OpenRaiser/Intern-Atlas huggingface dataset:
Policy alone is probably not the solution: A large-scale experiment on how developers struggle to design meaningful end-user explanations
Venue: arXiv cs.HC 2026
Year: 2026

Developers play a central role in determining how machine learning systems are explained in practice, yet they are rarely trained to design explanations for non-technical audiences. Despite this, transparency and explainability requirements are increasingly codified in regulation and organizational policy. It remains unclear how such policies influence developer behavior or the quality of the explanations they produce. We report results from two controlled experiments with 194 participants, typical developers without specialized training in human-centered explainable AI, who designed explanations for an ML-powered diabetic retinopathy screening tool. In the first experiment, differences in policy purpose and level of detail had little effect: policy guidance was often ignored and explanation quality remained low. In the second experiment, stronger enforcement increased formal compliance, but explanations largely remained poorly suited to medical professionals and patients. We further observed that across both experiments, developers repeatedly produced explanations that were technically flawed or difficult to interpret, framed for developers rather than end users, reliant on medical jargon, or insufficiently grounded in the clinical decision context and workflow, with developer-centric framing being the most prevalent. These findings suggest that policy and policy enforcement alone are insufficient to produce meaningful end-user explanations and that responsible AI frameworks may overestimate developers' ability to translate high-level requirements into human-centered designs without additional training, tools, or implementation support.
Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
Venue: arXiv cs.CR 2026
Year: 2026

Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite its many advantages, FL still contends with significant challenges -- most notably regarding security and trust. Zero-Knowledge Proofs (ZKPs) offer a potential solution by establishing trust and enhancing system integrity throughout the FL process. Although several studies have explored ZKP-based FL (ZK-FL), a systematic framework and comprehensive analysis are still lacking. This article makes two key contributions. First, we propose a structured ZK-FL framework that categorizes and analyzes the technical roles of ZKPs across various FL stages and tasks. Second, we introduce a novel algorithm, Verifiable Client Selection FL (Veri-CS-FL), which employs ZKPs to refine the client selection process. In Veri-CS-FL, participating clients generate verifiable proofs for the performance metrics of their local models and submit these concise proofs to the server for efficient verification. The server then selects clients with high-quality local models for uploading, subsequently aggregating the contributions from these selected clients. By integrating ZKPs, Veri-CS-FL not only ensures the accuracy of performance metrics but also fortifies trust among participants while enhancing the overall efficiency and security of FL systems.
Acurast: Decentralized Serverless Cloud
Venue: arXiv cs.CR 2026
Year: 2026

Centralized trust is ubiquitous in today's interconnected world, from computational resources to data storage and its underlying infrastructure. The monopolization of cloud computing resembles a feudalistic system, causing a loss of privacy and data ownership. Cloud Computing and the Internet in general face widely recognized challenges, such as (1) the centralization of trust in auxiliary systems (e.g., centralized cloud providers), (2) the seamless and permissionless interoperability of fragmented ecosystems and (2) the effectiveness, verifiability, and confidentiality of the computation. Acurast is a decentralized serverless cloud that addresses all these shortcomings, following the call for a global-scale cloud founded on the principles of the open-source movement. In Acurast, a purpose-built orchestrator, a reputation engine, and an attestation service are enshrined in the consensus layer. Developers can off-load their computations and verify executions cryptographically. Furthermore, Acurast offers a modular execution layer, taking advantage of secure hardware and trusted execution environments, removing the trust required in third parties, and reducing them to cryptographic hardness assumptions. With this modular architecture, Acurast serves as a decentralized and serverless cloud, allowing confidential and verifiable compute backed by the hardware of security and performance mobile devices.
Imitating AI agents increase diversity in homogeneous information environments but can reduce it in heterogeneous ones
Venue: arXiv cs.CY 2026
Year: 2026

Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full population of Danish digital news articles published in 2022. Varying imitation strategies and AI prevalence across information environments with different baseline structures, we show that the effects of AI-driven imitation are strongly context-dependent: imitating AI agents increase semantic diversity in initially homogeneous environments but can reduce diversity in heterogeneous ones. This pattern is qualitatively consistent across multiple LLMs. However, this diversity arises primarily through stylistic differentiation and variance compression rather than factual enrichment, as AI-generated articles tend to omit information while remaining semantically distinct. These findings indicate that AI-driven imitation produces ambivalent transformations of information environments that may shape collective intelligence in democratic societies.
HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks
Venue: arXiv cs.LG 2026
Year: 2026

Estimating the global Lipschitz constant of neural networks is crucial for understanding and improving their robustness and generalization capabilities. However, precise calculations are NP-hard, and current semidefinite programming (SDP) methods face challenges such as high memory usage and slow processing speeds. In this paper, we propose HiQ-Lip, a hybrid quantum-classical hierarchical method that leverages quantum computing to estimate the global Lipschitz constant. We tackle the estimation by converting it into a Quadratic Unconstrained Binary Optimization problem and implement a multilevel graph coarsening and refinement strategy to adapt to the constraints of contemporary quantum hardware. Our experimental evaluations on fully connected neural networks demonstrate that HiQ-Lip not only provides estimates comparable to state-of-the-art methods but also significantly accelerates the computation process. In specific tests involving two-layer neural networks with 256 hidden neurons, HiQ-Lip doubles the solving speed and offers more accurate upper bounds than the existing best method, LiPopt. These findings highlight the promising utility of small-scale quantum devices in advancing the estimation of neural network robustness.
Towards Open Diversity-Aware Social Interactions
Venue: arXiv cs.HC 2026
Year: 2026

Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much-needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet of Us", an online platform that uses diversity-aware Artificial Intelligence to mediate and empower human social interactions. We discuss the multiple facets of diversity in social settings, the multidisciplinary work that is required to reap the benefits of diversity, and the vision for a diversity-aware hybrid human-AI society.
LLM-Glasses: GenAI-driven Glasses with Haptic Feedback for Navigation of Visually Impaired People
Venue: arXiv cs.HC 2026
Year: 2026

LLM-Glasses is a wearable navigation system which assists visually impaired people by utilizing YOLO-World object detection, GPT-4o-based reasoning, and haptic feedback for real-time guidance. The device translates visual scene understanding into intuitive tactile feedback on the temples, allowing hands-free navigation. Three studies evaluate the system: recognition of 13 haptic patterns with an average recognition rate of 81.3%, VICON-based guidance with predefined paths using haptic cues, and an LLM-guided scene evaluation with decision accuracies of 91.8% without obstacles, 84.6% with static obstacles, and 81.5% with dynamic obstacles. These results show that LLM-Glasses can deliver reliable navigation support in controlled environments and motivate further work on responsiveness and deployment in more complex real-world scenarios.
BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase
Venue: arXiv cs.LG 2026
Year: 2026

In contrast to the assumptions of most existing Electromigration (EM) analysis tools, the evolution of EM-induced stress is inherently non-deterministic, influenced by factors such as input current fluctuations and manufacturing non-idealities. Traditional approaches for estimating stress variations typically involve computationally expensive and inefficient Monte Carlo simulations with industrial solvers, which quantify variations using mean and variance metrics. In this work, we introduce a novel machine learning-based framework, termed BPINN-EM- Post, for efficient stochastic analysis of EM-induced post-voiding aging processes. For the first time, our new approach integrates closed-form analytical solutions with a Bayesian Physics- Informed Neural Network (BPINN) framework to accelerate the analysis. The closed-form solutions enforce physical laws at the individual wire segment level, while the BPINN ensures that physics constraints at inter-segment junctions are satisfied and stochastic behaviors are accurately modeled. By reducing the number of variables in the loss functions through utilizing analytical solutions, our method significantly improves training efficiency without accuracy loss and naturally incorporates variational effects. Additionally, the analytical solutions effectively address the challenge of incorporating initial stress distributions in interconnect structures during post-void stress calculations. Numerical results demonstrate that BPINN-EM-Post achieves over 240x and more than 67x speedup compared to Monte Carlo simulations using the FEM-based COMSOL solver and FDM-based EMSpice, respectively, with marginal accuracy loss.
V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction
Venue: arXiv cs.CV 2026
Year: 2026

Large Vision-Language Models (LVLMs) have made significant strides in the field of video understanding in recent times. Nevertheless, existing video benchmarks predominantly rely on text prompts for evaluation, which often require complex referential language and diminish both the accuracy and efficiency of human model interaction in turn. To address this limitation, we propose V2P-Bench, a robust and comprehensive benchmark for evaluating the ability of LVLMs to understand Video Visual Prompts in human model interaction scenarios. V2P-Bench consists of 980 videos and 1172 well-structured high-quality QA pairs, each paired with manually annotated visual prompt frames. The benchmark spans three main tasks and twelve categories, thereby enabling fine-grained, instance-level evaluation. Through an in-depth analysis of current LVLMs, we identify several key findings: 1) Visual prompts are both more model-friendly and user-friendly in interactive scenarios than text prompts, leading to significantly improved model performance and enhanced user experience. 2) Models are reasonably capable of zero-shot understanding of visual prompts, but struggle with spatiotemporal understanding. Even o1 achieves only 71.8%, far below the human expert score of 88.3%, while most open-source models perform below 60%. 3) LVLMs exhibit pervasive Hack Phenomena in video question answering tasks, which become more pronounced as video length increases and frame sampling density decreases, thereby inflating performance scores artificially. We anticipate that V2P-Bench will not only shed light on these challenges but also serve as a foundational tool for advancing human model interaction and improving the evaluation of video understanding.
FROG: Fair Removal on Graphs
Venue: arXiv cs.LG 2026
Year: 2026

With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many of which are naturally represented as graphs. However, existing graph unlearning methods often modify nodes or edges indiscriminately, overlooking their impact on fairness. For instance, forgetting links between users of different genders may inadvertently exacerbate group disparities. To address this issue, we propose a novel framework that jointly optimizes both the graph structure and the model to achieve fair unlearning. Our method rewires the graph by removing redundant edges that hinder forgetting while preserving fairness through targeted edge augmentation. We further introduce a worst-case evaluation mechanism to assess robustness under challenging scenarios. Experiments on real-world datasets show that our approach achieves more effective and fair unlearning than existing baselines.
SciClaims: An End-to-End Generative System for Biomedical Claim Analysis
Venue: arXiv cs.CL 2026
Year: 2026

We present SciClaims, an interactive web-based system for end-to-end scientific claim analysis in the biomedical domain. Designed for high-stakes use cases such as systematic literature reviews and patent validation, SciClaims extracts claims from text, retrieves relevant evidence from PubMed, and verifies their veracity. The system features a user-friendly interface where users can input scientific text and view extracted claims, predictions, supporting or refuting evidence, and justifications in natural language. Unlike prior approaches, SciClaims seamlessly integrates the entire scientific claim analysis process using a single large language model, without requiring additional fine-tuning. SciClaims is optimized to run efficiently on a single GPU and is publicly available for live interaction.
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture
Venue: arXiv cs.CR 2026
Year: 2026

The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.
DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
Venue: arXiv cs.LG 2026
Year: 2026

As the Mix-of-Experts (MoE) architecture increases the number of parameters in large models, there is an even greater need for model quantization. However, existing quantization methods overlook the expert dynamics of MoE across multiple datasets. Moreover, the existing static quantization cannot adapt MoE to various data change scenarios. In this paper, we perform a multi-level analysis to reveal MoE dynamics and define the significance of each channel/each expert. Based on the analysis results, we propose \textit{DynaMo}, an end-to-end MoE quantization framework. DynaMo adopts an expert-level mixed-precision baseline quantization strategy, which ensures the quantized MoEs are compatible with multiple existing datasets. Furthermore, DynaMo incorporates a channel-level dynamic switching mechanism to adapt these quantized MoE models to novel datasets. Experiments show that DynaMo achieves a 2.78~4.54 PPL decrease and a 1.85%~3.77% accuracy improvement in various datasets, with ~3x inference speedup and negligible overhead.
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
Venue: arXiv cs.SE 2026
Year: 2026

Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domains relies on large tabular datasets, where manual analysis is slow, costly, and error-prone. While Large Language Models (LLMs) offer promising automation potential, they face challenges in analytical reasoning, structured data handling, and ambiguity resolution. This paper introduces GateLens, an LLM-based architecture for reliable analysis of complex tabular data. Its key innovation is the use of Relational Algebra (RA) as a formal intermediate representation between natural-language reasoning and executable code, addressing the reasoning-to-code gap that can arise in direct generation approaches. In our automotive instantiation, GateLens translates natural language queries into RA expressions and generates optimized Python code. Unlike traditional multi-agent or planning-based systems that can be slow, opaque, and costly to maintain, GateLens emphasizes speed, transparency, and reliability. We validate the architecture in automotive software release analytics, where experimental results show that GateLens outperforms the existing Chain-of-Thought (CoT) + Self-Consistency (SC) based system on real-world datasets, particularly in handling complex and ambiguous queries. Ablation studies confirm the essential role of the RA layer. Industrial deployment demonstrates over 80% reduction in analysis time while maintaining high accuracy across domain-specific tasks. GateLens operates effectively in zero-shot settings without requiring few-shot examples or agent orchestration. This work advances deployable LLM system design by identifying key architectural features--intermediate formal representations, execution efficiency, and low configuration overhead--crucial for domain-specific analytical applications.
Towards the Usage of Window Counting Constraints in the Synthesis of Reactive Systems to Reduce State Space Explosion
Venue: arXiv cs.LO 2026
Year: 2026

The synthesis of reactive systems aims for the automated construction of strategies for systems that interact with their environment. Whereas the synthesis approach has the potential to change the development of reactive systems significantly due to the avoidance of manual implementation, it still suffers from a lack of efficient synthesis algorithms for many application scenarios. The translation of the system specification into an automaton that allows for strategy construction (if a winning strategy exists) is nonelementary in the length of the specification in S1S and doubly exponential for LTL, raising the need of highly specialized algorithms. In this article, we present an approach on how to reduce this state space explosion in the construction of this automaton by exploiting a monotonicity property of specifications. For this, we introduce window counting constraints that allow for step-wise refinement or abstraction of specifications. In an iterative synthesis procedure, those window counting constraints are used to construct automata representing over- or under-approximations (depending on the counting constraint) of constraint-compliant behavior. Analysis results on winning regions of previous iterations are used to reduce the size of the next automaton, leading to an overall reduction of the state space explosion extent. We present the implementation results of the iterated synthesis for a zero-sum game setting as proof of concept. Furthermore, we discuss the current limitations of the approach in a zero-sum setting and sketch future work in non-zero-sum settings.
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
Venue: arXiv cs.LG 2026
Year: 2026

Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework, significantly enhancing computational efficiency in multi-task learning. Recently, several SVD-based techniques have been introduced to exploit low-rank structures for enhanced merging, but their reliance on such manually designed rank selection often leads to cross-task interference and suboptimal performance. In this paper, we propose AdaRank, a novel model merging framework that adaptively selects the most beneficial singular directions of task vectors to merge multiple models. We empirically show that the dominant singular components of task vectors can cause critical interference with other tasks, and that naive truncation across tasks and layers degrades performance. In contrast, AdaRank dynamically prunes the singular components that cause interference and offers an optimal amount of information to each task vector by learning to prune ranks during test-time via entropy minimization. Our analysis demonstrates that such method mitigates detrimental overlaps among tasks, while empirical results show that AdaRank consistently achieves state-of-the-art performance with various backbones and number of tasks, reducing the performance gap between fine-tuned models to nearly 1%.
MediTools -- Medical Education Powered by LLMs
Venue: arXiv cs.CY 2026
Year: 2026

Artificial Intelligence (AI) has been advancing rapidly and with the advent of large language models (LLMs) in late 2022, numerous opportunities have emerged for adopting this technology across various domains, including medicine. These innovations hold immense potential to revolutionize and modernize medical education. Our research project leverages large language models to enhance medical education and address workflow challenges through the development of MediTools - AI Medical Education. This prototype application focuses on developing interactive tools that simulate real-life clinical scenarios, provide access to medical literature, and keep users updated with the latest medical news. Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions and enables interaction with LLMs acting as virtual patients. This platform allows users to practice their diagnostic skills and enhance their clinical decision-making abilities. The application also features two additional tools: an AI-enhanced PubMed tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties. A comprehensive survey has been conducted among medical professionals and students to gather initial feedback on the effectiveness and user satisfaction of MediTools, providing insights for further development and refinement of the application. This research demonstrates the potential of AI-driven tools in transforming and revolutionizing medical education, offering a scalable and interactive platform for continuous learning and skill development.