OpenRaiser/Intern-Atlas huggingface dataset:
Robust and Efficient Communication in Multi-Agent Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents. However, most existing approaches assume that communication is instantaneous, reliable, and has unlimited bandwidth; these conditions are rarely met in real-world deployments. This survey systematically reviews recent advances in robust and efficient communication strategies for MARL under realistic constraints, including message perturbations, transmission delays, and limited bandwidth. Furthermore, because the challenges of low-latency reliability, bandwidth-intensive data sharing, and communication-privacy trade-offs are central to practical MARL systems, we focus on three applications involving cooperative autonomous driving, distributed simultaneous localization and mapping, and federated learning. Finally, we identify key open challenges and future research directions, advocating a unified approach that co-designs communication, learning, and robustness to bridge the gap between theoretical MARL models and practical implementations.
From Synthetic Scenes to Real Performance: Enhancing Spatial Reasoning in VLMs
Venue: arXiv cs.CV 2025
Year: 2025

Fine-tuning Vision-Language Models (VLMs) is a common strategy to improve performance following an ad-hoc data collection and annotation of real-world scenes. However, this process is often prone to biases, errors, and distribution imbalance, resulting in overfitting and imbalanced performance. Although a few studies have tried to address this problem by generating synthetic data, they lacked control over distribution bias and annotation quality. To address these challenges, we redesign the fine-tuning process in two ways. First, we control the generation of data and its annotations, ensuring it is free from bias, distribution imbalance, and annotation errors. We automatically construct the dataset by comprehensively sampling objects' attributes, including color, shape, size, and position within the scene. Secondly, using this annotated dataset, we fine-tune state-of-the-art VLMs and assess performance transferability to real-world data on the absolute position task. We conduct exhaustive evaluations on both synthetic and real-world benchmarks. Our experiments reveal two key findings: 1) fine-tuning on balanced synthetic data yields uniform performance across the visual scene and mitigates common biases; and 2) fine-tuning on synthetic stimuli significantly improves performance on real-world data (COCO), outperforming models fine-tuned in the matched setting.
Quantifying and Improving Adaptivity in Conformal Prediction through Input Transformations
Venue: arXiv cs.LG 2025
Year: 2025

Conformal prediction constructs a set of labels instead of a single point prediction, while providing a probabilistic coverage guarantee. Beyond the coverage guarantee, adaptiveness to example difficulty is an important property. It means that the method should produce larger prediction sets for more difficult examples, and smaller ones for easier examples. Existing evaluation methods for adaptiveness typically analyze coverage rate violation or average set size across bins of examples grouped by difficulty. However, these approaches often suffer from imbalanced binning, which can lead to inaccurate estimates of coverage or set size. To address this issue, we propose a binning method that leverages input transformations to sort examples by difficulty, followed by uniform-mass binning. Building on this binning, we introduce two metrics to better evaluate adaptiveness. These metrics provide more reliable estimates of coverage rate violation and average set size due to balanced binning, leading to more accurate adaptivity assessment. Through experiments, we demonstrate that our proposed metric correlates more strongly with the desired adaptiveness property compared to existing ones. Furthermore, motivated by our findings, we propose a new adaptive prediction set algorithm that groups examples by estimated difficulty and applies group-conditional conformal prediction. This allows us to determine appropriate thresholds for each group. Experimental results on both (a) an Image Classification (ImageNet) (b) a medical task (visual acuity prediction) show that our method outperforms existing approaches according to the new metrics.
A Comparative Evaluation of Prominent Methods in Autonomous Vehicle Certification
Venue: arXiv cs.RO 2025
Year: 2025

The "Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the basic safety requirements that autonomous vehicles must meet will be verified and certified, and which methods will be used. This paper focuses on the comparative evaluation of the prominent methods planned to be used in the certification process of autonomous vehicles. It examines the prominent methods used in the certification process, develops a pipeline for the certification process of autonomous vehicles, and determines the stages, actors, and areas where the addressed methods can be applied.
Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images
Venue: arXiv cs.CV 2025
Year: 2025

Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a Bayesian segmentation framework that infers PD-L1 expression directly from H&E-stained histology images using Multimodal Posterior Sampling (MPS). Built upon nnUNet-v2, our method samples diverse model checkpoints during cyclic training to approximate the posterior, enabling both accurate segmentation and epistemic uncertainty estimation via entropy and standard deviation. Evaluated on a dataset of lung squamous cell carcinoma, our approach achieves competitive performance against established baselines with mean Dice Score and mean IoU of 0.805 and 0.709, respectively, while providing pixel-wise uncertainty maps. Uncertainty estimates show strong correlation with segmentation error, though calibration remains imperfect. These results suggest that uncertainty-aware H&E-based PD-L1 prediction is a promising step toward scalable, interpretable biomarker assessment in clinical workflows.
OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning
Venue: arXiv cs.CV 2025
Year: 2025

Ultrasound (US) is one of the most widely used medical imaging modalities, thanks to its low cost, portability, real-time feedback, and absence of ionizing radiation. However, US image interpretation remains highly operator-dependent and varies significantly across anatomical regions, acquisition protocols, and device types. These variations, along with unique challenges such as speckle, low contrast, and limited standardized annotations, hinder the development of generalizable, label-efficient ultrasound AI models. In this paper, we propose OpenUS, the first reproducible, open-source ultrasound foundation model built on a large collection of public data. OpenUS employs a vision Mamba backbone, capturing both local and global long-range dependencies across the image. To extract rich features during pre-training, we introduce a novel self-adaptive masking framework that combines contrastive learning with masked image modeling. This strategy integrates the teacher's attention map with student reconstruction loss, adaptively refining clinically-relevant masking to enhance pre-training effectiveness. OpenUS also applies a dynamic learning schedule to progressively adjust the difficulty of the pre-training process. To develop the foundation model, we compile the largest to-date public ultrasound dataset comprising over 308K images from 42 publicly available datasets, covering diverse anatomical regions, institutions, imaging devices, and disease types. Our pre-trained OpenUS model can be easily adapted to specific downstream tasks by serving as a backbone for label-efficient fine-tuning. Code is available at https://github.com/XZheng0427/OpenUS.
Generalizing Fair Clustering to Multiple Groups: Algorithms and Applications
Venue: arXiv cs.LG 2025
Year: 2025

Clustering is a fundamental task in machine learning and data analysis, but it frequently fails to provide fair representation for various marginalized communities defined by multiple protected attributes -- a shortcoming often caused by biases in the training data. As a result, there is a growing need to enhance the fairness of clustering outcomes, ideally by making minimal modifications, possibly as a post-processing step after conventional clustering. Recently, Chakraborty et al. [COLT'25] initiated the study of \emph{closest fair clustering}, though in a restricted scenario where data points belong to only two groups. In practice, however, data points are typically characterized by many groups, reflecting diverse protected attributes such as age, ethnicity, gender, etc. In this work, we generalize the study of the \emph{closest fair clustering} problem to settings with an arbitrary number (more than two) of groups. We begin by showing that the problem is NP-hard even when all groups are of equal size -- a stark contrast with the two-group case, for which an exact algorithm exists. Next, we propose near-linear time approximation algorithms that efficiently handle arbitrary-sized multiple groups, thereby answering an open question posed by Chakraborty et al. [COLT'25]. Leveraging our closest fair clustering algorithms, we further achieve improved approximation guarantees for the \emph{fair correlation clustering} problem, advancing the state-of-the-art results established by Ahmadian et al. [AISTATS'20] and Ahmadi et al. [2020]. Additionally, we are the first to provide approximation algorithms for the \emph{fair consensus clustering} problem involving multiple (more than two) groups, thus addressing another open direction highlighted by Chakraborty et al. [COLT'25].
HetDAPAC: Leveraging Attribute Heterogeneity in Distributed Attribute-Based Private Access Control
Venue: arXiv cs.CR 2025
Year: 2025

Verifying user attributes to provide fine-grained access control to databases is fundamental to attribute-based authentication. Either a single (central) authority verifies all the attributes, or multiple independent authorities verify the attributes distributedly. In the central setup, the authority verifies all user attributes, and the user downloads only the authorized record. While this is communication efficient, it reveals all user attributes to the authority. A distributed setup prevents this privacy breach by letting each authority verify and learn only one attribute. Motivated by this, Jafarpisheh~et~al. introduced an information-theoretic formulation, called distributed attribute-based private access control (DAPAC). With $N$ non-colluding authorities (servers), $N$ attributes and $K$ possible values for each attribute, the DAPAC system lets each server learn only the single attribute value that it verifies, and is oblivious to the remaining $N-1$. The user retrieves its designated record, without learning anything about the remaining database records. The goal is to maximize the rate, i.e., the ratio of desired message size to total download size. However, not all attributes are sensitive, and DAPAC's privacy constraints can be too restrictive, negatively affecting the rate. To leverage the heterogeneous privacy requirements of user attributes, we propose heterogeneous (Het)DAPAC, a framework which off-loads verification of $N-D$ of the $N$ attributes to a central server, and retains DAPAC's architecture for the $D$ sensitive attributes. We first present a HetDAPAC scheme, which improves the rate from $\frac{1}{2K}$ to $\frac{1}{K+1}$, while sacrificing the privacy of a few non-sensitive attributes. Unlike DAPAC, our scheme entails a download imbalance across servers; we propose a second scheme achieving a balanced per-server download and a rate of $\frac{D+1}{2KD}$.
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to assess open-ended, economically consequential tasks in high-stakes domains like Legal and Finance, where practical returns are paramount. To address this, we introduce Professional Reasoning Bench (PRBench), a realistic, open-ended, and difficult benchmark of real-world problems in Finance and Law. We open-source its 1,100 expert-authored tasks and 19,356 expert-curated criteria, making it, to our knowledge, the largest public, rubric-based benchmark for both legal and finance domains. We recruit 182 qualified professionals, holding JDs, CFAs, or 6+ years of experience, who contributed tasks inspired by their actual workflows. This process yields significant diversity, with tasks spanning 114 countries and 47 US jurisdictions. Our expert-curated rubrics are validated through a rigorous quality pipeline, including independent expert validation. Subsequent evaluation of 20 leading models reveals substantial room for improvement, with top scores of only 0.39 (Finance) and 0.37 (Legal) on our Hard subsets. We further catalog associated economic impacts of the prompts and analyze performance using human-annotated rubric categories. Our analysis shows that models with similar overall scores can diverge significantly on specific capabilities. Common failure modes include inaccurate judgments, a lack of process transparency and incomplete reasoning, highlighting critical gaps in their reliability for professional adoption.
Private Frequency Estimation Via Residue Number Systems
Venue: arXiv cs.CR 2025
Year: 2025

We present \textsf{ModularSubsetSelection} (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. Given a universe of size $k$ and $n$ users, our $\varepsilon$-LDP mechanism encodes each input via a Residue Number System (RNS) over $\ell$ pairwise-coprime moduli $m_0, \ldots, m_{\ell-1}$, and reports a randomly chosen index $j \in [\ell]$ along with the perturbed residue using the statistically optimal \textsf{SubsetSelection} (SS) (Wang et al. 2016). This design reduces the user communication cost from $Θ\bigl(ω\log_2(k/ω)\bigr)$ bits required by standard SS (with $ω\approx k/(e^\varepsilon+1)$) down to $\lceil \log_2 \ell \rceil + \lceil \log_2 m_j \rceil$ bits, where $m_j < k$. Server-side decoding runs in $Θ(n + r k \ell)$ time, where $r$ is the number of LSMR (Fong and Saunders 2011) iterations. In practice, with well-conditioned moduli (\textit{i.e.}, constant $r$ and $\ell = Θ(\log k)$), this becomes $Θ(n + k \log k)$. We prove that MSS achieves worst-case MSE within a constant factor of state-of-the-art protocols such as SS and \textsf{ProjectiveGeometryResponse} (PGR) (Feldman et al. 2022) while avoiding the algebraic prerequisites and dynamic-programming decoder required by PGR. Empirically, MSS matches the estimation accuracy of SS, PGR, and \textsf{RAPPOR} (Erlingsson, Pihur, and Korolova 2014) across realistic $(k, \varepsilon)$ settings, while offering faster decoding than PGR and shorter user messages than SS. Lastly, by sampling from multiple moduli and reporting only a single perturbed residue, MSS achieves the lowest reconstruction-attack success rate among all evaluated LDP protocols.
From customer to product: design tools for the visually impaired
Venue: arXiv cs.HC 2025
Year: 2025

Navigation in new or unknown environments is vital, especially for visually impaired individuals. While many solutions exist, few are tailored to specific disabilities, often due to limited collaboration with handicap users in the design process. This article examines 7 tools that enable visually impaired users to participate in design, selected through a systematic review and analyzed for affinities, differences, and applications. The study suggests correlations among the tools, offering a foundation for a methodology that enhances inclusive design and accessibility.
Social and Physical Attributes-Defined Trust Evaluation for Effective Collaborator Selection in Human-Device Coexistence Systems
Venue: arXiv cs.HC 2025
Year: 2025

In human-device coexistence systems, collaborations among devices are determined by not only physical attributes such as network topology but also social attributes among human users. Consequently, trust evaluation of potential collaborators based on these multifaceted attributes becomes critical for ensuring the eventual outcome. However, due to the high heterogeneity and complexity of physical and social attributes, efficiently integrating them for accurate trust evaluation remains challenging. To overcome this difficulty, a canonical correlation analysis-enhanced hypergraph self-supervised learning (HSLCCA) method is proposed in this research. First, by treating all attributes as relationships among connected devices, a relationship hypergraph is constructed to comprehensively capture inter-device relationships across three dimensions: spatial attribute-related, device attribute-related, and social attribute-related. Next, a self-supervised learning framework is developed to integrate these multi-dimensional relationships and generate device embeddings enriched with relational semantics. In this learning framework, the relationship hypergraph is augmented into two distinct views to enhance semantic information. A parameter-sharing hypergraph neural network is then utilized to learn device embeddings from both views. To further enhance embedding quality, a CCA approach is applied, allowing the comparison of data between the two views. Finally, the trustworthiness of devices is calculated based on the learned device embeddings. Extensive experiments demonstrate that the proposed HSLCCA method significantly outperforms the baseline algorithm in effectively identifying trusted devices.
Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
Venue: arXiv cs.LG 2025
Year: 2025

Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings due to heavy computation and communication and statistical/system heterogeneity. We propose FedGen-Edge, a framework that decouples a frozen, pre-trained global backbone from lightweight client-side adapters and federates only the adapters. Using Low-Rank Adaptation (LoRA) constrains client updates to a compact subspace, which reduces uplink traffic by more than 99 percent versus full-model FedAvg, stabilizes aggregation under non-IID data, and naturally supports personalization because each client can keep a locally tuned adapter. On language modeling (PTB) and image generation (CIFAR-10), FedGen-Edge achieves lower perplexity/FID and faster convergence than strong baselines while retaining a simple FedAvg-style server. A brief ablation shows diminishing returns beyond moderate LoRA rank and a trade-off between local epochs and client drift. FedGen-Edge offers a practical path toward privacy-preserving, resource-aware, and personalized generative AI on heterogeneous edge devices.
Loss Given Default Prediction Under Measurement-Induced Mixture Distributions: An Information-Theoretic Approach
Venue: arXiv cs.LG 2025
Year: 2025

Loss Given Default (LGD) modeling faces a fundamental data quality constraint: 90% of available training data consists of proxy estimates based on pre-distress balance sheets rather than actual recovery outcomes from completed bankruptcy proceedings. We demonstrate that this mixture-contaminated training structure causes systematic failure of recursive partitioning methods, with Random Forest achieving negative r-squared (-0.664, worse than predicting the mean) on held-out test data. Information-theoretic approaches based on Shannon entropy and mutual information provide superior generalization, achieving r-squared of 0.191 and RMSE of 0.284 on 1,218 corporate bankruptcies (1980-2023). Analysis reveals that leverage-based features contain 1.510 bits of mutual information while size effects contribute only 0.086 bits, contradicting regulatory assumptions about scale-dependent recovery. These results establish practical guidance for financial institutions deploying LGD models under Basel III requirements when representative outcome data is unavailable at sufficient scale. The findings generalize to medical outcomes research, climate forecasting, and technology reliability-domains where extended observation periods create unavoidable mixture structure in training data.
CLINB: A Climate Intelligence Benchmark for Foundational Models
Venue: arXiv cs.AI 2025
Year: 2025

Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CLINB, a benchmark that assesses models on open-ended, grounded, multimodal question answering tasks with clear requirements for knowledge quality and evidential support. CLINB relies on a dataset of real users' questions and evaluation rubrics curated by leading climate scientists. We implement and validate a model-based evaluation process and evaluate several frontier models. Our findings reveal a critical dichotomy. Frontier models demonstrate remarkable knowledge synthesis capabilities, often exhibiting PhD-level understanding and presentation quality. They outperform "hybrid" answers curated by domain experts assisted by weaker models. However, this performance is countered by failures in grounding. The quality of evidence varies, with substantial hallucination rates for references and images. We argue that bridging this gap between knowledge synthesis and verifiable attribution is essential for the deployment of AI in scientific workflows and that reliable, interpretable benchmarks like CLINB are needed to progress towards building trustworthy AI systems.
Distributed Q-learning-based Shortest-Path Tree Construction in IoT Sensor Networks
Venue: arXiv cs.DC 2025
Year: 2025

Efficient routing in IoT sensor networks is critical for minimizing energy consumption and latency. Traditional centralized algorithms, such as Dijkstra's, are computationally intensive and ill-suited for dynamic, distributed IoT environments. We propose a novel distributed Q-learning framework for constructing shortest-path trees (SPTs), enabling sensor nodes to independently learn optimal next-hop decisions using only local information. States are defined based on node positions and routing history, with a reward function that incentivizes progression toward the sink while penalizing inefficient paths. Trained on diverse network topologies, the framework generalizes effectively to unseen networks. Simulations across 100 to 500 nodes demonstrate near-optimal routing accuracy (over 99% for networks with more than 300 nodes), with minor deviations (1-2 extra hops) in smaller networks having negligible impact on performance. Compared to centralized and flooding-based methods, our approach reduces communication overhead, adapts to topology changes, and enhances scalability and energy efficiency. This work underscores the potential of Q-learning for autonomous, robust routing in resource-constrained IoT networks, offering a scalable alternative to traditional protocols.