OpenRaiser/Intern-Atlas huggingface dataset:
Augmented Weak Distance for Fast and Accurate Bounds Checking
Venue: arXiv cs.PL 2025
Year: 2025

This work advances floating-point program verification by introducing Augmented Weak-Distance (AWD), a principled extension of the Weak-Distance (WD) framework. WD is a recent approach that reformulates program analysis as a numerical minimization problem, providing correctness guarantees through non-negativity and zero-target correspondence. It consistently outperforms traditional floating-point analysis, often achieving speedups of several orders of magnitude. However, WD suffers from ill-conditioned optimization landscapes and branching discontinuities, which significantly hinder its practical effectiveness. AWD overcomes these limitations with two key contributions. First, it enforces the Monotonic Convergence Condition (MCC), ensuring a strictly decreasing objective function and mitigating misleading optimization stalls. Second, it extends WD with a per-path analysis scheme, preserving the correctness guarantees of weak-distance theory while integrating execution paths into the optimization process. These enhancements construct a well-conditioned optimization landscape, enabling AWD to handle floating-point programs effectively, even in the presence of loops and external functions. We evaluate AWD on SV-COMP 2024, a widely used benchmark for software verification.Across 40 benchmarks initially selected for bounds checking, AWD achieves 100% accuracy, matching the state-of-the-art bounded model checker CBMC, one of the most widely used verification tools, while running 170X faster on average. In contrast, the static analysis tool Astrée, despite being fast, solves only 17.5% of the benchmarks. These results establish AWD as a highly efficient alternative to CBMC for bounds checking, delivering precise floating-point verification without compromising correctness.
VoQA: Visual-only Question Answering
Venue: arXiv cs.CV 2025
Year: 2025

Visual understanding requires interpreting both natural scenes and the textual information that appears within them, motivating tasks such as Visual Question Answering (VQA). However, current VQA benchmarks overlook scenarios with visually embedded questions, whereas advanced agents should be able to see the question without separate text input as humans. We introduce Visual-only Question Answering (VoQA), where both the scene and the question appear within a single image, requiring models to perceive and reason purely through vision. This setting supports more realistic visual understanding and interaction in scenarios where questions or instructions are embedded directly in the visual scene. Evaluations under pure visual-only zero-shot, prompt-guided and OCR-assisted settings show that current models exhibit a clear performance drop compared to traditional VQA. To address this, we investigate question-alignment fine-tuning strategies designed to guide models toward interpreting the visual question prior to reasoning. Leveraging VoQA dataset together with these strategies yields robust vision-only reasoning while preserving cross-task generalization to traditional VQA, reflecting the complementary visual and textual reasoning capabilities fostered through VoQA training. The code and data are publicly available.
Toward Embodied AGI: A Review of Embodied AI and the Road Ahead
Venue: arXiv cs.AI 2025
Year: 2025

Artificial General Intelligence (AGI) is often envisioned as inherently embodied. With recent advances in robotics and foundational AI models, we stand at the threshold of a new era-one marked by increasingly generalized embodied AI systems. This paper contributes to the discourse by introducing a systematic taxonomy of Embodied AGI spanning five levels (L1-L5). We review existing research and challenges at the foundational stages (L1-L2) and outline the key components required to achieve higher-level capabilities (L3-L5). Building on these insights and existing technologies, we propose a conceptual framework for an L3+ robotic brain, offering both a technical outlook and a foundation for future exploration.
Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow
Venue: arXiv cs.CV 2025
Year: 2025

Due to the unidirectional masking mechanism, Decoder-Only models propagate information from left to right. LVLMs (Large Vision-Language Models) follow the same architecture, with visual information gradually integrated into semantic representations during forward propagation. Through systematic analysis, we observe that the majority of the visual information is absorbed into the semantic representations. However, the model's attention distribution does not exhibit sufficient emphasis on semantic representations. This misalignment between the attention distribution and the actual information flow undermines the model's visual understanding ability and contributes to hallucinations. To address this issue, we enhance the model's visual understanding by leveraging the core information embedded in semantic representations. Specifically, we identify attention heads that focus on core semantic representations based on their attention distributions. Then, through a two-stage optimization paradigm, we propagate the advantages of these attention heads across the entire model, aligning the attention distribution with the actual information flow. We evaluate our method on three image captioning benchmarks using five different LVLMs, demonstrating its effectiveness in significantly reducing hallucinations. Further experiments reveal a trade-off between reduced hallucinations and richer details. Notably, our method allows for manual adjustment of the model's conservativeness, enabling flexible control to meet diverse real-world requirements.
Speculative Decoding Reimagined for Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces Multimodal Speculative Decoding (MSD) to accelerate Multimodal Large Language Models (MLLMs) inference. Speculative decoding has been shown to accelerate Large Language Models (LLMs) without sacrificing accuracy. However, current speculative decoding methods for MLLMs fail to achieve the same speedup as they do for LLMs. To address this, we reimagine speculative decoding specifically for MLLMs. Our analysis of MLLM characteristics reveals two key design principles for MSD: (1) Text and visual tokens have fundamentally different characteristics and need to be processed separately during drafting. (2) Both language modeling ability and visual perception capability are crucial for the draft model. For the first principle, MSD decouples text and visual tokens in the draft model, allowing each to be handled based on its own characteristics. For the second principle, MSD uses a two-stage training strategy: In stage one, the draft model is trained on text-only instruction-tuning datasets to improve its language modeling ability. In stage two, MSD gradually introduces multimodal data to enhance the visual perception capability of the draft model. Experiments show that MSD boosts inference speed by up to $2.29\times$ for LLaVA-1.5-7B and up to $2.46\times$ for LLaVA-1.5-13B on multimodal benchmarks, demonstrating its effectiveness. Our code is available at https://github.com/Lyn-Lucy/MSD.
AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Momentum
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models (LLMs), especially in scenarios where supervised fine-tuning (SFT) falls short due to limited chain-of-thought (CoT) data. Among RL-based post-training methods, group relative advantage estimation, as exemplified by Group Relative Policy Optimization (GRPO), has attracted considerable attention for eliminating the dependency on the value model, thereby simplifying training compared to traditional approaches like Proximal Policy Optimization (PPO). However, we observe that exsiting group relative advantage estimation method still suffers from training inefficiencies, particularly when the estimated advantage approaches zero. To address this limitation, we propose Advantage-Augmented Policy Optimization (AAPO), a novel RL algorithm that optimizes the cross-entropy (CE) loss using advantages enhanced through a momentum-based estimation scheme. This approach effectively mitigates the inefficiencies associated with group relative advantage estimation. Experimental results on multiple mathematical reasoning benchmarks demonstrate the superior performance of AAPO.
AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis
Venue: arXiv cs.SD 2025
Year: 2025

This paper presents AquaSignal, a modular and scalable pipeline for preprocessing, denoising, classification, and novelty detection of underwater acoustic signals. Designed to operate effectively in noisy and dynamic marine environments, AquaSignal integrates state-of-the-art deep learning architectures to enhance the reliability and accuracy of acoustic signal analysis. The system is evaluated on a combined dataset from the Deepship and Ocean Networks Canada (ONC) benchmarks, providing a diverse set of real-world underwater scenarios. AquaSignal employs a U-Net architecture for denoising, a ResNet18 convolutional neural network for classifying known acoustic events, and an AutoEncoder-based model for unsupervised detection of novel or anomalous signals. To our knowledge, this is the first comprehensive study to apply and evaluate this combination of techniques on maritime vessel acoustic data. Experimental results show that AquaSignal improves signal clarity and task performance, achieving 71% classification accuracy and 91% accuracy in novelty detection. Despite slightly lower classification performance compared to some state-of-the-art models, differences in data partitioning strategies limit direct comparisons. Overall, AquaSignal demonstrates strong potential for real-time underwater acoustic monitoring in scientific, environmental, and maritime domains.
Towards Generating Realistic Underwater Images
Venue: arXiv cs.CV 2025
Year: 2025

This paper explores the use of contrastive learning and generative adversarial networks for generating realistic underwater images from synthetic images with uniform lighting. We investigate the performance of image translation models for generating realistic underwater images using the VAROS dataset. Two key evaluation metrics, Fréchet Inception Distance (FID) and Structural Similarity Index Measure (SSIM), provide insights into the trade-offs between perceptual quality and structural preservation. For paired image translation, pix2pix achieves the best FID scores due to its paired supervision and PatchGAN discriminator, while the autoencoder model attains the highest SSIM, suggesting better structural fidelity despite producing blurrier outputs. Among unpaired methods, CycleGAN achieves a competitive FID score by leveraging cycle-consistency loss, whereas CUT, which replaces cycle-consistency with contrastive learning, attains higher SSIM, indicating improved spatial similarity retention. Notably, incorporating depth information into CUT results in the lowest overall FID score, demonstrating that depth cues enhance realism. However, the slight decrease in SSIM suggests that depth-aware learning may introduce structural variations.
SafetyNet: Detecting Harmful Outputs in LLMs by Modeling and Monitoring Deceptive Behaviors
Venue: arXiv cs.AI 2025
Year: 2025

High-risk industries like nuclear and aviation use real-time monitoring to detect dangerous system conditions. Similarly, Large Language Models (LLMs) need monitoring safeguards. We propose a real-time framework to predict harmful AI outputs before they occur by using an unsupervised approach that treats normal behavior as the baseline and harmful outputs as outliers. Our study focuses specifically on backdoor-triggered responses -- where specific input phrases activate hidden vulnerabilities causing the model to generate unsafe content like violence, pornography, or hate speech. We address two key challenges: (1) identifying true causal indicators rather than surface correlations, and (2) preventing advanced models from deception -- deliberately evading monitoring systems. Hence, we approach this problem from an unsupervised lens by drawing parallels to human deception: just as humans exhibit physical indicators while lying, we investigate whether LLMs display distinct internal behavioral signatures when generating harmful content. Our study addresses two critical challenges: 1) designing monitoring systems that capture true causal indicators rather than superficial correlations; and 2)preventing intentional evasion by increasingly capable "Future models''. Our findings show that models can produce harmful content through causal mechanisms and can become deceptive by: (a) alternating between linear and non-linear representations, and (b) modifying feature relationships. To counter this, we developed Safety-Net -- a multi-detector framework that monitors different representation dimensions, successfully detecting harmful behavior even when information is shifted across representational spaces to evade individual monitors. Our evaluation shows 96% accuracy in detecting harmful cases using our unsupervised ensemble approach.
From Metadata to Storytelling: A Framework For 3D Cultural Heritage Visualization on RDF Data
Venue: arXiv cs.DL 2025
Year: 2025

This paper introduces a pipeline for integrating semantic metadata, 3D models, and storytelling, enhancing cultural heritage digitization. Using the Aldrovandi Digital Twin case study, it outlines a reusable workflow combining RDF-driven narratives and data visualization for creating interactive experiences to facilitate access to cultural heritage.
Domain Adaptation for Multi-label Image Classification: a Discriminator-free Approach
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces a discriminator-free adversarial-based approach termed DDA-MLIC for Unsupervised Domain Adaptation (UDA) in the context of Multi-Label Image Classification (MLIC). While recent efforts have explored adversarial-based UDA methods for MLIC, they typically include an additional discriminator subnet. Nevertheless, decoupling the classification and the discrimination tasks may harm their task-specific discriminative power. Herein, we address this challenge by presenting a novel adversarial critic directly derived from the task-specific classifier. Specifically, we employ a two-component Gaussian Mixture Model (GMM) to model both source and target predictions, distinguishing between two distinct clusters. Instead of using the traditional Expectation Maximization (EM) algorithm, our approach utilizes a Deep Neural Network (DNN) to estimate the parameters of each GMM component. Subsequently, the source and target GMM parameters are leveraged to formulate an adversarial loss using the Fréchet distance. The proposed framework is therefore not only fully differentiable but is also cost-effective as it avoids the expensive iterative process usually induced by the standard EM method. The proposed method is evaluated on several multi-label image datasets covering three different types of domain shift. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods in terms of precision while requiring a lower number of parameters. The code is made publicly available at github.com/cvi2snt/DDA-MLIC.
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey
Venue: arXiv cs.CV 2025
Year: 2025

Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks.
Relational Hoare Logic for Realistically Modelled Machine Code
Venue: arXiv cs.LO 2025
Year: 2025

Many security- and performance-critical domains, such as cryptography, rely on low-level verification to minimize the trusted computing surface and allow code to be written directly in assembly. However, verifying assembly code against a realistic machine model is a challenging task. Furthermore, certain security properties -- such as constant-time behavior -- require relational reasoning that goes beyond traditional correctness by linking multiple execution traces within a single specification. Yet, relational verification has been extensively explored at a higher level of abstraction. In this work, we introduce a Hoare-style logic that provides low-level, expressive relational verification. We demonstrate our approach on the s2n-bignum library, proving both constant-time discipline and equivalence between optimized and verification-friendly routines. Formalized in HOL Light, our results confirm the real-world applicability of relational verification in large assembly codebases.
Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Many studies focus on data annotation techniques for training effective PRMs. However, current methods encounter a significant issue when applied to long CoT reasoning processes: they tend to focus solely on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect. These methods overlook the unique self-correction and reflection mechanisms inherent in long CoT, where correct reasoning steps may still occur after initial reasoning mistakes. To address this issue, we propose a novel data annotation method for PRMs specifically designed to score the long CoT reasoning process. Given that under the reflection pattern, correct and incorrect steps often alternate, we introduce the concepts of Error Propagation and Error Cessation, enhancing PRMs' ability to identify both effective self-correction behaviors and reasoning based on erroneous steps. Leveraging an LLM-based judger for annotation, we collect 1.7 million data samples to train a 7B PRM and evaluate it at both solution and step levels. Experimental results demonstrate that compared to existing open-source PRMs and PRMs trained on open-source datasets, our PRM achieves superior performance across various metrics, including search guidance, BoN, and F1 scores. Compared to widely used MC-based annotation methods, our annotation approach not only achieves higher data efficiency but also delivers superior performance. Detailed analysis is also conducted to demonstrate the stability and generalizability of our method.
Explaining Unreliable Perception in Automated Driving: A Fuzzy-based Monitoring Approach
Venue: arXiv cs.LG 2025
Year: 2025

Autonomous systems that rely on Machine Learning (ML) utilize online fault tolerance mechanisms, such as runtime monitors, to detect ML prediction errors and maintain safety during operation. However, the lack of human-interpretable explanations for these errors can hinder the creation of strong assurances about the system's safety and reliability. This paper introduces a novel fuzzy-based monitor tailored for ML perception components. It provides human-interpretable explanations about how different operating conditions affect the reliability of perception components and also functions as a runtime safety monitor. We evaluated our proposed monitor using naturalistic driving datasets as part of an automated driving case study. The interpretability of the monitor was evaluated and we identified a set of operating conditions in which the perception component performs reliably. Additionally, we created an assurance case that links unit-level evidence of \textit{correct} ML operation to system-level \textit{safety}. The benchmarking demonstrated that our monitor achieved a better increase in safety (i.e., absence of hazardous situations) while maintaining availability (i.e., ability to perform the mission) compared to state-of-the-art runtime ML monitors in the evaluated dataset.
Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks
Venue: arXiv cs.CG 2025
Year: 2025

In the era of foundation models and Large Language Models (LLMs), Euclidean space is the de facto geometric setting of our machine learning architectures. However, recent literature has demonstrated that this choice comes with fundamental limitations. To that end, non-Euclidean learning is quickly gaining traction, particularly in web-related applications where complex relationships and structures are prevalent. Non-Euclidean spaces, such as hyperbolic, spherical, and mixed-curvature spaces, have been shown to provide more efficient and effective representations for data with intrinsic geometric properties, including web-related data like social network topology, query-document relationships, and user-item interactions. Integrating foundation models with non-Euclidean geometries has great potential to enhance their ability to capture and model the underlying structures, leading to better performance in search, recommendations, and content understanding. This workshop focuses on the intersection of Non-Euclidean Foundation Models and Geometric Learning (NEGEL), exploring its potential benefits, including the potential benefits for advancing web-related technologies, challenges, and future directions. Workshop page: [https://hyperboliclearning.github.io/events/www2025workshop](https://hyperboliclearning.github.io/events/www2025workshop)
MindVote: When AI Meets the Wild West of Social Media Opinion
Venue: arXiv cs.SI 2025
Year: 2025

Large Language Models (LLMs) are increasingly used as scalable tools for pilot testing, predicting public opinion distributions before deploying costly surveys. To serve as effective pilot testing tools, the performance of these LLMs is typically benchmarked against their ability to reproduce the outcomes of past structured surveys. This evaluation paradigm, however, is misaligned with the dynamic, context-rich social media environments where public opinion is increasingly formed and expressed. By design, surveys strip away the social, cultural, and temporal context that shapes public opinion, and LLM benchmarks built on this paradigm inherit these critical limitations. To bridge this gap, we introduce MindVote, the first benchmark for public opinion distribution prediction grounded in authentic social media discourse. MindVote is constructed from 3,918 naturalistic polls sourced from Reddit and Weibo, spanning 23 topics and enriched with detailed annotations for platform, topical, and temporal context. Using this benchmark, we conduct a comprehensive evaluation of 15 LLMs. MindVote provides a robust, ecologically valid framework to move beyond survey-based evaluations and advance the development of more socially intelligent AI systems.