OpenRaiser/Intern-Atlas huggingface dataset:
Safety-Prioritized, Reinforcement Learning-Enabled Traffic Flow Optimization in a 3D City-Wide Simulation Environment
Venue: arXiv cs.LG 2025
Year: 2025

Traffic congestion and collisions represent significant economic, environmental, and social challenges worldwide. Traditional traffic management approaches have shown limited success in addressing these complex, dynamic problems. To address the current research gaps, three potential tools are developed: a comprehensive 3D city-wide simulation environment that integrates both macroscopic and microscopic traffic dynamics; a collision model; and a reinforcement learning framework with custom reward functions prioritizing safety over efficiency. Unity game engine-based simulation is used for direct collision modeling. A custom reward enabled reinforcement learning method, proximal policy optimization (PPO) model, yields substantial improvements over baseline results, reducing the number of serious collisions, number of vehicle-vehicle collisions, and total distance travelled by over 3 times the baseline values. The model also improves fuel efficiency by 39% and reduces carbon emissions by 88%. Results establish feasibility for city-wide 3D traffic simulation applications incorporating the vision-zero safety principles of the Department of Transportation, including physics-informed, adaptable, realistic collision modeling, as well as appropriate reward modeling for real-world traffic signal light control towards reducing collisions, optimizing traffic flow and reducing greenhouse emissions.
PALADIN : Robust Neural Fingerprinting for Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

The risk of misusing text-to-image generative models for malicious uses, especially due to the open-source development of such models, has become a serious concern. As a risk mitigation strategy, attributing generative models with neural fingerprinting is emerging as a popular technique. There has been a plethora of recent work that aim for addressing neural fingerprinting. A trade-off between the attribution accuracy and generation quality of such models has been studied extensively. None of the existing methods yet achieved 100% attribution accuracy. However, any model with less than cent percent accuracy is practically non-deployable. In this work, we propose an accurate method to incorporate neural fingerprinting for text-to-image diffusion models leveraging the concepts of cyclic error correcting codes from the literature of coding theory.
EdgeVidSum: Real-Time Personalized Video Summarization at the Edge
Venue: arXiv cs.CV 2025
Year: 2025

EdgeVidSum is a lightweight method that generates personalized, fast-forward summaries of long-form videos directly on edge devices. The proposed approach enables real-time video summarization while safeguarding user privacy through local data processing using innovative thumbnail-based techniques and efficient neural architectures. Unlike conventional methods that process entire videos frame by frame, the proposed method uses thumbnail containers to significantly reduce computational complexity without sacrificing semantic relevance. The framework employs a hierarchical analysis approach, where a lightweight 2D CNN model identifies user-preferred content from thumbnails and generates timestamps to create fast-forward summaries. Our interactive demo highlights the system's ability to create tailored video summaries for long-form videos, such as movies, sports events, and TV shows, based on individual user preferences. The entire computation occurs seamlessly on resource-constrained devices like Jetson Nano, demonstrating how EdgeVidSum addresses the critical challenges of computational efficiency, personalization, and privacy in modern video consumption environments.
Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
Venue: arXiv cs.NE 2025
Year: 2025

The inventory routing problem (IRP) focuses on jointly optimizing inventory and distribution operations from a supplier to retailers over multiple days. Compared to other problems from the vehicle routing family, the interrelations between inventory and routing decisions render IRP optimization more challenging and call for advanced solution techniques. A few studies have focused on developing large neighborhood search approaches for this class of problems, but this remains a research area with vast possibilities due to the challenges related to the integration of inventory and routing decisions. In this study, we advance this research area by developing a new large neighborhood search operator tailored for the IRP. Specifically, the operator optimally removes and reinserts all visits to a specific retailer while minimizing routing and inventory costs. We propose an efficient tailored dynamic programming algorithm that exploits preprocessing and acceleration strategies. The operator is used to build an effective local search routine, and included in a state-of-the-art routing algorithm, i.e., Hybrid Genetic Search (HGS). Through extensive computational experiments, we demonstrate that the resulting heuristic algorithm leads to solutions of unmatched quality up to this date, especially on large-scale benchmark instances.
Multimodal Foundation Model for Cross-Modal Retrieval and Activity Recognition Tasks
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, the widespread adoption of wearable devices has highlighted the growing importance of behavior analysis using IMU. While applications span diverse fields such as healthcare and robotics, recent studies have increasingly focused on multimodal analysis, in addition to unimodal analysis. Several studies have proposed multimodal foundation models that incorporate first-person video and text data; however, these models still fall short in providing a detailed analysis of full-body human activity. To address this limitation, we propose Activity Understanding and Representations Alignment - Multimodal Foundation Model (AURA-MFM), a foundational model integrating four modalities: third-person video, motion capture, IMU, and text. By incorporating third-person video and motion capture data, the model enables a detailed and multidimensional understanding of human activity, which first-person perspectives alone fail to capture. Additionally, a Transformer-based IMU encoder is employed to enhance the model's overall performance. Experimental evaluations on retrieval and activity recognition tasks demonstrate that our model surpasses existing methods. Notably, in the zero-shot classification for action recognition, our method achieved significantly higher performance, with an F1-score of 0.6226 and an accuracy of 0.7320, whereas the existing method recorded an F1-score of 0.0747 and an accuracy of 0.1961.
Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science
Venue: arXiv cs.DL 2025
Year: 2025

Interdisciplinary scientific research is increasingly important in knowledge production, funding policies, and academic discussions on scholarly communication. While many studies focus on interdisciplinary corpora defined a priori -- usually through keyword-based searches within assumed interdisciplinary domains -- few explore interdisciplinarity as an emergent intersection between two distinct fields. Thus, methodological proposals for building databases at the intersection of two fields of knowledge are scarce. The goal of this article is to develop and compare different strategies for defining an interdisciplinary corpus between two bodies of knowledge. As a case study, we focus on the intersection between neuroscience and computer science. To this end, we develop and compare four retrieval strategies, two of them based on keywords and two based on citation and reference patterns. Our results show that the reference-based strategy provides better retrieval, pseudorecall, and F1. While we focus on comparing strategies for the study of the intersection between the fields of neuroscience and computer science, this methodological reflection is applicable to a wide range of interdisciplinary domains.
Continual Learning in Vision-Language Models via Aligned Model Merging
Venue: arXiv cs.CV 2025
Year: 2025

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors plasticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.
Human Fall Detection using Transfer Learning-based 3D CNN
Venue: arXiv cs.CV 2025
Year: 2025

Unintentional or accidental falls are one of the significant health issues in senior persons. The population of senior persons is increasing steadily. So, there is a need for an automated fall detection monitoring system. This paper introduces a vision-based fall detection system using a pre-trained 3D CNN. Unlike 2D CNN, 3D CNN extracts not only spatial but also temporal features. The proposed model leverages the original learned weights of a 3D CNN model pre-trained on the Sports1M dataset to extract the spatio-temporal features. Only the SVM classifier was trained, which saves the time required to train the 3D CNN. Stratified shuffle five split cross-validation has been used to split the dataset into training and testing data. Extracted features from the proposed 3D CNN model were fed to an SVM classifier to classify the activity as fall or ADL. Two datasets, GMDCSA and CAUCAFall, were utilized to conduct the experiment. The source code for this work can be accessed via the following link: https://github.com/ekramalam/HFD_3DCNN.
ExtracTable: Human-in-the-Loop Transformation of Scientific Corpora into Structured Knowledge
Venue: arXiv cs.DL 2025
Year: 2025

As the volume of scientific literature grows, efficient knowledge organization becomes increasingly challenging. Traditional approaches to structuring scientific content are time-consuming and require significant domain expertise, highlighting the need for tool support. We present ExtracTable, a Human-in-the-Loop (HITL) workflow and framework that assists researchers in transforming unstructured publications into structured representations. The workflow combines large language models (LLMs) with user-defined schemas and is designed for downstream integration into knowledge graphs (KGs). Developed and evaluated in the context of the Open Research Knowledge Graph (ORKG), ExtracTable automates key steps such as document preprocessing and data extraction while ensuring user oversight through validation. In an evaluation with ORKG community participants following the Quality Improvement Paradigm (QIP), ExtracTable demonstrated high usability and practical value. Participants gave it an average System Usability Scale (SUS) score of 84.17 (A+, the highest rating). The time to progress from a research interest to literature-based insights was reduced from between 4 hours and 2 weeks to an average of 24:40 minutes. By streamlining corpus creation and structured data extraction for knowledge graph integration, ExtracTable leverages LLMs and user models to accelerate literature reviews. However, human validation remains essential to ensure quality, and future work will address improving extraction accuracy and entity linking to existing knowledge resources.
Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification
Venue: arXiv cs.LG 2025
Year: 2025

A neural ordinary differential equation (neural ODE) is a machine learning model that is commonly described as a continuous-depth generalization of a residual network (ResNet) with a single residual block, or conversely, the ResNet can be seen as the Euler discretization of the neural ODE. These two models are therefore strongly related in a way that the behaviors of either model are considered to be an approximation of the behaviors of the other. In this work, we establish a more formal relationship between these two models by bounding the approximation error between two such related models. The obtained error bound then allows us to use one of the models as a verification proxy for the other, without running the verification tools twice: if the reachable output set expanded by the error bound satisfies a safety property on one of the models, this safety property is then guaranteed to be also satisfied on the other model. This feature is fully reversible, and the initial safety verification can be run indifferently on either of the two models. This novel approach is illustrated on a numerical example of a fixed-point attractor system modeled as a neural ODE.
NetPress: Dynamically Generated LLM Benchmarks for Network Applications
Venue: arXiv cs.NI 2025
Year: 2025

Despite growing interest in domain-specific benchmarking of large language models (LLMs) and agents, current evaluations remain limited to static, small-scale datasets, especially in high-stakes tasks like network operations that demand reliability for deployments. We present NetPress, an automated benchmark generation framework for evaluating LLM agents in network applications. NetPress introduces a unified abstraction with state and action, enabling dynamic generation of diverse query sets along with corresponding ground truths. At runtime, users can specify benchmark configurations to generate millions of queries on the fly. In addition to dynamic benchmark construction, NetPress integrates with network emulators to provide realistic environment feedback, supporting comprehensive evaluation across correctness, safety, and latency. We instantiate NetPress on three representative applications, revealing interesting fine-grained differences in agent behavior that static, correctness-only benchmarks often miss. NetPress moves LLM evaluation toward realistic, scalable testing in infrastructure-centric domains, helping close the gap between benchmark performance and real-world deployment readiness. Code is available at https://github.com/Froot-NetSys/NetPress.
Chipmunk: Training-Free Acceleration of Diffusion Transformers with Dynamic Column-Sparse Deltas
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion Transformers (DiTs) have achieved state-of-the-art performance in high-quality image and video generation but incur substantial compute cost at inference. A common observation is that DiT latent noise vectors change slowly across inference steps, which suggests that the DiT compute may be redundant across steps. In this paper, we aim to speed up inference by reducing this redundancy, without additional training. We first study how activations change between steps in two state-of-the-art open-source DiTs. We find that just 5-25% of the values in attention and MLP explain 70-90% of the change in activations across steps. This finding motivates our approach, Chipmunk, which uses dynamic sparsity at inference time to recompute only the fastest-changing intermediate activations, while caching the rest. Dynamic sparsity introduces two systems challenges: (1) sparse attention and MLP operations tend to underutilize GPU tensor cores; and (2) computing dynamic sparsity patterns at runtime and caching activations both introduce overhead. To address these challenges, Chipmunk first uses a voxel-based reordering of input tokens to introduce column-wise sparsity. We implement column-sparse kernels utilizing efficient sparse gathers from global to shared GPU memory, achieving a 9.3x speedup at 93% sparsity compared to highly-optimized dense baselines. Second, Chipmunk overlaps the computation of sparsity patterns and cache updates with other parts of the computation (e.g., second layer of the MLP) to hide the extra latency. Chipmunk achieves up to 2.16x speedup on HunyuanVideo and 1.41x on FLUX.1-dev without compromising generation quality. Furthermore, we show that Chipmunk can be stacked on top of full step caching, achieving a 3.72x speedup on HunyuanVideo, a 2.67x speedup on WAN2.1, and a 2.25x speedup on FLUX.1-dev with minimal quality impact.
Dynamics and Control of Vision-Aided Multi-UAV-tethered Netted System Capturing Non-Cooperative Target
Venue: arXiv cs.RO 2025
Year: 2025

As the number of Unmanned Aerial Vehicles (UAVs) operating in low-altitude airspace continues to increase, non-cooperative targets pose growing challenges to low-altitude operations. To address this issue, this paper proposes a multi-UAV-tethered netted system as a non-lethal solution for capturing non-cooperative targets. To validate the proposed system, we develop mySim, a multibody dynamics-based UAV simulation environment that integrates high-precision physics modeling, vision-based motion tracking, and reinforcement learning-driven control strategies. In mySim, the spring-damper model is employed to simulate the dynamic behavior of the tethered net, while the dynamics of the entire system is modeled using multibody dynamics (MBD) to achieve accurate representations of system interactions. The motion of the UAVs and the target are estimated using VINS-MONO and DETR, and the system autonomously executes the capture strategy through MAPPO. Simulation results demonstrate that mySim accurately simulates dynamics and control of the system, successfully enabling the multi-UAV-tethered netted system to capture both non-propelled and maneuvering non-cooperative targets. By providing a high-precision simulation platform that integrates dynamics modeling with perception and learning-based control, mySim enables efficient testing and optimization of UAV-based control policies before real-world deployment. This approach offers significant advantages for simulating complex UAVs coordination tasks and has the potential to be applied to the design of other UAV-based systems.
Hermes: High-Performance Homomorphically Encrypted Vector Databases
Venue: arXiv cs.CR 2025
Year: 2025

Fully Homomorphic Encryption (FHE) has long promised the ability to compute over encrypted data without revealing sensitive contents -- a foundational goal for secure cloud analytics. Yet despite decades of cryptographic advances, practical integration of FHE into real-world relational databases remains elusive. This paper presents \textbf{Hermes}, the first system to enable FHE-native vector query processing inside a standard SQL engine. By leveraging the multi-slot capabilities of modern schemes, Hermes introduces a novel data model that packs multiple records per ciphertext and embeds encrypted auxiliary statistics (e.g., local sums) to support in-place updates and aggregation. To reconcile ciphertext immutability with record-level mutability, we develop new homomorphic algorithms based on slot masking, shifting, and rewriting. Hermes is implemented as native C++ loadable functions in MySQL using OpenFHE v1.2.4, comprising over 3,500 lines of code. Experiments on real-world datasets show up to 1{,}600$\times$ throughput gain in encryption and over 30$\times$ speedup in insertion compared to per-tuple baselines. Hermes brings FHE from cryptographic promise to practical reality -- realizing a long-standing vision at the intersection of databases and secure computation.
Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
Venue: arXiv cs.CE 2025
Year: 2025

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model ($i.e.$, quadratic unconstrained binary optimization), formulated by factorization machine. However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in surrogate-based active learning.
A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake
Multishot Capacity of Networks with Restricted Adversaries
Venue: arXiv cs.IT 2025
Year: 2025

We investigate adversarial network coding and decoding, focusing on the multishot regime and when the adversary is restricted to operate on a vulnerable region of the network. Errors can occur on a proper subset of the network edges and are modeled via an adversarial channel. The paper contains both bounds and capacity-achieving schemes for the Diamond Network, the Mirrored Diamond Network, and generalizations of these networks. We also initiate the study of the capacity of 3-level networks in the multishot setting by computing the multishot capacity of the Butterfly Network, considered in [IEEE Transactions on Information Theory, vol. 69, no. 6, 2023], which is a variant of the network introduced by Ahlswede, Cai, Li and Yeung in 2000.