads to prohibitive computational costs that demand powerful hardware, limiting VSR deployment on resource-constrained devices. This work addresses this limitation by developing lightweight VSR architectures. Leveraging efficient operation design paradigms, we create compact yet powerful models with reduced resource requirements and minimal accuracy loss. We train and evaluate our models on a large-scale public dataset for recognition of words from video sequences, demonstrating their effectiveness for practical applications. We also conduct an extensive array of ablative experiments to thoroughly analyze the size and complexity of each model. Code and trained models will be made publicly available.
Enhancing Disinformation Detection with Explainable AI and Named Entity Replacement
Venue: arXiv cs.CL 2025
Year: 2025

The automatic detection of disinformation presents a significant challenge in the field of natural language processing. This task addresses a multifaceted societal and communication issue, which needs approaches that extend beyond the identification of general linguistic patterns through data-driven algorithms. In this research work, we hypothesise that text classification methods are not able to capture the nuances of disinformation and they often ground their decision in superfluous features. Hence, we apply a post-hoc explainability method (SHAP, SHapley Additive exPlanations) to identify spurious elements with high impact on the classification models. Our findings show that non-informative elements (e.g., URLs and emoticons) should be removed and named entities (e.g., Rwanda) should be pseudo-anonymized before training to avoid models' bias and increase their generalization capabilities. We evaluate this methodology with internal dataset and external dataset before and after applying extended data preprocessing and named entity replacement. The results show that our proposal enhances on average the performance of a disinformation classification method with external test data in 65.78% without a significant decrease of the internal test performance.
On the Number of Real Types of Univariate Polynomials
Venue: arXiv cs.SC 2025
Year: 2025

The real type of a finite family of univariate polynomials characterizes the combined sign behavior of the polynomials over the real line. We derive an explicit formula for the number of real types subject to given degree bounds. For the special case of a single polynomial we present a closed-form expression involving Fibonacci numbers. This allows us to precisely describe the asymptotic growth of the number of real types as the degree increases, in terms of the golden ratio.
Unveiling tortured phrases in Humanities and Social Sciences
Venue: arXiv cs.DL 2025
Year: 2025

A small amount of unscrupulous people, concerned by their career prospects, resort to paper mill services to publish articles in renowned journals and conference proceedings. These include patchworks of synonymized contents using paraphrasing tools, featuring tortured phrases, increasingly polluting the scientific literature. The Problematic Paper Screener (PPS) has been developed to allow articles (re)assessment on PubPeer. Since most of the known tortured phrases are found in publications in science, technology, engineering, and mathematics (STEM), we extend this work by exploring their presence in the humanities and social sciences (HSS). To do so, we used the PPS to look for tortured abbreviations, generated from the two social science thesauri ELSST and THESOZ. We also used two case studies to find new tortured abbreviations, by screening the Hindawi EDRI journal and the GESIS SSOAR repository. We found a total of 32 multidisciplinary problematic documents, related to Education, Psychology, and Economics. We also generated 121 new fingerprints to be added to the PPS. These articles and future screening have to be investigated by social scientists, as most of it is currently done by STEM domain experts.
Trust-Aware Diversion for Data-Effective Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Dataset distillation compresses a large dataset into a small synthetic subset that retains essential information. Existing methods assume that all samples are perfectly labeled, limiting their real-world applications where incorrect labels are ubiquitous. These mislabeled samples introduce untrustworthy information into the dataset, which misleads model optimization in dataset distillation. To tackle this issue, we propose a Trust-Aware Diversion (TAD) dataset distillation method. Our proposed TAD introduces an iterative dual-loop optimization framework for data-effective distillation. Specifically, the outer loop divides data into trusted and untrusted spaces, redirecting distillation toward trusted samples to guarantee trust in the distillation process. This step minimizes the impact of mislabeled samples on dataset distillation. The inner loop maximizes the distillation objective by recalibrating untrusted samples, thus transforming them into valuable ones for distillation. This dual-loop iteratively refines and compensates for each other, gradually expanding the trusted space and shrinking the untrusted space. Experiments demonstrate that our method can significantly improve the performance of existing dataset distillation methods on three widely used benchmarks (CIFAR10, CIFAR100, and Tiny ImageNet) in three challenging mislabeled settings (symmetric, asymmetric, and real-world).
Preference-aware compensation policies for crowdsourced on-demand services
Venue: arXiv cs.LG 2025
Year: 2025

Crowdsourced on-demand services offer benefits such as reduced costs, faster service fulfillment times, greater adaptability, and contributions to sustainable urban transportation in on-demand delivery contexts. However, the success of an on-demand platform that utilizes crowdsourcing relies on finding a compensation policy that strikes a balance between creating attractive offers for gig workers and ensuring profitability. In this work, we examine a dynamic pricing problem for an on-demand platform that sets request-specific compensation of gig workers in a discrete-time framework, where requests and workers arrive stochastically. The operator's goal is to determine a compensation policy that maximizes the total expected reward over the time horizon. Our approach introduces compensation strategies that explicitly account for gig worker request preferences. To achieve this, we employ the Multinomial Logit model to represent the acceptance probabilities of gig workers, and, as a result, derive an analytical solution that utilizes post-decision states. Subsequently, we integrate this solution into an approximate dynamic programming algorithm. We compare our algorithm against benchmark algorithms, including formula-based policies and an upper bound provided by the full information linear programming solution. Our algorithm demonstrates consistent performance across diverse settings, achieving improvements of at least 2.5-7.5% in homogeneous gig worker populations and 9% in heterogeneous populations over benchmarks, based on fully synthetic data. For real-world data, it surpasses benchmarks by 8% in weak and 20% in strong location preference scenarios.
A Randomised Approach to Distributed Sorting
Venue: arXiv cs.DS 2025
Year: 2025

We introduce and analyse a new, extremely simple, randomised sorting algorithm: - choose a pair of indices $\{i, j\}$ according to some distribution $q$; - sort the elements in positions $i$ and $j$ of the array in ascending order. Choosing $q_{\{i,j\}} \propto 1/|j - i|$ yields an order-$n (\log n)^2$ sorting time. We call it the harmonic sorter. The sorter trivially parallelises in the asynchronous setting, yielding a linear speed-up. We also exhibit a low-communication, synchronous version with a linear speed-up. We compare and contrast this algorithm with other sorters, and discuss some of its benefits, particularly its robustness and amenability to parallelisation and distributed computing.
Causality can systematically address the monsters under the bench(marks)
Venue: arXiv cs.LG 2025
Year: 2025

Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever more complex, high-level tasks make systematic evaluation more challenging. Benchmarks are plagued by various biases, artifacts, or leakage, while models may behave unreliably due to poorly explored failure modes. Haphazard treatments and inconsistent formulations of such "monsters" can contribute to a duplication of efforts, a lack of trust in results, and unsupported inferences. In this position paper, we argue causality offers an ideal framework to systematically address these challenges. By making causal assumptions in an approach explicit, we can faithfully model phenomena, formulate testable hypotheses with explanatory power, and leverage principled tools for analysis. To make causal model design more accessible, we identify several useful Common Abstract Topologies (CATs) in causal graphs which help gain insight into the reasoning abilities in large language models. Through a series of case studies, we demonstrate how the precise yet pragmatic language of causality clarifies the strengths and limitations of a method and inspires new approaches for systematic progress.
REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly
Venue: arXiv cs.RO 2025
Year: 2025

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data, including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and task inversion learning. The REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios. The dataset is publicly available on our project website: https://tuwien-asl.github.io/REASSEMBLE_page/.
GiesKaNe: Bridging Past and Present in Grammatical Theory and Practical Application
Venue: arXiv cs.CL 2025
Year: 2025

This article explores the requirements for corpus compilation within the GiesKaNe project (University of Giessen and Kassel, Syntactic Basic Structures of New High German). The project is defined by three central characteristics: it is a reference corpus, a historical corpus, and a syntactically deeply annotated treebank. As a historical corpus, GiesKaNe aims to establish connections with both historical and contemporary corpora, ensuring its relevance across temporal and linguistic contexts. The compilation process strikes the balance between innovation and adherence to standards, addressing both internal project goals and the broader interests of the research community. The methodological complexity of such a project is managed through a complementary interplay of human expertise and machine-assisted processes. The article discusses foundational topics such as tokenization, normalization, sentence definition, tagging, parsing, and inter-annotator agreement, alongside advanced considerations. These include comparisons between grammatical models, annotation schemas, and established de facto annotation standards as well as the integration of human and machine collaboration. Notably, a novel method for machine-assisted classification of texts along the continuum of conceptual orality and literacy is proposed, offering new perspectives on text selection. Furthermore, the article introduces an approach to deriving de facto standard annotations from existing ones, mediating between standardization and innovation. In the course of describing the workflow the article demonstrates that even ambitious projects like GiesKaNe can be effectively implemented using existing research infrastructure, requiring no specialized annotation tools. Instead, it is shown that the workflow can be based on the strategic use of a simple spreadsheet and integrates the capabilities of the existing infrastructure.
Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers
Venue: arXiv cs.HC 2025
Year: 2025

E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and perceived safety of both users and non-users. Artificial intelligence (AI), as a cutting-edge and widely applied technology, has demonstrated potential to enhance transportation safety, particularly in driver assistance systems. The integration of AI into e-scooters presents a promising approach to addressing these safety concerns. This study aims to explore the factors influencing individuals willingness to use AI-assisted e-scooters. Data were collected using a structured questionnaire, capturing responses from 405 participants. The questionnaire gathered information on demographic characteristics, micromobility usage frequency, road users' perception of safety around e-scooters, perceptions of safety in AI-enabled technology, trust in AI-enabled e-scooters, and involvement in e-scooter crash incidents. To examine the impact of demographic factors on participants' preferences between AI-assisted and regular e-scooters, decision tree analysis is employed, indicating that ethnicity, income, and age significantly influence preferences. To analyze the impact of other factors on the willingness to use AI-enabled e-scooters, a full-scale Structural Equation Model (SEM) is applied, revealing that the perception of safety in AI enabled technology and the level of trust in AI-enabled e-scooters are the strongest predictors.
Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound
Venue: arXiv cs.SD 2025
Year: 2025

The quantification of audio aesthetics remains a complex challenge in audio processing, primarily due to its subjective nature, which is influenced by human perception and cultural context. Traditional methods often depend on human listeners for evaluation, leading to inconsistencies and high resource demands. This paper addresses the growing need for automated systems capable of predicting audio aesthetics without human intervention. Such systems are crucial for applications like data filtering, pseudo-labeling large datasets, and evaluating generative audio models, especially as these models become more sophisticated. In this work, we introduce a novel approach to audio aesthetic evaluation by proposing new annotation guidelines that decompose human listening perspectives into four distinct axes. We develop and train no-reference, per-item prediction models that offer a more nuanced assessment of audio quality. Our models are evaluated against human mean opinion scores (MOS) and existing methods, demonstrating comparable or superior performance. This research not only advances the field of audio aesthetics but also provides open-source models and datasets to facilitate future work and benchmarking. We release our code and pre-trained model at: https://github.com/facebookresearch/audiobox-aesthetics
A Lightweight Method to Disrupt Memorized Sequences in LLM
Venue: arXiv cs.LG 2025
Year: 2025

As language models scale, their performance improves dramatically across a wide range of tasks, but so does their tendency to memorize and regurgitate parts of their training data verbatim. This tradeoff poses serious legal, ethical, and safety concerns, especially in real-world deployments. Existing mitigation techniques, such as differential privacy or model unlearning, often require retraining or access to internal weights making them impractical for most users. In this work, we introduce TokenSwap, a lightweight, post-hoc defense designed for realistic settings where the user can only access token-level outputs. Our key insight is that while large models are necessary for high task performance, small models (e.g., DistilGPT-2) are often sufficient to assign fluent, grammatically plausible probabilities to common function words - and crucially, they memorize far less. By selectively swapping token probabilities between models, TokenSwap preserves the capabilities of large models while reducing their propensity for verbatim reproduction. Evaluations on Pythia-6.9B and Llama-3-8B show up to a 10$\times$ drop in exact memorization with negligible task degradation. Our method offers a practical, accessible solution for mitigating memorized generation in deployed LLMs.