OpenRaiser/Intern-Atlas huggingface dataset:
Training Environment for High Performance Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents Tunnel, a simple, open source, reinforcement learning training environment for high performance aircraft. It integrates the F16 3D nonlinear flight dynamics into OpenAI Gymnasium python package. The template includes primitives for boundaries, targets, adversaries and sensing capabilities that may vary depending on operational need. This offers mission planners a means to rapidly respond to evolving environments, sensor capabilities and adversaries for autonomous air combat aircraft. It offers researchers access to operationally relevant aircraft physics. Tunnel code base is accessible to anyone familiar with Gymnasium and/or those with basic python skills. This paper includes a demonstration of a week long trade study that investigated a variety of training methods, observation spaces, and threat presentations. This enables increased collaboration between researchers and mission planners which can translate to a national military advantage. As warfare becomes increasingly reliant upon automation, software agility will correlate with decision advantages. Airmen must have tools to adapt to adversaries in this context. It may take months for researchers to develop skills to customize observation, actions, tasks and training methodologies in air combat simulators. In Tunnel, this can be done in a matter of days.
A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in multimodal tasks, but visual object hallucination remains a persistent issue. It refers to scenarios where models generate inaccurate visual object-related information based on the query input, potentially leading to misinformation and concerns about safety and reliability. Previous works focus on the evaluation and mitigation of visual hallucinations, but the underlying causes have not been comprehensively investigated. In this paper, we analyze each component of LLaVA-like LVLMs -- the large language model, the vision backbone, and the projector -- to identify potential sources of error and their impact. Based on our observations, we propose methods to mitigate hallucination for each problematic component. Additionally, we developed two hallucination benchmarks: QA-VisualGenome, which emphasizes attribute and relation hallucinations, and QA-FB15k, which focuses on cognition-based hallucinations.
HAS-GPU: Efficient Hybrid Auto-scaling with Fine-grained GPU Allocation for SLO-aware Serverless Inferences
Venue: arXiv cs.DC 2025
Year: 2025

Serverless Computing (FaaS) has become a popular paradigm for deep learning inference due to the ease of deployment and pay-per-use benefits. However, current serverless inference platforms encounter the coarse-grained and static GPU resource allocation problems during scaling, which leads to high costs and Service Level Objective (SLO) violations in fluctuating workloads. Meanwhile, current platforms only support horizontal scaling for GPU inferences, thus the cold start problem further exacerbates the problems. In this paper, we propose HAS-GPU, an efficient Hybrid Auto-scaling Serverless architecture with fine-grained GPU allocation for deep learning inferences. HAS-GPU proposes an agile scheduler capable of allocating GPU Streaming Multiprocessor (SM) partitions and time quotas with arbitrary granularity and enables significant vertical quota scalability at runtime. To resolve performance uncertainty introduced by massive fine-grained resource configuration spaces, we propose the Resource-aware Performance Predictor (RaPP). Furthermore, we present an adaptive hybrid auto-scaling algorithm with both horizontal and vertical scaling to ensure inference SLOs and minimize GPU costs. The experiments demonstrated that compared to the mainstream serverless inference platform, HAS-GPU reduces function costs by an average of 10.8x with better SLO guarantees. Compared to state-of-the-art spatio-temporal GPU sharing serverless framework, HAS-GPU reduces function SLO violation by 4.8x and cost by 1.72x on average.
Drug classification based on X-ray spectroscopy combined with machine learning
Venue: arXiv cs.CV 2025
Year: 2025

The proliferation of new types of drugs necessitates the urgent development of faster and more accurate detection methods. Traditional detection methods have high requirements for instruments and environments, making the operation complex. X-ray absorption spectroscopy, a non-destructive detection technique, offers advantages such as ease of operation, penetrative observation, and strong substance differentiation capabilities, making it well-suited for application in the field of drug detection and identification. In this study, we constructed a classification model using Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Particle Swarm Optimization (PSO) to classify and identify drugs based on their X-ray spectral profiles. In the experiments, we selected 14 chemical reagents with chemical formulas similar to drugs as samples. We utilized CNN to extract features from the spectral data of these 14 chemical reagents and used the extracted features to train an SVM model. We also utilized PSO to optimize two critical initial parameters of the SVM. The experimental results demonstrate that this model achieved higher classification accuracy compared to two other common methods, with a prediction accuracy of 99.14%. Additionally, the model exhibited fast execution speed, mitigating the drawback of a drastic increase in running time and efficiency reduction that may result from the direct fusion of PSO and SVM. Therefore, the combined approach of X-ray absorption spectroscopy with CNN, PSO, and SVM provides a rapid, highly accurate, and reliable classification and identification method for the field of drug detection, holding promising prospects for widespread application.
On optimal distinguishers for Planted Clique
Venue: arXiv cs.CC 2025
Year: 2025

In a distinguishing problem, the input is a sample drawn from one of two distributions and the algorithm is tasked with identifying the source distribution. The performance of a distinguishing algorithm is measured by its advantage, i.e., its incremental probability of success over a random guess. A classic example of a distinguishing problem is the Planted Clique problem, where the input is a graph sampled from either $G(n,1/2)$ -- the standard Erdős-Rényi model, or $G(n,1/2,k)$ -- the Erdős-Rényi model with a clique planted on a random subset of $k$ vertices. The Planted Clique Hypothesis asserts that efficient algorithms cannot achieve advantage better than some absolute constant, say $1/4$, whenever $k=n^{1/2-Ω(1)}$. In this work, we aim to precisely understand the optimal distinguishing advantage achievable by efficient algorithms on Planted Clique. We show the following results under the Planted Clique hypothesis: 1. Optimality of low-degree polynomials: No efficient algorithm can beat the advantage the optimal low-degree polynomial. Concretely, this means that the advantage of any efficient algorithm is at most $(1+o(1))\cdot k^2/(\sqrtπn)$, which is optimal in light of a simple edge-counting algorithm achieving this bound. 2. Harder planted distributions: There is an efficiently sampleable distribution $\mathcal{P}^*$ supported on graphs containing $k$-cliques such that no efficient algorithm can distinguish $\mathcal{P}^*$ from $G(n,1/2)$ with advantage $n^{-d}$ for an arbitrarily large constant $d$. In other words, there exist alternate planted distributions that are much harder than $G(n,1/2,k)$. Along the way, we prove a constructive hard-core lemma for a broad class of distributions with respect to low-degree polynomials. This result is applicable much more widely beyond Planted Clique and might be of independent interest.
Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fields
Venue: arXiv cs.CV 2025
Year: 2025

Recent NeRF methods on large-scale scenes have underlined the importance of scene decomposition for scalable NeRFs. Although achieving reasonable scalability, there are several critical problems remaining unexplored, i.e., learnable decomposition, modeling scene heterogeneity, and modeling efficiency. In this paper, we introduce Switch-NeRF++, a Heterogeneous Mixture of Hash Experts (HMoHE) network that addresses these challenges within a unified framework. It is a highly scalable NeRF that learns heterogeneous decomposition and heterogeneous NeRFs efficiently for large-scale scenes in an end-to-end manner. In our framework, a gating network learns to decompose scenes and allocates 3D points to specialized NeRF experts. This gating network is co-optimized with the experts by our proposed Sparsely Gated Mixture of Experts (MoE) NeRF framework. We incorporate a hash-based gating network and distinct heterogeneous hash experts. The hash-based gating efficiently learns the decomposition of the large-scale scene. The distinct heterogeneous hash experts consist of hash grids of different resolution ranges, enabling effective learning of the heterogeneous representation of different scene parts. These design choices make our framework an end-to-end and highly scalable NeRF solution for real-world large-scale scene modeling to achieve both quality and efficiency. We evaluate our accuracy and scalability on existing large-scale NeRF datasets and a new dataset with very large-scale scenes ($>6.5km^2$) from UrbanBIS. Extensive experiments demonstrate that our approach can be easily scaled to various large-scale scenes and achieve state-of-the-art scene rendering accuracy. Furthermore, our method exhibits significant efficiency, with an 8x acceleration in training and a 16x acceleration in rendering compared to Switch-NeRF. Codes will be released at https://github.com/MiZhenxing/Switch-NeRF.
Testing Database Systems with Large Language Model Synthesized Fragments
Venue: arXiv cs.SE 2025
Year: 2025

Various automated testing approaches have been proposed for Database Management Systems (DBMSs). Many such approaches generate pairs of equivalent queries to identify bugs that cause DBMSs to compute incorrect results, and have found hundreds of bugs in mature, widely used DBMSs. Most of these approaches are based on manually written SQL generators; however, their bug-finding capabilities remain constrained by the limited set of SQL features supported by the generators. In this work, we propose ShQveL, an approach that augments existing SQL test-case generators by leveraging Large Language Models (LLMs) to synthesize SQL fragments. Our key idea is to systematically incorporate SQL features gained through automated interactions with LLMs into the SQL generators, increasing the features covered while efficiently generating test cases. Specifically, ShQveL uses SQL sketches -- SQL statements with incomplete code segments that LLMs fill -- to integrate LLM-generated content into the generator. We evaluated ShQveL on 5 DBMSs and discovered 55 unique and previously unknown bugs, 50 of which were promptly fixed after our reports.
Wide & Deep Learning for Node Classification
Venue: arXiv cs.LG 2025
Year: 2025

Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its combination of the memorization ability of generalized linear models and the generalization ability of deep models. Graph convolutional networks (GCNs) remain dominant in node classification tasks; however, recent studies have highlighted issues such as heterophily and expressiveness, which focus on graph structure while seemingly neglecting the potential role of node features. In this paper, we propose a flexible framework GCNIII, which leverages the Wide & Deep architecture and incorporates three techniques: Intersect memory, Initial residual and Identity mapping. We provide comprehensive empirical evidence showing that GCNIII can more effectively balance the trade-off between over-fitting and over-generalization on various semi- and full- supervised tasks. Additionally, we explore the use of large language models (LLMs) for node feature engineering to enhance the performance of GCNIII in cross-domain node classification tasks. Our implementation is available at https://github.com/CYCUCAS/GCNIII.
Performance Characterization of Containers in Edge Computing
Venue: arXiv cs.PF 2025
Year: 2025

Edge computing addresses critical limitations of cloud computing such as high latency and network congestion by decentralizing processing from cloud to the edge. However, the need for software replication across heterogeneous edge devices introduces dependency and portability challenges, driving the adoption of containerization technologies like Docker. While containers offer lightweight isolation and deployment advantages, they introduce new bottlenecks in edge environments, including cold-start delays, memory constraints, network throughput variability, and inefficient IO handling when interfacing with embedded peripherals. This paper presents an empirical evaluation of Docker containers on resource-constrained edge devices, using Raspberry Pi as a representative platform. We benchmark performance across diverse workloads, including microbenchmarks (CPU, memory, network profiling) and macrobenchmarks (AI inference, sensor IO operations), to quantify the overheads of containerization in real-world edge scenarios. Our testbed comprises physical Raspberry Pi nodes integrated with environmental sensors and camera modules, enabling measurements of latency, memory faults, IO throughput, and cold start delays under varying loads. Key findings reveal trade-offs between container isolation and edge-specific resource limitations, with performance degradation observed in IO heavy and latency sensitive tasks. We identify configuration optimizations to mitigate these issues, providing actionable insights for deploying containers in edge environments while meeting real time and reliability requirements. This work advances the understanding of containerized edge computing by systematically evaluating its feasibility and pitfalls on low-power embedded systems.
A Deep Learning-Aided Approach for Estimating Field Permeability Map by Fusing Well Logs, Well Tests, and Seismic Data
Venue: arXiv cs.CE 2025
Year: 2025

Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and, therefore, designing effective recovery strategies. This problem, however, remains challenging, as it requires the integration of various data sources by experts from different disciplines. Moreover, there are no sources to provide direct information about the inter-well space. In this work, a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area. This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources: well logs, well tests, and seismics. A convolutional neural network is developed to process seismic data and then incorporate it with other sources. A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct the adequate permeability map. The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia. The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells, and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data.
TeMTG: Text-Enhanced Multi-Hop Temporal Graph Modeling for Audio-Visual Video Parsing
Venue: arXiv cs.MM 2025
Year: 2025

Audio-Visual Video Parsing (AVVP) task aims to parse the event categories and occurrence times from audio and visual modalities in a given video. Existing methods usually focus on implicitly modeling audio and visual features through weak labels, without mining semantic relationships for different modalities and explicit modeling of event temporal dependencies. This makes it difficult for the model to accurately parse event information for each segment under weak supervision, especially when high similarity between segmental modal features leads to ambiguous event boundaries. Hence, we propose a multimodal optimization framework, TeMTG, that combines text enhancement and multi-hop temporal graph modeling. Specifically, we leverage pre-trained multimodal models to generate modality-specific text embeddings, and fuse them with audio-visual features to enhance the semantic representation of these features. In addition, we introduce a multi-hop temporal graph neural network, which explicitly models the local temporal relationships between segments, capturing the temporal continuity of both short-term and long-range events. Experimental results demonstrate that our proposed method achieves state-of-the-art (SOTA) performance in multiple key indicators in the LLP dataset.
Deep Representation Learning for Electronic Design Automation
Venue: arXiv cs.LG 2025
Year: 2025

Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural representation of workflow elements as images, grids, and graphs. By addressing challenges related to the increasing complexity of circuits and stringent power, performance, and area (PPA) requirements, representation learning facilitates the automatic extraction of meaningful features from complex data formats, including images, grids, and graphs. This paper examines the application of representation learning in EDA, covering foundational concepts and analyzing prior work and case studies on tasks that include timing prediction, routability analysis, and automated placement. Key techniques, including image-based methods, graph-based approaches, and hybrid multimodal solutions, are presented to illustrate the improvements provided in routing, timing, and parasitic prediction. The provided advancements demonstrate the potential of representation learning to enhance efficiency, accuracy, and scalability in current integrated circuit design flows.
DriveAgent: Multi-Agent Structured Reasoning with LLM and Multimodal Sensor Fusion for Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

We introduce DriveAgent, a novel multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion to enhance situational understanding and decision-making. DriveAgent uniquely integrates diverse sensor modalities-including camera, LiDAR, GPS, and IMU-with LLM-driven analytical processes structured across specialized agents. The framework operates through a modular agent-based pipeline comprising four principal modules: (i) a descriptive analysis agent identifying critical sensor data events based on filtered timestamps, (ii) dedicated vehicle-level analysis conducted by LiDAR and vision agents that collaboratively assess vehicle conditions and movements, (iii) environmental reasoning and causal analysis agents explaining contextual changes and their underlying mechanisms, and (iv) an urgency-aware decision-generation agent prioritizing insights and proposing timely maneuvers. This modular design empowers the LLM to effectively coordinate specialized perception and reasoning agents, delivering cohesive, interpretable insights into complex autonomous driving scenarios. Extensive experiments on challenging autonomous driving datasets demonstrate that DriveAgent is achieving superior performance on multiple metrics against baseline methods. These results validate the efficacy of the proposed LLM-driven multi-agent sensor fusion framework, underscoring its potential to substantially enhance the robustness and reliability of autonomous driving systems.
HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Developing effective approaches to generate enhanced results that align well with human visual preferences for high-quality well-lit images remains a challenge in low-light image enhancement (LLIE). In this paper, we propose a human-in-the-loop LLIE training framework that improves the visual quality of unsupervised LLIE model outputs through iterative training stages, named HiLLIE. At each stage, we introduce human guidance into the training process through efficient visual quality annotations of enhanced outputs. Subsequently, we employ a tailored image quality assessment (IQA) model to learn human visual preferences encoded in the acquired labels, which is then utilized to guide the training process of an enhancement model. With only a small amount of pairwise ranking annotations required at each stage, our approach continually improves the IQA model's capability to simulate human visual assessment of enhanced outputs, thus leading to visually appealing LLIE results. Extensive experiments demonstrate that our approach significantly improves unsupervised LLIE model performance in terms of both quantitative and qualitative performance. The code and collected ranking dataset will be available at https://github.com/LabShuHangGU/HiLLIE.
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
Venue: arXiv cs.CL 2025
Year: 2025

Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substantial computational costs and complexity. In contrast, simpler and more economical Offline RL methods remain underexplored. To address this gap, we investigate the effectiveness of Offline RL methods, specifically Direct Preference Optimization (DPO) and its length-desensitized variant LD-DPO, in enhancing the reasoning capabilities of LLMs. Extensive experiments across multiple reasoning benchmarks demonstrate that these simpler Offline RL methods substantially improve model performance, achieving an average enhancement of 3.3\%, with a particularly notable increase of 10.1\% on the challenging Arena-Hard benchmark. Furthermore, we analyze DPO's sensitivity to output length, emphasizing that increasing reasoning length should align with semantic richness, as indiscriminate lengthening may adversely affect model performance. We provide comprehensive descriptions of our data processing and training methodologies, offering empirical evidence and practical insights for developing more cost-effective Offline RL approaches.