OpenRaiser/Intern-Atlas huggingface dataset:
AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities
Venue: arXiv cs.NI 2025
Year: 2025

While simulators exist for vehicular IoT nodes communicating with the Cloud through Edge nodes in a fully-simulated osmotic architecture, they often lack support for dynamic agent planning and optimisation to minimise vehicular battery consumption while ensuring fair communication times. Addressing these challenges requires extending current simulator architectures with AI algorithms for both traffic prediction and dynamic agent planning. This paper presents an extension of SimulatorOrchestrator (SO) to meet these requirements. Preliminary results over a realistic urban dataset show that utilising vehicular planning algorithms can lead to improved battery and QoS performance compared with traditional shortest path algorithms. The additional inclusion of desirability areas enabled more ambulances to be routed to their target destinations while utilising less energy to do so, compared to traditional and weighted algorithms without desirability considerations.
A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

The rapid progress of Multi-Modal Large Language Models (MLLMs) has spurred the creation of numerous benchmarks. However, conventional full-coverage Question-Answering evaluations suffer from high redundancy and low efficiency. Inspired by human interview processes, we propose a multi-to-one interview paradigm for efficient MLLM evaluation. Our framework consists of (i) a two-stage interview strategy with pre-interview and formal interview phases, (ii) dynamic adjustment of interviewer weights to ensure fairness, and (iii) an adaptive mechanism for question difficulty-level chosen. Experiments on different benchmarks show that the proposed paradigm achieves significantly higher correlation with full-coverage results than random sampling, with improvements of up to 17.6% in PLCC and 16.7% in SRCC, while reducing the number of required questions. These findings demonstrate that the proposed paradigm provides a reliable and efficient alternative for large-scale MLLM benchmarking.
Temporally Heterogeneous Graph Contrastive Learning for Multimodal Acoustic event Classification
Venue: arXiv cs.SD 2025
Year: 2025

Multimodal acoustic event classification plays a key role in audio-visual systems. Although combining audio and visual signals improves recognition, it is still difficult to align them over time and to reduce the effect of noise across modalities. Existing methods often treat audio and visual streams separately, fusing features later with contrastive or mutual information objectives. Recent advances explore multimodal graph learning, but most fail to distinguish between intra- and inter-modal temporal dependencies. To address this, we propose Temporally Heterogeneous Graph-based Contrastive Learning (THGCL). Our framework constructs a temporal graph for each event, where audio and video segments form nodes and their temporal links form edges. We introduce Gaussian processes for intra-modal smoothness, Hawkes processes for inter-modal decay, and contrastive learning to capture fine-grained relationships. Experiments on AudioSet show that THGCL achieves state-of-the-art performance.
CAD-Driven Co-Design for Flight-Ready Jet-Powered Humanoids
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a CAD-driven co-design framework for optimizing jet-powered aerial humanoid robots to execute dynamically constrained trajectories. Starting from the iRonCub-Mk3 model, a Design of Experiments (DoE) approach is used to generate 5,000 geometrically varied and mechanically feasible designs by modifying limb dimensions, jet interface geometry (e.g., angle and offset), and overall mass distribution. Each model is constructed through CAD assemblies to ensure structural validity and compatibility with simulation tools. To reduce computational cost and enable parameter sensitivity analysis, the models are clustered using K-means, with representative centroids selected for evaluation. A minimum-jerk trajectory is used to assess flight performance, providing position and velocity references for a momentum-based linearized Model Predictive Control (MPC) strategy. A multi-objective optimization is then conducted using the NSGA-II algorithm, jointly exploring the space of design centroids and MPC gain parameters. The objectives are to minimize trajectory tracking error and mechanical energy expenditure. The framework outputs a set of flight-ready humanoid configurations with validated control parameters, offering a structured method for selecting and implementing feasible aerial humanoid designs.
A Comparative Analysis of Transformer Models in Social Bot Detection
Venue: arXiv cs.LG 2025
Year: 2025

Social media has become a key medium of communication in today's society. This realisation has led to many parties employing artificial users (or bots) to mislead others into believing untruths or acting in a beneficial manner to such parties. Sophisticated text generation tools, such as large language models, have further exacerbated this issue. This paper aims to compare the effectiveness of bot detection models based on encoder and decoder transformers. Pipelines are developed to evaluate the performance of these classifiers, revealing that encoder-based classifiers demonstrate greater accuracy and robustness. However, decoder-based models showed greater adaptability through task-specific alignment, suggesting more potential for generalisation across different use cases in addition to superior observa. These findings contribute to the ongoing effort to prevent digital environments being manipulated while protecting the integrity of online discussion.
A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Venue: arXiv cs.RO 2025
Year: 2025

Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address these issues, we introduce DART, a novel framework that enhances a diffusion-based policy with affordance learning and linear temporal logic (LTL) representations to improve the learning efficiency and generalizability of articulated dexterous manipulation. Specifically, DART leverages LTL to understand task semantics and affordance learning to identify optimal interaction points. The {diffusion-based policy} then generalizes these interactions across various categories. Additionally, we exploit an optimization method based on interaction data to refine actions, overcoming the limitations of traditional diffusion policies that typically rely on offline reinforcement learning or learning from demonstrations. Experimental results demonstrate that DART outperforms most existing methods in manipulation ability, generalization performance, transfer reasoning, and robustness. For more information, visit our project website at: https://sites.google.com/view/dart0257/.
PRISM: Product Retrieval In Shopping Carts using Hybrid Matching
Venue: arXiv cs.CV 2025
Year: 2025

Compared to traditional image retrieval tasks, product retrieval in retail settings is even more challenging. Products of the same type from different brands may have highly similar visual appearances, and the query image may be taken from an angle that differs significantly from view angles of the stored catalog images. Foundational models, such as CLIP and SigLIP, often struggle to distinguish these subtle but important local differences. Pixel-wise matching methods, on the other hand, are computationally expensive and incur prohibitively high matching times. In this paper, we propose a new, hybrid method, called PRISM, for product retrieval in retail settings by leveraging the advantages of both vision-language model-based and pixel-wise matching approaches. To provide both efficiency/speed and finegrained retrieval accuracy, PRISM consists of three stages: 1) A vision-language model (SigLIP) is employed first to retrieve the top 35 most semantically similar products from a fixed gallery, thereby narrowing the search space significantly; 2) a segmentation model (YOLO-E) is applied to eliminate background clutter; 3) fine-grained pixel-level matching is performed using LightGlue across the filtered candidates. This framework enables more accurate discrimination between products with high inter-class similarity by focusing on subtle visual cues often missed by global models. Experiments performed on the ABV dataset show that our proposed PRISM outperforms the state-of-the-art image retrieval methods by 4.21% in top-1 accuracy while still remaining within the bounds of real-time processing for practical retail deployments.
UCorr: Wire Detection and Depth Estimation for Autonomous Drones
Venue: arXiv cs.CV 2025
Year: 2025

In the realm of fully autonomous drones, the accurate detection of obstacles is paramount to ensure safe navigation and prevent collisions. Among these challenges, the detection of wires stands out due to their slender profile, which poses a unique and intricate problem. To address this issue, we present an innovative solution in the form of a monocular end-to-end model for wire segmentation and depth estimation. Our approach leverages a temporal correlation layer trained on synthetic data, providing the model with the ability to effectively tackle the complex joint task of wire detection and depth estimation. We demonstrate the superiority of our proposed method over existing competitive approaches in the joint task of wire detection and depth estimation. Our results underscore the potential of our model to enhance the safety and precision of autonomous drones, shedding light on its promising applications in real-world scenarios.
Fast and Optimal Incremental Parametric Procedure for the Densest Subgraph Problem: An Experimental Study
Venue: arXiv cs.DS 2025
Year: 2025

The Densest Subgraph Problem (DSP) is widely used to identify community structures and patterns in networks such as bioinformatics and social networks. While solvable in polynomial time, traditional exact algorithms face computational and scalability limitations, leading to the adoption of faster, but non-optimal, heuristic methods. This work presents the first experimental study of the recently devised Incremental Parametric Cut (IPC) algorithm, which is an exact method for DSP and other "monotone ratio problems". Our findings demonstrate that IPC not only overcomes the limitations of previous exact approaches but also substantially outperforms leading state-of-the-art heuristics in both speed and solution quality. IPC's performance is also evaluated here for other "monotone ratio problems" related to conductance, Cheeger constant and normalized cut. For these, our experimental study on large-scale instances demonstrate exceptional computational speed. In particular, comparing IPC with the "fully parametric cut" algorithm, which is the only other efficient known optimization algorithm for such problems, demonstrate the superior performance of IPC. We provide here code and benchmarks, establishing IPC as a fast, scalable, and optimal solution framework for densest subgraph and related monotone ratio problems.
CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

While LLMs have been extensively studied on general text generation tasks, there is less research on text rewriting, a task related to general text generation, and particularly on the behavior of models on this task. In this paper we analyze what changes LLMs make in a text rewriting setting. We focus specifically on argumentative texts and their improvement, a task named Argument Improvement (ArgImp). We present CLEAR: an evaluation pipeline consisting of 57 metrics mapped to four linguistic levels: lexical, syntactic, semantic and pragmatic. This pipeline is used to examine the qualities of LLM-rewritten arguments on a broad set of argumentation corpora and compare the behavior of different LLMs on this task and analyze the behavior of different LLMs on this task in terms of linguistic levels. By taking all four linguistic levels into consideration, we find that the models perform ArgImp by shortening the texts while simultaneously increasing average word length and merging sentences. Overall we note an increase in the persuasion and coherence dimensions.
NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow
Venue: arXiv cs.AR 2025
Year: 2025

Spiking neural networks (SNNs) have emerged as a promising alternative to artificial neural networks (ANNs), offering improved energy efficiency by leveraging sparse and event-driven computation. However, existing hardware implementations of SNNs still suffer from the inherent spike sparsity and multi-timestep execution, which significantly increase latency and reduce energy efficiency. This study presents NEURAL, a novel neuromorphic architecture based on a hybrid data-event execution paradigm by decoupling sparsity-aware processing from neuron computation and using elastic first-in-first-out (FIFO). NEURAL supports on-the-fly execution of spiking QKFormer by embedding its operations within the baseline computing flow without requiring dedicated hardware units. It also integrates a novel window-to-time-to-first-spike (W2TTFS) mechanism to replace average pooling and enable full-spike execution. Furthermore, we introduce a knowledge distillation (KD)-based training framework to construct single-timestep SNN models with competitive accuracy. NEURAL is implemented on a Xilinx Virtex-7 FPGA and evaluated using ResNet-11, QKFResNet-11, and VGG-11. Experimental results demonstrate that, at the algorithm level, the VGG-11 model trained with KD improves accuracy by 3.20% on CIFAR-10 and 5.13% on CIFAR-100. At the architecture level, compared to existing SNN accelerators, NEURAL achieves a 50% reduction in resource utilization and a 1.97x improvement in energy efficiency.
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
Venue: arXiv cs.LG 2025
Year: 2025

Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a two-stage framework that integrates unsupervised pattern extracion with interpretable forecasting. (i) SIMPC segments and clusters multivariate time series, extracting recurrent patterns that are invariant to amplitude scaling and temporal distortion, even under varying window sizes. (ii) JISC-Net is a shapelet-based classifier that uses the initial part of extracted patterns as input and forecasts subsequent partial sequences for short-term directional movement. Experiments on Bitcoin and three S&P 500 equities demonstrate that our method ranks first or second in 11 out of 12 metric--dataset combinations, consistently outperforming baselines. Unlike conventional deep learning models that output buy-or-sell signals without interpretable justification, our approach enables transparent decision-making by revealing the underlying pattern structures that drive predictive outcomes.
Distributed Batch Matrix Multiplication: Trade-Offs in Download Rate, Randomness, and Privacy
Venue: arXiv cs.IT 2025
Year: 2025

We study the trade-off between communication rate and privacy for distributed batch matrix multiplication of two independent sequences of matrices $\mathbf{A}$ and $\mathbf{B}$ with uniformly distributed entries. In our setting, $\mathbf{B}$ is publicly accessible by all the servers while $\mathbf{A}$ must remain private. A user is interested in evaluating the product $\mathbf{AB}$ with the responses from the $k$ fastest servers. For a given parameter $α\in [0, 1]$, our privacy constraint must ensure that any set of $\ell$ colluding servers cannot learn more than a fraction $α$ of $\mathbf{A}$. Additionally, we study the trade-off between the amount of local randomness needed at the encoder and privacy. Finally, we establish the optimal trade-offs when the matrices are square and identify a linear relationship between information leakage and communication rate.
Balancing Sparse RNNs with Hyperparameterization Benefiting Meta-Learning
Venue: arXiv cs.LG 2025
Year: 2025

This paper develops alternative hyperparameters for specifying sparse Recurrent Neural Networks (RNNs). These hyperparameters allow for varying sparsity within the trainable weight matrices of the model while improving overall performance. This architecture enables the definition of a novel metric, hidden proportion, which seeks to balance the distribution of unknowns within the model and provides significant explanatory power of model performance. Together, the use of the varied sparsity RNN architecture combined with the hidden proportion metric generates significant performance gains while improving performance expectations on an a priori basis. This combined approach provides a path forward towards generalized meta-learning applications and model optimization based on intrinsic characteristics of the data set, including input and output dimensions.
Communication Efficient Split Learning of ViTs with Attention-based Double Compression
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes a novel communication-efficient Split Learning (SL) framework, named Attention-based Double Compression (ADC), which reduces the communication overhead required for transmitting intermediate Vision Transformers activations during the SL training process. ADC incorporates two parallel compression strategies. The first one merges samples' activations that are similar, based on the average attention score calculated in the last client layer; this strategy is class-agnostic, meaning that it can also merge samples having different classes, without losing generalization ability nor decreasing final results. The second strategy follows the first and discards the least meaningful tokens, further reducing the communication cost. Combining these strategies not only allows for sending less during the forward pass, but also the gradients are naturally compressed, allowing the whole model to be trained without additional tuning or approximations of the gradients. Simulation results demonstrate that Attention-based Double Compression outperforms state-of-the-art SL frameworks by significantly reducing communication overheads while maintaining high accuracy.
Learning in Context: Personalizing Educational Content with Large Language Models to Enhance Student Learning
Venue: arXiv cs.HC 2025
Year: 2025

Standardized, one-size-fits-all educational content often fails to connect with students' individual backgrounds and interests, leading to disengagement and a perceived lack of relevance. To address this challenge, we introduce PAGE, a novel framework that leverages large language models (LLMs) to automatically personalize educational materials by adapting them to each student's unique context, such as their major and personal interests. To validate our approach, we deployed PAGE in a semester-long intelligent tutoring system and conducted a user study to evaluate its impact in an authentic educational setting. Our findings show that students who received personalized content demonstrated significantly improved learning outcomes and reported higher levels of engagement, perceived relevance, and trust compared to those who used standardized materials. This work demonstrates the practical value of LLM-powered personalization and offers key design implications for creating more effective, engaging, and trustworthy educational experiences.
Transplant-Ready? Evaluating AI Lung Segmentation Models in Candidates with Severe Lung Disease
Venue: arXiv cs.CV 2025
Year: 2025

This study evaluates publicly available deep-learning based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung sides, and to identify limitations impacting their use in preoperative planning in lung transplantation. This retrospective study included 32 patients who underwent chest CT scans at Duke University Health System between 2017 and 2019 (total of 3,645 2D axial slices). Patients with standard axial CT scans were selected based on the presence of two or more lung pathologies of varying severity. Lung segmentation was performed using three previously developed deep learning models: Unet-R231, TotalSegmentator, MedSAM. Performance was assessed using quantitative metrics (volumetric similarity, Dice similarity coefficient, Hausdorff distance) and a qualitative measure (four-point clinical acceptability scale). Unet-R231 consistently outperformed TotalSegmentator and MedSAM in general, for different severity levels, and pathology categories (p<0.05). All models showed significant performance declines from mild to moderate-to-severe cases, particularly in volumetric similarity (p<0.05), without significant differences among lung sides or pathology types. Unet-R231 provided the most accurate automated lung segmentation among evaluated models with TotalSegmentator being a close second, though their performance declined significantly in moderate-to-severe cases, emphasizing the need for specialized model fine-tuning in severe pathology contexts.
From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
Venue: arXiv cs.AI 2025
Year: 2025

As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of XAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.
MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentation
Venue: arXiv cs.CV 2025
Year: 2025

Ensuring factual consistency and reliable reasoning remains a critical challenge for medical vision-language models. We introduce MEDFACT-R1, a two-stage framework that integrates external knowledge grounding with reinforcement learning to improve the factual medical reasoning. The first stage uses pseudo-label supervised fine-tuning (SFT) to incorporate external factual expertise; while the second stage applies Group Relative Policy Optimization (GRPO) with four tailored factual reward signals to encourage self-consistent reasoning. Across three public medical QA benchmarks, MEDFACT-R1 delivers up to 22.5% absolute improvement in factual accuracy over previous state-of-the-art methods. Ablation studies highlight the necessity of pseudo-label SFT cold start and validate the contribution of each GRPO reward, underscoring the synergy between knowledge grounding and RL-driven reasoning for trustworthy medical AI. Codes are released at https://github.com/Garfieldgengliang/MEDFACT-R1.