OpenRaiser/Intern-Atlas huggingface dataset:
CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis
Venue: arXiv cs.CL 2025
Year: 2025

Cultural competence, defined as the ability to understand and adapt to multicultural contexts, is increasingly vital for large language models (LLMs) in global environments. While several cultural benchmarks exist to assess LLMs' cultural competence, current evaluations suffer from fragmented taxonomies, domain specificity, and heavy reliance on manual data annotation. To address these limitations, we introduce CultureSynth, a novel framework comprising (1) a comprehensive hierarchical multilingual cultural taxonomy covering 12 primary and 130 secondary topics, and (2) a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs. The CultureSynth-7 synthetic benchmark contains 19,360 entries and 4,149 manually verified entries across 7 languages. Evaluation of 14 prevalent LLMs of different sizes reveals clear performance stratification led by ChatGPT-4o-Latest and Qwen2.5-72B-Instruct. The results demonstrate that a 3B-parameter threshold is necessary for achieving basic cultural competence, models display varying architectural biases in knowledge processing, and significant geographic disparities exist across models. We believe that CultureSynth offers a scalable framework for developing culturally aware AI systems while reducing reliance on manual annotation\footnote{Benchmark is available at https://github.com/Eyr3/CultureSynth.}.
Robustifying Diffusion-Denoised Smoothing Against Covariate Shift
Venue: arXiv cs.LG 2025
Year: 2025

Randomized smoothing is a well-established method for achieving certified robustness against l2-adversarial perturbations. By incorporating a denoiser before the base classifier, pretrained classifiers can be seamlessly integrated into randomized smoothing without significant performance degradation. Among existing methods, Diffusion Denoised Smoothing - where a pretrained denoising diffusion model serves as the denoiser - has produced state-of-the-art results. However, we show that employing a denoising diffusion model introduces a covariate shift via misestimation of the added noise, ultimately degrading the smoothed classifier's performance. To address this issue, we propose a novel adversarial objective function focused on the added noise of the denoising diffusion model. This approach is inspired by our understanding of the origin of the covariate shift. Our goal is to train the base classifier to ensure it is robust against the covariate shift introduced by the denoiser. Our method significantly improves certified accuracy across three standard classification benchmarks - MNIST, CIFAR-10, and ImageNet - achieving new state-of-the-art performance in l2-adversarial perturbations. Our implementation is publicly available at https://github.com/ahedayat/Robustifying-DDS-Against-Covariate-Shift
Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak attacks that exploit intrinsic weaknesses in the architecture and learning paradigms of LLMs. In response, we propose \textbf{H}armful \textbf{P}rompt \textbf{La}undering (HaPLa), a novel and broadly applicable jailbreaking technique that requires only black-box access to target models. HaPLa incorporates two primary strategies: 1) \textit{abductive framing}, which instructs LLMs to infer plausible intermediate steps toward harmful activities, rather than directly responding to explicit harmful queries; and 2) \textit{symbolic encoding}, a lightweight and flexible approach designed to obfuscate harmful content, given that current LLMs remain sensitive primarily to explicit harmful keywords. Experimental results show that HaPLa achieves over 95% attack success rate on GPT-series models and 70% across all targets. Further analysis with diverse symbolic encoding rules also reveals a fundamental challenge: it remains difficult to safely tune LLMs without significantly diminishing their helpfulness in responding to benign queries.
Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
Venue: arXiv cs.CE 2025
Year: 2025

Using machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models - one global and one specific to New Zealand - are trained by learning to mimic geotechnical models at the sites of in-situ tests. Our geospatial approach has conceptual advantages in that predictions: (i) are anchored to mechanics, which encourages more sensible response and scaling across the domains of soil, site, and loading characteristics; (ii) are driven by ML, which allows more predictive information to be used, with greater potential for it to be exploited; (iii) are geostatistically updated by subsurface data, which anchors the predictions to known conditions; and (iv) are precomputed everywhere on earth for all conceivable earthquakes, which allows the models to be executed very easily, thus encouraging user adoption and evaluation. Test applications suggest that: (i) the proposed models outperform others to a statistically significant degree; (ii) the geostatistical updating further improves performance; and (iii) the anticipated advantages of region-specific models may largely be negated by the benefits of learning from larger global datasets. These models are best suited for regional-scale liquefaction hazard simulation and near-real-time response and are accompanied by variance products that convey where, and to what degree, the ML-predicted liquefaction response is influenced by local geotechnical data.
PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint
Venue: arXiv cs.LG 2025
Year: 2025

We introduce PHLoRA (Pronounced "flora"). (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients. By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time via S-LoRA, or served in scalable, industry settings using platforms like NVIDIA NIM. This approach amortizes latency overhead across requests and yields substantial cost savings. Unlike prior work that trains each adapter explicitly, our approach decouples fine-tuning from adapter generation, allowing adapter extraction from existing full-rank models or third-party checkpoints. Experiments on text, image, and video benchmarks using the Amazon Nova model family demonstrate that extracted adapters preserve high energy from the full weight delta, can be pruned safely, and yield negligible degradation in downstream task performance when re-merged. Overall, PHLoRA provides a practical path for making all existing full-rank checkpoints adapter-ready, democratizing scalable inference for all models.
Actively Learning to Coordinate in Convex Games via Approximate Correlated Equilibrium
Venue: arXiv cs.GT 2025
Year: 2025

Correlated equilibrium generalizes Nash equilibrium by allowing a central coordinator to guide players' actions through shared recommendations, similar to how routing apps guide drivers. We investigate how a coordinator can learn a correlated equilibrium in convex games where each player minimizes a convex cost function that depends on other players' actions, subject to convex constraints without knowledge of the players' cost functions. We propose a learning framework that learns an approximate correlated equilibrium by actively querying players' regrets, \emph{i.e.}, the cost saved by deviating from the coordinator's recommendations. We first show that a correlated equilibrium in convex games corresponds to a joint action distribution over an infinite joint action space that minimizes all players' regrets. To make the learning problem tractable, we introduce a heuristic that selects finitely many representative joint actions by maximizing their pairwise differences. We then apply Bayesian optimization to learn a probability distribution over the selected joint actions by querying all players' regrets. The learned distribution approximates a correlated equilibrium by minimizing players' regrets. We demonstrate the proposed approach via numerical experiments on multi-user traffic assignment games in a shared transportation network.
Vocabuild: An Accessible Augmented Tangible Interface for Gamified Vocabulary Learning of Constructing Meaning
Venue: arXiv cs.HC 2025
Year: 2025

Vocabulary acquisition in early education often relies on rote memorization and passive screen-based tools, which can fail to engage students kinesthetically and collaboratively. This paper introduces Vocabuild, an augmented tangible interface designed to transform vocabulary learning into an active, embodied, and playful experience. The system combines physical letter blocks with a projection-augmented surface. As children physically construct words with the blocks, the system provides real-time, dynamic feedback, such as displaying corresponding images and animations, thus helping them construct semantic meaning. Deployed in a classroom context, our gamified approach fosters both individual exploration and peer collaboration. A user study conducted with elementary school children demonstrates that our tangible interface leads to higher engagement, increased collaboration, and a more positive attitude towards learning compared to traditional methods. Our contributions are twofold: (1) the design and implementation of Vocabuild, a projection-augmented tangible system that transforms vocabulary learning into an embodied and collaborative activity; and (2) empirical findings from a classroom study showing that our tangible approach significantly increases engagement, peer collaboration, and positive learning attitudes compared to traditional methods.
Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images
Venue: arXiv cs.CV 2025
Year: 2025

Multi-Instance Learning (MIL) is pivotal for analyzing complex, weakly labeled datasets, such as whole-slide images (WSIs) in computational pathology, where bags comprise unordered collections of instances with sparse diagnostic relevance. Traditional MIL approaches, including early statistical methods and recent attention-based frameworks, struggle with instance redundancy and lack explicit mechanisms for discarding non-informative instances, limiting their robustness and interpretability. We propose Cluster-level Sparse MIL (csMIL), a novel framework that integrates global-local instance clustering, within-cluster attention, and cluster-level sparsity induction to address these challenges. Our csMIL first performs global clustering across all bags to establish $K$ cluster centers, followed by local clustering within each bag to assign cluster labels. Attention scores are computed within each cluster, and sparse regularization is applied to cluster weights, enabling the selective retention of diagnostically relevant clusters while discarding irrelevant ones. This approach enhances robustness to noisy instances, improves interpretability by identifying critical regions, and reduces computational complexity. Theoretical analysis demonstrates that csMIL requires $O(s log K)$ bags to recover $s$ relevant clusters, aligning with compressed sensing principles. Empirically, csMIL achieves state-of-the-art performance on two public histopathology benchmarks (CAMELYON16, TCGA-NSCLC).
Organoid Tracker: A SAM2-Powered Platform for Zero-shot Cyst Analysis in Human Kidney Organoid Videos
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in organoid models have revolutionized the study of human kidney disease mechanisms and drug discovery by enabling scalable, cost-effective research without the need for animal sacrifice. Here, we present a kidney organoid platform optimized for efficient screening in polycystic kidney disease (PKD). While these systems generate rich spatial-temporal microscopy video datasets, current manual approaches to analysis remain limited to coarse classifications (e.g., hit vs. non-hit), often missing valuable pixel-level and longitudinal information. To help overcome this bottleneck, we developed Organoid Tracker, a graphical user interface (GUI) platform designed with a modular plugin architecture, which empowers researchers to extract detailed, quantitative metrics without programming expertise. Built on the cutting-edge vision foundation model Segment Anything Model 2 (SAM2), Organoid Tracker enables zero-shot segmentation and automated analysis of spatial-temporal microscopy videos. It quantifies key metrics such as cyst formation rate, growth velocity, and morphological changes, while generating comprehensive reports. By providing an extensible, open-source framework, Organoid Tracker offers a powerful solution for improving and accelerating research in kidney development, PKD modeling, and therapeutic discovery. The platform is publicly available as open-source software at https://github.com/hrlblab/OrganoidTracker.
Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms
Venue: arXiv cs.LG 2025
Year: 2025

Audio-based equipment condition monitoring suffers from a lack of standardized methodologies for algorithm selection, hindering reproducible research. This paper addresses this gap by introducing a comprehensive framework for the systematic and statistically rigorous evaluation of machine learning models. Leveraging a rich 127-feature set across time, frequency, and time-frequency domains, our methodology is validated on both synthetic and real-world datasets. Results demonstrate that an ensemble method achieves superior performance (94.2% accuracy, 0.942 F1-score), with statistical testing confirming its significant outperformance of individual algorithms by 8-15%. Ultimately, this work provides a validated benchmarking protocol and practical guidelines for selecting robust monitoring solutions in industrial settings.
SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar
Venue: arXiv cs.GR 2025
Year: 2025

Synthetic aperture sonar (SAS) reconstruction requires recovering both the spatial distribution of acoustic scatterers and their direction-dependent response. Time-domain backprojection is the most common 3D SAS reconstruction algorithm, but it does not model directionality and can suffer from sampling limitations, aliasing, and occlusion. Prior neural volumetric methods applied to synthetic aperture sonar treat each voxel as an isotropic scattering density, not modeling anisotropic returns. We introduce SH-SAS, an implicit neural representation that expresses the complex acoustic scattering field as a set of spherical harmonic (SH) coefficients. A multi-resolution hash encoder feeds a lightweight MLP that outputs complex SH coefficients up to a specified degree L. The zeroth-order coefficient acts as an isotropic scattering field, which also serves as the density term, while higher orders compactly capture directional scattering with minimal parameter overhead. Because the model predicts the complex amplitude for any transmit-receive baseline, training is performed directly from 1-D time-of-flight signals without the need to beamform intermediate images for supervision. Across synthetic and real SAS (both in-air and underwater) benchmarks, results show that SH-SAS performs better in terms of 3D reconstruction quality and geometric metrics than previous methods.
End-to-End Visual Autonomous Parking via Control-Aided Attention
Venue: arXiv cs.CV 2025
Year: 2025

Precise parking requires an end-to-end system where perception adaptively provides policy-relevant details - especially in critical areas where fine control decisions are essential. End-to-end learning offers a unified framework by directly mapping sensor inputs to control actions, but existing approaches lack effective synergy between perception and control. Instead, we propose CAA-Policy, an end-to-end imitation learning system that allows control signal to guide the learning of visual attention via a novel Control-Aided Attention (CAA) mechanism. We train such an attention module in a self-supervised manner, using backpropagated gradients from the control outputs instead of from the training loss. This strategy encourages attention to focus on visual features that induce high variance in action outputs, rather than merely minimizing the training loss - a shift we demonstrate leads to a more robust and generalizable policy. To further strengthen the framework, CAA-Policy incorporates short-horizon waypoint prediction as an auxiliary task to improve temporal consistency of control outputs, a learnable motion prediction module to robustly track target slots over time, and a modified target tokenization scheme for more effective feature fusion. Extensive experiments in the CARLA simulator show that CAA-Policy consistently surpasses both the end-to-end learning baseline and the modular BEV segmentation + hybrid A* pipeline, achieving superior accuracy, robustness, and interpretability. Code and Collected Training datasets will be released. Code is released at https://github.com/ai4ce/CAAPolicy.
Seeing is Not Understanding: A Benchmark on Perception-Cognition Disparities in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid advancement of Multimodal Large Language Models (MLLMs), they have demonstrated exceptional capabilities across a variety of vision-language tasks. However, current evaluation benchmarks predominantly focus on objective visual question answering or captioning, inadequately assessing the models' ability to understand complex and subjective human emotions. To bridge this gap, we introduce EmoBench-Reddit, a novel, hierarchical benchmark for multimodal emotion understanding. The dataset comprises 350 meticulously curated samples from the social media platform Reddit, each containing an image, associated user-provided text, and an emotion category (sad, humor, sarcasm, happy) confirmed by user flairs. We designed a hierarchical task framework that progresses from basic perception to advanced cognition, with each data point featuring six multiple-choice questions and one open-ended question of increasing difficulty. Perception tasks evaluate the model's ability to identify basic visual elements (e.g., colors, objects), while cognition tasks require scene reasoning, intent understanding, and deep empathy integrating textual context. We ensured annotation quality through a combination of AI assistance (Claude 4) and manual verification.We conducted a comprehensive evaluation of nine leading MLLMs, including GPT-5, Gemini-2.5-pro, and GPT-4o, on EmoBench-Reddit.
FEWT: Improving Humanoid Robot Perception with Frequency-Enhanced Wavelet-based Transformers
Venue: arXiv cs.RO 2025
Year: 2025

The embodied intelligence bridges the physical world and information space. As its typical physical embodiment, humanoid robots have shown great promise through robot learning algorithms in recent years. In this study, a hardware platform, including humanoid robot and exoskeleton-style teleoperation cabin, was developed to realize intuitive remote manipulation and efficient collection of anthropomorphic action data. To improve the perception representation of humanoid robot, an imitation learning framework, termed Frequency-Enhanced Wavelet-based Transformer (FEWT), was proposed, which consists of two primary modules: Frequency-Enhanced Efficient Multi-Scale Attention (FE-EMA) and Time-Series Discrete Wavelet Transform (TS-DWT). By combining multi-scale wavelet decomposition with the residual network, FE-EMA can dynamically fuse features from both cross-spatial and frequency-domain. This fusion is able to capture feature information across various scales effectively, thereby enhancing model robustness. Experimental performance demonstrates that FEWT improves the success rate of the state-of-the-art algorithm (Action Chunking with Transformers, ACT baseline) by up to 30% in simulation and by 6-12% in real-world.
Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers
Venue: arXiv cs.CR 2025
Year: 2025

Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental vulnerability in their design: can an official, unmodified compiler alter a model's semantics during compilation and introduce hidden backdoors? We study both adversarial and natural settings. In the adversarial case, we craft benign models where triggers have no effect pre-compilation but become effective backdoors after compilation. Tested on six models, three commercial compilers, and two hardware platforms, our attack yields 100% success on triggered inputs while preserving normal accuracy and remaining undetected by state-of-the-art detectors. The attack generalizes across compilers, hardware, and floating-point settings. In the natural setting, we analyze the top 100 HuggingFace models (including one with 220M+ downloads) and find natural triggers in 31 models. This shows that compilers can introduce risks even without adversarial manipulation. Our results reveal an overlooked threat: unmodified DL compilers can silently alter model semantics. To our knowledge, this is the first work to expose inherent security risks in DL compiler design, opening a new direction for secure and trustworthy ML.
DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
Venue: arXiv cs.CR 2025
Year: 2025

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain robustness in real-world settings, particularly when facing code obfuscation and adversarial examples (AEs). Deep multimodal learning has emerged as a promising solution, leveraging the strengths of multiple feature types to enhance robustness and generalization. However, a systematic investigation of multimodal fusion for both accuracy and resilience remains underexplored. In this study, we propose DMLDroid, an Android malware detection based on multimodal fusion that leverages three different representations of malware features, including permissions & intents (tabular-based), DEX file representations (image-based), and API calls (graph-derived sequence-based). We conduct exhaustive experiments independently on each feature, as well as in combination, using different fusion strategies. Experimental results on the CICMalDroid 2020 dataset demonstrate that our multimodal approach with the dynamic weighted fusion mechanism achieves high performance, reaching 97.98% accuracy and 98.67% F1-score on original malware detection. Notably, the proposed method maintains strong robustness, sustaining over 98% accuracy and 98% F1-score under both obfuscation and adversarial attack scenarios. Our findings highlight the benefits of multimodal fusion in improving both detection accuracy and robustness against evolving Android malware threats.