OpenRaiser/Intern-Atlas huggingface dataset:
Functional ISS-Driven Verification of Superscalar RISC-V Processors
Venue: arXiv cs.AR 2025
Year: 2025

A time-efficient and comprehensive verification is a fundamental part of the design process for modern computing platforms, and it becomes ever more important and critical to optimize as the latter get ever more complex. SupeRFIVe is a methodology for the functional verification of superscalar processors that leverages an instruction set simulator to validate their correctness according to a simulation-based approach, interfacing a testbench for the design under test with the instruction set simulator by means of socket communication. We demonstrate the effectiveness of the SupeRFIVe methodology by applying it to verify the functional correctness of a RISC-V dual-issue superscalar CPU, leveraging the state-of-the-art RISC-V instruction set simulator Spike and executing a set of benchmark applications from the open literature.
AQUA: Network-Accelerated Memory Offloading for LLMs in Scale-Up GPU Domains
Venue: arXiv cs.DC 2025
Year: 2025

Inference on large-language models (LLMs) is constrained by GPU memory capacity. A sudden increase in the number of inference requests to a cloud-hosted LLM can deplete GPU memory, leading to contention between multiple prompts for limited resources. Modern LLM serving engines deal with the challenge of limited GPU memory using admission control, which causes them to be unresponsive during request bursts. We propose that preemptive scheduling of prompts in time slices is essential for ensuring responsive LLM inference, especially under conditions of high load and limited GPU memory. However, preempting prompt inference incurs a high paging overhead, which reduces inference throughput. We present Aqua, a GPU memory management framework that significantly reduces the overhead of paging inference state, achieving both responsive and high-throughput inference even under bursty request patterns. We evaluate Aqua by hosting several state-of-the-art large generative ML models of different modalities on servers with 8 Nvidia H100 80G GPUs. Aqua improves the responsiveness of LLM inference by 20X compared to the state-of-the-art and improves LLM inference throughput over a single long prompt by 4X.
ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images
Venue: arXiv cs.CV 2025
Year: 2025

With the development of eXtended Reality (XR), photo capturing and display technology based on head-mounted displays (HMDs) have experienced significant advancements and gained considerable attention. Egocentric spatial images and videos are emerging as a compelling form of stereoscopic XR content. The assessment for the Quality of Experience (QoE) of XR content is important to ensure a high-quality viewing experience. Different from traditional 2D images, egocentric spatial images present challenges for perceptual quality assessment due to their special shooting, processing methods, and stereoscopic characteristics. However, the corresponding image quality assessment (IQA) research for egocentric spatial images is still lacking. In this paper, we establish the Egocentric Spatial Images Quality Assessment Database (ESIQAD), the first IQA database dedicated for egocentric spatial images as far as we know. Our ESIQAD includes 500 egocentric spatial images and the corresponding mean opinion scores (MOSs) under three display modes, including 2D display, 3D-window display, and 3D-immersive display. Based on our ESIQAD, we propose a novel mamba2-based multi-stage feature fusion model, termed ESIQAnet, which predicts the perceptual quality of egocentric spatial images under the three display modes. Specifically, we first extract features from multiple visual state space duality (VSSD) blocks, then apply cross attention to fuse binocular view information and use transposed attention to further refine the features. The multi-stage features are finally concatenated and fed into a quality regression network to predict the quality score. Extensive experimental results demonstrate that the ESIQAnet outperforms 22 state-of-the-art IQA models on the ESIQAD under all three display modes. The database and code are available at https://github.com/IntMeGroup/ESIQA.
A Performance Analysis of Lexicase-Based and Traditional Selection Methods in GP for Symbolic Regression
Venue: arXiv cs.NE 2025
Year: 2025

In recent years, several new lexicase-based selection variants have emerged due to the success of standard lexicase selection in various application domains. For symbolic regression problems, variants that use an epsilon-threshold or batches of training cases, among others, have led to performance improvements. Lately, especially variants that combine lexicase selection and down-sampling strategies have received a lot of attention. This paper evaluates the most relevant lexicase-based selection methods as well as traditional selection methods in combination with different down-sampling strategies on a wide range of symbolic regression problems. In contrast to most work, we not only compare the methods over a given evaluation budget, but also over a given time budget as time is usually limited in practice. We find that for a given evaluation budget, epsilon-lexicase selection in combination with a down-sampling strategy outperforms all other methods. If the given running time is very short, lexicase variants using batches of training cases perform best. Further, we find that the combination of tournament selection with informed down-sampling performs well in all studied settings.
Mobility-Aware Federated Self-supervised Learning in Vehicular Network
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU). This enables FL to handle scenarios with sensitive or widely distributed data. However, in these fields, it is well known that the labeling costs can be a significant expense, and models relying on labels are not suitable for these rapidly evolving fields especially in vehicular networks, or mobile internet of things (MIoT), where new data emerges constantly. To handle this issue, the self-supervised learning paves the way for training without labels. Additionally, for vehicles with high velocity, owing to blurred images, simple aggregation not only impacts the accuracy of the aggregated model but also reduces the convergence speed of FL. This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment. Simulation results demonstrate that the proposed algorithm exhibits fast and stable convergence.
Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving
Venue: arXiv cs.AI 2025
Year: 2025

Current research on trajectory prediction primarily relies on data collected by onboard sensors of an ego vehicle. With the rapid advancement in connected technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, valuable information from alternate views becomes accessible via wireless networks. The integration of information from alternative views has the potential to overcome the inherent limitations associated with a single viewpoint, such as occlusions and limited field of view. In this work, we introduce V2INet, a novel trajectory prediction framework designed to model multi-view data by extending existing single-view models. Unlike previous approaches where the multi-view data is manually fused or formulated as a separate training stage, our model supports end-to-end training, enhancing both flexibility and performance. Moreover, the predicted multimodal trajectories are calibrated by a post-hoc conformal prediction module to get valid and efficient confidence regions. We evaluated the entire framework using the real-world V2I dataset V2X-Seq. Our results demonstrate superior performance in terms of Final Displacement Error (FDE) and Miss Rate (MR) using a single GPU. The code is publicly available at: https://github.com/xichennn/V2I_trajectory_prediction.
Quantitative Group Testing and Pooled Data in the Linear Regime with Sublinear Tests
Venue: arXiv cs.IT 2025
Year: 2025

In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items in the pool belonging to each category. A prominent special case is quantitative group testing (QGT), which is the case of pooled data with two categories. We consider these problems in the non-adaptive and linear regime, where the fraction of items in each category is of constant order. We propose a scheme with a spatially coupled Bernoulli test matrix and an efficient approximate message passing (AMP) algorithm for recovery. We rigorously characterize its asymptotic performance in both the noiseless and noisy settings, and prove that in the noiseless case, the AMP algorithm achieves almost-exact recovery with a number of tests sublinear in the total number of items $p$. Although there exist other efficient schemes for noiseless QGT and pooled data that achieve recovery with order-optimal sample complexity ($Θ(\frac{p}{\log p})$ tests), there are no guarantees on their performance in the presence of noise, even at low noise-levels. In comparison, our scheme achieves recovery in the noiseless case with a number of tests sublinear in $p$, and its performance degrades gracefully in the presence of noise. Numerical simulations illustrate the benefits of the spatially coupled scheme at finite dimensions, showing that it outperforms i.i.d. test designs as well as other recovery algorithms based on convex programming.
Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion
Venue: arXiv cs.CV 2025
Year: 2025

Recent years have seen a tremendous improvement in the quality of video generation and editing approaches. While several techniques focus on editing appearance, few address motion. Current approaches using text, trajectories, or bounding boxes are limited to simple motions, so we specify motions with a single motion reference video instead. We further propose to use a pre-trained image-to-video model rather than a text-to-video model. This approach allows us to preserve the exact appearance and position of a target object or scene and helps disentangle appearance from motion. Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input, while the text/image embedding injected via cross-attention predominantly controls motion. We thus represent motion using text/image embedding tokens. By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity. Once optimized on the motion reference video, this embedding can be applied to various target images to generate videos with semantically similar motions. Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks such as full-body and face reenactment, as well as controlling the motion of inanimate objects and the camera. We empirically demonstrate the effectiveness of our method in the semantic video motion transfer task, significantly outperforming existing methods in this context. Project website: https://mkansy.github.io/reenact-anything/
A new approach for encoding code and assisting code understanding
Venue: arXiv cs.AI 2025
Year: 2025

Some companies (e.g., Microsoft Research and Google DeepMind) have discovered some of the limitations of GPTs' autoregressive paradigm next-word prediction, manifested in the model's lack of planning, working memory, backtracking, and reasoning skills. GPTs rely on a local and greedy process of generating the next word, without a global understanding of the task or the output. We have confirmed the above limitations through specialized empirical studies of code comprehension. Although GPT-4 is good at producing fluent and coherent text, it cannot handle complex logic and generate new code that hasn't been seen, and it relies too much on the formatting of the prompt to generate the correct code. We propose a new paradigm for code understanding that goes beyond the next-word prediction paradigm, inspired by the successful application of diffusion techniques to image generation (Dalle-2, Sora) and protein structure generation (AlphaFold-3), which have no autoregressive constraints. Instead of encoding the code in a form that mimics natural language, we encode the code as a heterogeneous image paradigm with a memory of global information that mimics both images and protein structures. We then refer to Sora's CLIP upstream text-to-image encoder model to design a text-to-code encoder model that can be applied to various downstream code understanding tasks. The model learns the global understanding of code under the new paradigm heterogeneous image, connects the encoding space of text and code, and encodes the input of text into the vector of code most similar to it. Using self-supervised comparative learning on 456,360 text-code pairs, the model achieved a zero-shot prediction of new data. This work is the basis for future work on code generation using diffusion techniques under a new paradigm to avoid autoregressive limitations.
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models
Venue: arXiv cs.CR 2025
Year: 2025

Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to jailbreaking attacks, where carefully crafted prompts bypass safety mechanisms to produce unsafe content. While researchers have developed various jailbreak attacks to expose this risk, these methods face significant limitations, including impractical access requirements, easily detectable unnatural prompts, restricted search spaces, and high query demands on the target system. In this paper, we propose JailFuzzer, a novel fuzzing framework driven by large language model (LLM) agents, designed to efficiently generate natural and semantically meaningful jailbreak prompts in a black-box setting. Specifically, JailFuzzer employs fuzz-testing principles with three components: a seed pool for initial and jailbreak prompts, a guided mutation engine for generating meaningful variations, and an oracle function to evaluate jailbreak success. Furthermore, we construct the guided mutation engine and oracle function by LLM-based agents, which further ensures efficiency and adaptability in black-box settings. Extensive experiments demonstrate that JailFuzzer has significant advantages in jailbreaking T2I models. It generates natural and semantically coherent prompts, reducing the likelihood of detection by traditional defenses. Additionally, it achieves a high success rate in jailbreak attacks with minimal query overhead, outperforming existing methods across all key metrics. This study underscores the need for stronger safety mechanisms in generative models and provides a foundation for future research on defending against sophisticated jailbreaking attacks. JailFuzzer is open-source and available at this repository: https://github.com/YingkaiD/JailFuzzer.
Occupation-aware planning method for robotic monitoring missions in dynamic environments
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a method for robotic monitoring missions in the presence of moving obstacles. Although the scenario map is known, the robot lacks information about the movement of dynamic obstacles during the monitoring mission. Numerous local planners have been developed in recent years for navigating highly dynamic environments. However, the absence of a global planner for these environments can result in unavoidable collisions or the inability to successfully complete missions in densely populated areas, such as a scenario monitoring in our case. This work addresses the development and evaluation of a global planner, $MADA$ (Monitoring Avoiding Dynamic Areas), aimed at enhancing the deployment of robots in such challenging conditions. The robot plans and executes the mission using the proposed two-step approach. The first step involves selecting the observation goal based on the environment's distribution and estimated monitoring costs. In the second step, the robot identifies areas with moving obstacles and obtains paths avoiding densely occupied dynamic regions based on their occupation. Quantitative and qualitative results based on simulations and on real-world experimentation, confirm that the proposed method allows the robot to effectively monitor most of the environment while avoiding densely occupied dynamic areas.
A Survey of Mamba
Venue: arXiv cs.LG 2025
Year: 2025

As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions of parameters, becoming a cornerstone in deep learning. Despite the impressive achievements, Transformers still face inherent limitations, particularly the time-consuming inference resulting from the quadratic computation complexity of attention calculation. Recently, a novel architecture named Mamba, drawing inspiration from classical state space models (SSMs), has emerged as a promising alternative for building foundation models, delivering comparable modeling abilities to Transformers while preserving near-linear scalability concerning sequence length. This has sparked an increasing number of studies actively exploring Mamba's potential to achieve impressive performance across diverse domains. Given such rapid evolution, there is a critical need for a systematic review that consolidates existing Mamba-empowered models, offering a comprehensive understanding of this emerging model architecture. In this survey, we therefore conduct an in-depth investigation of recent Mamba-associated studies, covering three main aspects: the advancements of Mamba-based models, the techniques of adapting Mamba to diverse data, and the applications where Mamba can excel. Specifically, we first review the foundational knowledge of various representative deep learning models and the details of Mamba-1&2 as preliminaries. Then, to showcase the significance of Mamba for AI, we comprehensively review the related studies focusing on Mamba models' architecture design, data adaptability, and applications. Finally, we present a discussion of current limitations and explore various promising research directions to provide deeper insights for future investigations.
Vertiport Terminal Scheduling and Throughput Analysis for Multiple Surface Directions
Venue: arXiv cs.ET 2025
Year: 2025

Vertical Take-Off and Landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of Urban Air Mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refueling or charging, and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimize vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilize multiple surface directions for climbing and approach. This flexibility necessitates specialized scheduling methods. We propose a Mixed Integer Linear Program (MILP) formulation to holistically optimize vertiminal operations, including taxiing, climbing (or approach) using multiple directions, and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the TLOF pad system, taxiway system, and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. We also validate our framework through a case study using a well-established vertiminal topology from the literature. Our MILP can be used to find the optimal configuration of vertiminal. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.
PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixing
Venue: arXiv cs.CV 2025
Year: 2025

Multiple instance learning (MIL) has emerged as a powerful framework for weakly supervised whole slide image (WSI) classification, enabling slide-level predictions without requiring detailed patch-level annotations. Despite its success, a critical limitation of current MIL methods lies in the underutilization of pre-training for the MIL aggregator. Most existing approaches initialize the aggregator randomly and train it from scratch, making performance highly sensitive to the quantity of labeled WSIs and ignoring the abundance of unlabeled WSIs commonly available in clinical settings. To address this, we propose PreMix, a novel framework that leverages a non-contrastive pre-training method, Barlow Twins, augmented with the Slide Mixing approach to generate additional positive pairs and enhance feature learning, particularly under limited labeled WSI conditions. Fine-tuning with Mixup and Manifold Mixup further enhances robustness by effectively handling the diverse sizes of gigapixel WSIs. Experimental results demonstrate that integrating PreMix as a plug-in module into HIPT yields an average F1 improvement of 4.7% over the baseline HIPT across various WSI training sizes and datasets. These findings underscore its potential to advance WSI classification with limited labeled data and its applicability to real-world histopathology practices. The code is available at https://github.com/bryanwong17/PreMix
High-Throughput Phenotyping of Clinical Text Using Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text.