OpenRaiser/Intern-Atlas huggingface dataset:
Perception-Enhanced Multitask Multimodal Semantic Communication for UAV-Assisted Integrated Sensing and Communication System
Venue: arXiv cs.IT 2025
Year: 2025

Recent advances in integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) have enabled their widespread deployment in critical applications such as emergency management. This paper investigates the challenge of efficient multitask multimodal data communication in UAV-assisted ISAC systems, in the considered system model, hyperspectral (HSI) and LiDAR data are collected by UAV-mounted sensors for both target classification and data reconstruction at the terrestrial BS. The limited channel capacity and complex environmental conditions pose significant challenges to effective air-to-ground communication. To tackle this issue, we propose a perception-enhanced multitask multimodal semantic communication (PE-MMSC) system that strategically leverages the onboard computational and sensing capabilities of UAVs. In particular, we first propose a robust multimodal feature fusion method that adaptively combines HSI and LiDAR semantics while considering channel noise and task requirements. Then the method introduces a perception-enhanced (PE) module incorporating attention mechanisms to perform coarse classification on UAV side, thereby optimizing the attention-based multimodal fusion and transmission. Experimental results demonstrate that the proposed PE-MMSC system achieves 5\%--10\% higher target classification accuracy compared to conventional systems without PE module, while maintaining comparable data reconstruction quality with acceptable computational overheads.
QINCODEC: Neural Audio Compression with Implicit Neural Codebooks
Venue: arXiv cs.SD 2025
Year: 2025

Neural audio codecs, neural networks which compress a waveform into discrete tokens, play a crucial role in the recent development of audio generative models. State-of-the-art codecs rely on the end-to-end training of an autoencoder and a quantization bottleneck. However, this approach restricts the choice of the quantization methods as it requires to define how gradients propagate through the quantizer and how to update the quantization parameters online. In this work, we revisit the common practice of joint training and propose to quantize the latent representations of a pre-trained autoencoder offline, followed by an optional finetuning of the decoder to mitigate degradation from quantization. This strategy allows to consider any off-the-shelf quantizer, especially state-of-the-art trainable quantizers with implicit neural codebooks such as QINCO2. We demonstrate that with the latter, our proposed codec termed QINCODEC, is competitive with baseline codecs while being notably simpler to train. Finally, our approach provides a general framework that amortizes the cost of autoencoder pretraining, and enables more flexible codec design.
Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in Large Language Models (LLMs) have introduced Reasoning Large Language Models (RLLMs), which employ extended thinking processes with reflection and self-correction capabilities, demonstrating the effectiveness of test-time scaling. RLLMs exhibit innate Chain-of-Thought (CoT) reasoning capability obtained from training, leading to a natural question: "Is CoT prompting, a popular In-Context Learning (ICL) method for chat LLMs, necessary to enhance the reasoning capability of RLLMs?" In this work, we present the first comprehensive analysis of the impacts of Zero-shot CoT and Few-shot CoT on RLLMs across mathematical reasoning tasks. We examine models ranging from 1.5B to 32B parameters, finding that contrary to concerns, CoT prompting significantly enhances RLLMs' performance in most scenarios. Our results reveal distinct patterns: large-capacity models show minimal improvement on simple tasks but substantial gains on complex problems, while smaller models exhibit the opposite behavior. Further analysis demonstrates that CoT prompting effectively controls the distribution of the numbers of thinking tokens and reasoning steps, reducing excessive reflections by approximately 90% in some cases. Moreover, attention logits analysis reveals the RLLMs' overfitting to reflection-related words, which is mitigated by external CoT guidance. Notably, our experiments indicate that for RLLMs, one-shot CoT consistently yields superior performance compared to Few-shot CoT approaches. Our findings provide important insights for optimizing RLLMs' performance through appropriate prompting strategies.
Burst Image Super-Resolution with Mamba
Venue: arXiv cs.CV 2025
Year: 2025

Burst image super-resolution (BISR) aims to enhance the resolution of a keyframe by leveraging information from multiple low-resolution images captured in quick succession. In the deep learning era, BISR methods have evolved from fully convolutional networks to transformer-based architectures, which, despite their effectiveness, suffer from the quadratic complexity of self-attention. We see Mamba as the next natural step in the evolution of this field, offering a comparable global receptive field and selective information routing with only linear time complexity. In this work, we introduce BurstMamba, a Mamba-based architecture for BISR. Our approach decouples the task into two specialized branches: a spatial module for keyframe super-resolution and a temporal module for subpixel prior extraction, striking a balance between computational efficiency and burst information integration. To further enhance burst processing with Mamba, we propose two novel strategies: (i) optical flow-based serialization, which aligns burst sequences only during state updates to preserve subpixel details, and (ii) a wavelet-based reparameterization of the state-space update rules, prioritizing high-frequency features for improved burst-to-keyframe information passing. Our framework achieves SOTA performance on public benchmarks of SyntheticSR, RealBSR-RGB, and RealBSR-RAW.
An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators
Venue: arXiv cs.LG 2025
Year: 2025

Transformer-based models have become the \textit{de facto} backbone across many fields, such as computer vision and natural language processing. However, as these models scale in size, external memory access (EMA) for weight and activations becomes a critical bottleneck due to its significantly higher energy consumption compared to internal computations. While most prior work has focused on optimizing the self-attention mechanism, little attention has been given to optimizing data transfer during linear projections, where EMA costs are equally important. In this paper, we propose the Tile-based Adaptive Stationary (TAS) scheme that selects the input or weight stationary in a tile granularity, based on the input sequence length. Our experimental results demonstrate that TAS can significantly reduce EMA by more than 97\% compared to traditional stationary schemes, while being compatible with various attention optimization techniques and hardware accelerators.
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data
Venue: arXiv cs.LG 2025
Year: 2025

This work addresses the problem of graph learning from data following a Gaussian Graphical Model (GGM) with a time-varying mean. Graphical Lasso (GL), the standard method for estimating sparse precision matrices, assumes that the observed data follows a zero-mean Gaussian distribution. However, this assumption is often violated in real-world scenarios where the mean evolves over time due to external influences, trends, or regime shifts. When the mean is not properly accounted for, applying GL directly can lead to estimating a biased precision matrix, hence hindering the graph learning task. To overcome this limitation, we propose Graphical Lasso with Adaptive Targeted Adaptive Importance Sampling (GL-ATAIS), an iterative method that jointly estimates the time-varying mean and the precision matrix. Our approach integrates Bayesian inference with frequentist estimation, leveraging importance sampling to obtain an estimate of the mean while using a regularized maximum likelihood estimator to infer the precision matrix. By iteratively refining both estimates, GL-ATAIS mitigates the bias introduced by time-varying means, leading to more accurate graph recovery. Our numerical evaluation demonstrates the impact of properly accounting for time-dependent means and highlights the advantages of GL-ATAIS over standard GL in recovering the true graph structure.
RGB-Th-Bench: A Dense benchmark for Visual-Thermal Understanding of Vision Language Models
Venue: arXiv cs.CV 2025
Year: 2025

We introduce RGB-Th-Bench, the first benchmark designed to evaluate the ability of Vision-Language Models (VLMs) to comprehend RGB-Thermal image pairs. While VLMs have demonstrated remarkable progress in visual reasoning and multimodal understanding, their evaluation has been predominantly limited to RGB-based benchmarks, leaving a critical gap in assessing their capabilities in infrared vision tasks. Existing visible-infrared datasets are either task-specific or lack high-quality annotations necessary for rigorous model evaluation. To address these limitations, RGB-Th-Bench provides a comprehensive evaluation framework covering 14 distinct skill dimensions, with a total of 1,600+ expert-annotated Yes/No questions. The benchmark employs two accuracy metrics: a standard question-level accuracy and a stricter skill-level accuracy, which evaluates model robustness across multiple questions within each skill dimension. This design ensures a thorough assessment of model performance, including resilience to adversarial and hallucinated responses. We conduct extensive evaluations on 19 state-of-the-art VLMs, revealing significant performance gaps in RGB-Thermal understanding. Our results show that even the strongest models struggle with thermal image comprehension, with performance heavily constrained by their RGB-based capabilities. Additionally, the lack of large-scale application-specific and expert-annotated thermal-caption-pair datasets in pre-training is an important reason of the observed performance gap. RGB-Th-Bench highlights the urgent need for further advancements in multimodal learning to bridge the gap between visible and thermal image understanding. The dataset is available through this link, and the evaluation code will also be made publicly available.
BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction
Venue: arXiv cs.CV 2025
Year: 2025

Manual digitization of bibliographic metadata is time consuming and labor intensive, especially for historical and real-world archives with highly variable formatting across documents. Despite advances in machine learning, the absence of dedicated datasets for metadata extraction hinders automation. To address this gap, we introduce BiblioPage, a dataset of scanned title pages annotated with structured bibliographic metadata. The dataset consists of approximately 2,000 monograph title pages collected from 14 Czech libraries, spanning a wide range of publication periods, typographic styles, and layout structures. Each title page is annotated with 16 bibliographic attributes, including title, contributors, and publication metadata, along with precise positional information in the form of bounding boxes. To extract structured information from this dataset, we valuated object detection models such as YOLO and DETR combined with transformer-based OCR, achieving a maximum mAP of 52 and an F1 score of 59. Additionally, we assess the performance of various visual large language models, including LlamA 3.2-Vision and GPT-4o, with the best model reaching an F1 score of 67. BiblioPage serves as a real-world benchmark for bibliographic metadata extraction, contributing to document understanding, document question answering, and document information extraction. Dataset and evaluation scripts are availible at: https://github.com/DCGM/biblio-dataset
A multitask transformer to sign language translation using motion gesture primitives
Venue: arXiv cs.CL 2025
Year: 2025

The absence of effective communication the deaf population represents the main social gap in this community. Furthermore, the sign language, main deaf communication tool, is unlettered, i.e., there is no formal written representation. In consequence, main challenge today is the automatic translation among spatiotemporal sign representation and natural text language. Recent approaches are based on encoder-decoder architectures, where the most relevant strategies integrate attention modules to enhance non-linear correspondences, besides, many of these approximations require complex training and architectural schemes to achieve reasonable predictions, because of the absence of intermediate text projections. However, they are still limited by the redundant background information of the video sequences. This work introduces a multitask transformer architecture that includes a gloss learning representation to achieve a more suitable translation. The proposed approach also includes a dense motion representation that enhances gestures and includes kinematic information, a key component in sign language. From this representation it is possible to avoid background information and exploit the geometry of the signs, in addition, it includes spatiotemporal representations that facilitate the alignment between gestures and glosses as an intermediate textual representation. The proposed approach outperforms the state-of-the-art evaluated on the CoL-SLTD dataset, achieving a BLEU-4 of 72,64% in split 1, and a BLEU-4 of 14,64% in split 2. Additionally, the strategy was validated on the RWTH-PHOENIX-Weather 2014 T dataset, achieving a competitive BLEU-4 of 11,58%.
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms
Venue: arXiv cs.SD 2025
Year: 2025

This paper explores the application of Convolutional Neural Networks CNNs for classifying emotions in speech through Mel Spectrogram representations of audio files. Traditional methods such as Gaussian Mixture Models and Hidden Markov Models have proven insufficient for practical deployment, prompting a shift towards deep learning techniques. By transforming audio data into a visual format, the CNN model autonomously learns to identify intricate patterns, enhancing classification accuracy. The developed model is integrated into a user-friendly graphical interface, facilitating realtime predictions and potential applications in educational environments. The study aims to advance the understanding of deep learning in speech emotion recognition, assess the models feasibility, and contribute to the integration of technology in learning contexts
Leveraging Cognitive States for Adaptive Scaffolding of Understanding in Explanatory Tasks in HRI
Venue: arXiv cs.HC 2025
Year: 2025

Understanding how scaffolding strategies influence human understanding in human-robot interaction is important for developing effective assistive systems. This empirical study investigates linguistic scaffolding strategies based on negation as an important means that de-biases the user from potential errors but increases processing costs and hesitations as a means to ameliorate processing costs. In an adaptive strategy, the user state with respect to the current state of understanding and processing capacity was estimated via a scoring scheme based on task performance, prior scaffolding strategy, and current eye gaze behavior. In the study, the adaptive strategy of providing negations and hesitations was compared with a non-adaptive strategy of providing only affirmations. The adaptive scaffolding strategy was generated using the computational model SHIFT. Our findings indicate that using adaptive scaffolding strategies with SHIFT tends to (1) increased processing costs, as reflected in longer reaction times, but (2) improved task understanding, evidenced by a lower error rate of almost 23%. We assessed the efficiency of SHIFT's selected scaffolding strategies across different cognitive states, finding that in three out of five states, the error rate was lower compared to the baseline condition. We discuss how these results align with the assumptions of the SHIFT model and highlight areas for refinement. Moreover, we demonstrate how scaffolding strategies, such as negation and hesitation, contribute to more effective human-robot explanatory dialogues.
PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning (ML) workloads launch hundreds to thousands of short-running GPU kernels per iteration. With GPU compute throughput growing rapidly, CPU-side launch latency of kernels is emerging as a bottleneck. CUDA Graphs promise to address this by replaying a set of kernels with a single dispatch of the graph, removing per-kernel launch costs. However, CUDA Graphs remain surprisingly difficult to deploy correctly and efficiently. We present PyGraph - a compiler framework to maximize the coverage and benefits of CUDA Graphs for ML workloads. It introduces three novel optimizations: it applies automatic code transformations to make ML applications amenable to CUDA Graphs; it eliminates the parameter copy overheads for kernels executing in CUDA Graphs, and it selectively deploys CUDA Graphs guided by a cost-benefit analysis. For 25 ML workloads from TorchBench, HuggingFace, and TIMM, PyGraph more than doubles the benefit from deploying CUDA Graph compared to the most popular and widely used ML compiler, PyTorch2. PyGraph is built atop PyTorch2's compilation framework and requires no programmer intervention.
SemEval-2025 Task 9: The Food Hazard Detection Challenge
Venue: arXiv cs.CL 2025
Year: 2025

In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.
Outsourcing an Information Operation: A Complete Dataset of Tenet Media's Podcasts on Rumble
Venue: arXiv cs.SI 2025
Year: 2025

Tenet Media, a U.S.-based, right-wing media company, hired six established podcasters to create content related to U.S. politics and culture during the 2024 U.S. presidential election cycle. After publishing content on YouTube and Rumble for nearly a year, Tenet Media was declared by the U.S. government to be funded entirely by Russia -- making it effectively an outsourced state-sponsored information operation (SSIO). We present a complete dataset of the 560 podcast videos published by the Tenet Media channel on the video-sharing platform Rumble between November 2023 and September 2024. Our dataset includes video metadata and user comments, as well as high-quality video transcriptions, representing over 300 hours of video content. This dataset provides researchers with material to study a Russian SSIO, and notably on Rumble, which is an understudied platform in SSIO scholarship.
Multi-view Learning for the Identification of Risky Users in Dynamic Social Networks
Venue: arXiv cs.SI 2025
Year: 2025

Technological progress in the last few decades has granted an increasing number of people access to social media platforms such as Facebook, X (formerly Twitter), and Instagram. Consequently, the potential risks associated with these services have also risen due to users exploiting these services for malicious purposes. The platforms have tools capable of detecting and blocking dangerous users, but they primarily focus on the content posted by users and usually overlook additional factors, such as the relationships among users. Another key aspect to consider is that users' beliefs and interests evolve over time. Therefore, a user who can be considered safe at one moment might later become malicious, and vice versa. This work describes a novel approach to node classification in temporal graphs, aimed at classifying users in social networks. The method was evaluated on a real-world scenario and was compared to a state-of-the-art system that treats the network as a static entity. Experiments showed that taking into account the temporal evolution of the network, in terms of node features and connections, is beneficial.
SLA-Awareness for AI-assisted coding
Venue: arXiv cs.SE 2025
Year: 2025

The integration of AI-assisted coding tools within development environments drastically reduces development time, and allows developers to focus more on creative and critical aspects of software engineering through the use of Code Large Language Models (CodeLLMs). These coding assistants automate repetitive and time-consuming coding tasks such as code generation, code completion, code summarization, and code translation. Responsiveness is a crucial requirement of these coding assistants to maintain real-time interactivity, such that their use does not impede the developers' workflows. Different coding tasks have unique characteristics and latency requirements: Time-To-First-Token (TTFT) latency is essential for code completion tasks, while End-To-End (E2E) latency is crucial for code translation tasks. Managing these varying requirements simultaneously while optimizing resource usage poses significant challenges. Existing work adopts the Model-as-a-Service paradigm for serving individual CodeLLMs, but cannot effectively manage latency requirements of concurrent coding tasks and sequences of CodeLLM inference calls, due to a lack of end-to-end latency awareness. Another challenge is keeping resource utilization high, when the serving system is deployed on a shared cluster environment. To address these challenges, we propose Coding Assistant Task Orchestrator (CATO), a runtime system designed to serve a diverse assortment of coding tasks while meeting latency requirements and maximizing resource utilization. Our experiments demonstrate that when all types of coding tasks were served simultaneously, for TTFT-critical tasks, CATO improves overall Goodput rate and resource utilization by up to 10% and 41.1%, respectively. P95 E2E latency was also reduced by 18% for code summarization tasks, and P95 TTFT for code generation tasks were reduced by 14% compared against state-of-the-art systems.