OpenRaiser/Intern-Atlas huggingface dataset:
A Lightweight Multi-Scale Attention Framework for Real-Time Spinal Endoscopic Instance Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Real-time instance segmentation for spinal endoscopy is important for identifying and protecting critical anatomy during surgery, but it is difficult because of the narrow field of view, specular highlights, smoke/bleeding, unclear boundaries, and large scale changes. Deployment is also constrained by limited surgical hardware, so the model must balance accuracy and speed and remain stable under small-batch (even batch-1) training. We propose LMSF-A, a lightweight multi-scale attention framework co-designed across backbone, neck, and head. The backbone uses a C2f-Pro module that combines RepViT-style re-parameterized convolution (RVB) with efficient multi-scale attention (EMA), enabling multi-branch training while collapsing into a single fast path for inference. The neck improves cross-scale consistency and boundary detail using Scale-Sequence Feature Fusion (SSFF) and Triple Feature Encoding (TFE), which strengthens high-resolution features. The head adopts a Lightweight Multi-task Shared Head (LMSH) with shared convolutions and GroupNorm to reduce parameters and support batch-1 stability. We also release the clinically reviewed PELD dataset (61 patients, 610 images) with instance masks for adipose tissue, bone, ligamentum flavum, and nerve. Experiments show that LMSF-A is highly competitive (or even better than) in all evaluation metrics and much lighter than most instance segmentation methods requiring only 1.8M parameters and 8.8 GFLOPs, and it generalizes well to a public teeth benchmark. Code and dataset: https://github.com/hhwmortal/PELD-Instance-segmentation.
DuaDeep-SeqAffinity: Dual-Stream Deep Learning Framework for Sequence-Only Antigen-Antibody Affinity Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Predicting the binding affinity between antigens and antibodies is fundamental to drug discovery and vaccine development. Traditional computational approaches often rely on experimentally determined 3D structures, which are scarce and computationally expensive to obtain. This paper introduces DuaDeep-SeqAffinity, a novel sequence-only deep learning framework that predicts affinity scores solely from their amino acid sequences using a dual-stream hybrid architecture. Our approach leverages pre-trained ESM-2 protein language model embeddings, combining 1D Convolutional Neural Networks (CNNs) for local motif detection with Transformer encoders for global contextual representation. A subsequent fusion module integrates these multi-faceted features, which are then passed to a fully connected network for final score regression. Experimental results demonstrate that DuaDeep-SeqAffinity significantly outperforms individual architectural components and existing state-of-the-art (SOTA) methods. DuaDeep achieved a superior Pearson correlation of 0.688, an R^2 of 0.460, and a Root Mean Square Error (RMSE) of 0.737, surpassing single-branch variants ESM-CNN and ESM-Transformer. Notably, the model achieved an Area Under the Curve (AUC) of 0.890, outperforming sequence-only benchmarks and even surpassing structure-sequence hybrid models. These findings prove that high-fidelity sequence embeddings can capture essential binding patterns typically reserved for structural modeling. By eliminating the reliance on 3D structures, DuaDeep-SeqAffinity provides a highly scalable and efficient solution for high-throughput screening of vast sequence libraries, significantly accelerating the therapeutic discovery pipeline.
LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration
Venue: arXiv cs.CV 2025
Year: 2025

Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89\% in success rate and 6.33\% in success weighted by path length, consistently across both seen and unseen environments.
SketchPlay: Intuitive Creation of Physically Realistic VR Content with Gesture-Driven Sketching
Venue: arXiv cs.HC 2025
Year: 2025

Creating physically realistic content in VR often requires complex modeling tools or predefined 3D models, textures, and animations, which present significant barriers for non-expert users. In this paper, we propose SketchPlay, a novel VR interaction framework that transforms humans' air-drawn sketches and gestures into dynamic, physically realistic scenes, making content creation intuitive and playful like drawing. Specifically, sketches capture the structure and spatial arrangement of objects and scenes, while gestures convey physical cues such as velocity, direction, and force that define movement and behavior. By combining these complementary forms of input, SketchPlay captures both the structure and dynamics of user-created content, enabling the generation of a wide range of complex physical phenomena, such as rigid body motion, elastic deformation, and cloth dynamics. Experimental results demonstrate that, compared to traditional text-driven methods, SketchPlay offers significant advantages in expressiveness, and user experience. By providing an intuitive and engaging creation process, SketchPlay lowers the entry barrier for non-expert users and shows strong potential for applications in education, art, and immersive storytelling.
Deep Learning Based Auction Design for Selling Agricultural Produce through Farmer Collectives to Maximize Nash Social Welfare
Venue: arXiv cs.GT 2025
Year: 2025

This paper is motivated by the need to design a robust market mechanism to benefit farmers (producers of agricultural produce) as well as buyers of agricultural produce (consumers). Our proposal is a volume discount auction with a Farmer Collective (FC) as the selling agent and high volume or retail consumers as buying agents. An FC is a cooperative of farmers coming together to harness the power of aggregation and economies of scale. Our auction mechanism seeks to satisfy fundamental properties such as incentive compatibility and individual rationality, and an extremely relevant property for the agriculture setting, namely, Nash social welfare maximization. Besides satisfying these properties, our proposed auction mechanism also ensures that certain practical business constraints are met. Since an auction satisfying all of these properties exactly is a theoretical impossibility, we invoke the idea of designing deep learning networks that learn such an auction with minimal violation of the desired properties. The proposed auction, which we call VDA-SAP (Volume Discount Auction for Selling Agricultural Produce), is superior in many ways to the classical VCG (Vickrey-Clarke-Groves) mechanism in terms of richness of properties satisfied and further outperforms other baseline auctions as well. We demonstrate our results for a realistic setting of an FC selling perishable vegetables to potential buyers.
Expert-Grounded Automatic Prompt Engineering for Extracting Lattice Constants of High-Entropy Alloys from Scientific Publications using Large Language Models
Venue: arXiv cs.DL 2025
Year: 2025

Large language models (LLMs) have shown promise for scientific data extraction from publications, but rely on manual prompt refinement. We present an expert-grounded automatic prompt optimization framework that enhances LLM entity extraction reliability. Using high-entropy alloy lattice constant extraction as a testbed, we optimized prompts for Claude 3.5 Sonnet through feedback cycles on seven expert-annotated publications. Despite a modest optimization budget, recall improved from 0.27 to > 0.9, demonstrating that a small, expert-curated dataset can yield significant improvements. The approach was applied to extract lattice constants from 2,267 publications, yielding data for 1,861 compositions. The optimized prompt transferred effectively to newer models: Claude 4.5 Sonnet, GPT-5, and Gemini 2.5 Flash. Analysis revealed three categories of LLM mistakes: contextual hallucination, semantic misinterpretation, and unit conversion errors, emphasizing the need for validation protocols. These results establish feedback-guided prompt optimization as a low-cost, transferable methodology for reliable scientific data extraction, providing a scalable pathway for complex LLM-assisted research tasks.
Men and Women Survivors in Science: A Comprehensive Analysis
Venue: arXiv cs.DL 2025
Year: 2025

We followed scientists who started publishing in 2000 and who continued publishing until 2020-2023 (N = 41,424). These survivors in science authored 2 million articles (N = 2,089,097) with more than 70 million cited references (N = 73,118,395) and worked in 38 OECD countries. Using a raw Scopus dataset, we examined gender disparities in publishing intensity, international collaboration, journal selection, productivity, citations, team formation, and publishing breaks in 16 STEMM and social science disciplines. Several author-level metrics were computed. Our data show a gender productivity gap for both lifetime scholarly output and annual journal prestige-normalized productivity. Surprisingly, in the context of extant literature, the data do not show a gender international collaboration gap, a gender journal selection gap, a gender citation gap, or a gender team formation gap. Men were on average 23% more productive than women cumulatively in 2000-2023 and 19% more productive in the last 5 years studied (2019-2023). Men and women published in equally prestigious journals, received the same number of citations (field-normalized), and worked in equally sized teams. In all, 80% of scientists in STEMM disciplines and 70% in the social sciences had published every year. Our data indicate interesting disciplinary differences in gender disparities.
On Harnessing Idle Compute at the Edge for Foundation Model Training
Venue: arXiv cs.DC 2025
Year: 2025

The ecosystem behind foundation model development today is highly centralized and limited to large-scale cloud data center operators: training foundation models is costly, needing immense compute resources. Decentralized foundation model training across edge devices, leveraging their spare compute, promises a democratized alternative. However, existing edge-training approaches fall short: they struggle to match cloud-based training performance, exhibit limited scalability with model size, exceed device memory capacity, and have prohibitive communication overhead. They also fail to satisfactorily handle device heterogeneity and dynamism. We introduce a new paradigm, Cleave, which finely partitions training operations through a novel selective hybrid tensor parallelism method. Together with a parameter server centric training framework, Cleave copes with device memory limits and avoids communication bottlenecks, thereby enabling efficient training of large models on par with the cloud. Further, with a cost optimization model to guide device selection and training workload distribution, Cleave effectively accounts for device heterogeneity and churn. Our evaluations show that Cleave matches cloud-based GPU training by scaling efficiently to larger models and thousands of devices, supporting up to 8x more devices than baseline edge-training approaches. It outperforms state-of-the-art edge training methods by up to a factor of 10 in per-batch training time and efficiently handles device failures, achieving at least 100x faster recovery than prior methods.
Practical challenges of control monitoring in frontier AI deployments
Venue: arXiv cs.CR 2025
Year: 2025

Automated control monitors could play an important role in overseeing highly capable AI agents that we do not fully trust. Prior work has explored control monitoring in simplified settings, but scaling monitoring to real-world deployments introduces additional dynamics: parallel agent instances, non-negligible oversight latency, incremental attacks between agent instances, and the difficulty of identifying scheming agents based on individual harmful actions. In this paper, we analyse design choices to address these challenges, focusing on three forms of monitoring with different latency-safety trade-offs: synchronous, semi-synchronous, and asynchronous monitoring. We introduce a high-level safety case sketch as a tool for understanding and comparing these monitoring protocols. Our analysis identifies three challenges -- oversight, latency, and recovery -- and explores them in four case studies of possible future AI deployments.
A Robust framework for sound event localization and detection on real recordings
Venue: arXiv cs.SD 2025
Year: 2025

This technical report describes the systems submitted to the DCASE2022 challenge task 3: sound event localization and detection (SELD). The task aims to detect occurrences of sound events and specify their class, furthermore estimate their position. Our system utilizes a ResNet-based model under a proposed robust framework for SELD. To guarantee the generalized performance on the real-world sound scenes, we design the total framework with augmentation techniques, a pipeline of mixing datasets from real-world sound scenes and emulations, and test time augmentation. Augmentation techniques and exploitation of external sound sources enable training diverse samples and keeping the opportunity to train the real-world context enough by maintaining the number of the real recording samples in the batch. In addition, we design a test time augmentation and a clustering-based model ensemble method to aggregate confident predictions. Experimental results show that the model under a proposed framework outperforms the baseline methods and achieves competitive performance in real-world sound recordings.
Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation
Venue: arXiv cs.SD 2025
Year: 2025

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mismatch and linguistic variability. This challenge is amplified for modern Large Language Model (LLM)-based ASR systems, whose massive scale and complex training dynamics make effective fine-tuning non-trivial. To address this gap, this paper proposes a principled and metric-driven fine-tuning framework for adapting both traditional and LLM-based ASR models to specialized domains. The framework emphasizes learning rate optimization based on performance metrics, combined with domain-specific data transformation and augmentation. We empirically evaluate our framework on state-of-the-art models, including Whisper, Whisper-Turbo, and Qwen2-Audio, across multi-domain, multilingual, and multi-length datasets. Our results not only validate the proposed framework but also establish practical protocols for improving domain-specific ASR performance while preventing overfitting.
IANEC: Digital Forensic Investigation of Contemporary Writers' Archives
Venue: arXiv cs.DL 2025
Year: 2025

The IANEC project (Investigation of Digital Archives of Contemporary Writers), led by the GREYC Research Lab and funded by the French Ministry of Culture aims to develop dedicated digital forensic investigation tools to automate the analysis of archival corpora from the Institut M{é}moires de l'{É}dition Contemporaine (IMEC). The project is based on the observation that born-digital archival materials are increasingly prevalent in contemporary archival institutions, and that digital forensics technologies have become essential for the extraction, identification, processing, and description of natively digital archival corpora.*
Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation
Venue: arXiv cs.AI 2025
Year: 2025

Retrieval-Augmented Generation RAG systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. We introduce Bidirectional RAG, a novel RAG architecture that enables safe corpus expansion through validated write back of high quality generated responses. Our system employs a multi stage acceptance layer combining grounding verification (NLI based entailment, attribution checking, and novelty detection to prevent hallucination pollution while enabling knowledge accumulation. Across four datasets Natural Questions, TriviaQA, HotpotQA, Stack Overflow with three random seeds 12 experiments per system, Bidirectional RAG achieves 40.58% average coverage nearly doubling Standard RAG 20.33% while adding 72% fewer documents than naive write back 140 vs 500. Our work demonstrates that self improving RAG is feasible and safe when governed by rigorous validation, offering a practical path toward RAG systems that learn from deployment.
Emergent Persuasion: Will LLMs Persuade Without Being Prompted?
Venue: arXiv cs.AI 2025
Year: 2025

With the wide-scale adoption of conversational AI systems, AI are now able to exert unprecedented influence on human opinion and beliefs. Recent work has shown that many Large Language Models (LLMs) comply with requests to persuade users into harmful beliefs or actions when prompted and that model persuasiveness increases with model scale. However, this prior work looked at persuasion from the threat model of $\textit{misuse}$ (i.e., a bad actor asking an LLM to persuade). In this paper, we instead aim to answer the following question: Under what circumstances would models persuade $\textit{without being explicitly prompted}$, which would shape how concerned we should be about such emergent persuasion risks. To achieve this, we study unprompted persuasion under two scenarios: (i) when the model is steered (through internal activation steering) along persona traits, and (ii) when the model is supervised-finetuned (SFT) to exhibit the same traits. We showed that steering towards traits, both related to persuasion and unrelated, does not reliably increase models' tendency to persuade unprompted, however, SFT does. Moreover, SFT on general persuasion datasets containing solely benign topics admits a model that has a higher propensity to persuade on controversial and harmful topics--showing that emergent harmful persuasion can arise and should be studied further.
Scalable Cloud-Native Architectures for Intelligent PMU Data Processing
Venue: arXiv cs.DC 2025
Year: 2025

Phasor Measurement Units (PMUs) generate high-frequency, time-synchronized data essential for real-time power grid monitoring, yet the growing scale of PMU deployments creates significant challenges in latency, scalability, and reliability. Conventional centralized processing architectures are increasingly unable to handle the volume and velocity of PMU data, particularly in modern grids with dynamic operating conditions. This paper presents a scalable cloud-native architecture for intelligent PMU data processing that integrates artificial intelligence with edge and cloud computing. The proposed framework employs distributed stream processing, containerized microservices, and elastic resource orchestration to enable low-latency ingestion, real-time anomaly detection, and advanced analytics. Machine learning models for time-series analysis are incorporated to enhance grid observability and predictive capabilities. Analytical models are developed to evaluate system latency, throughput, and reliability, showing that the architecture can achieve sub-second response times while scaling to large PMU deployments. Security and privacy mechanisms are embedded to support deployment in critical infrastructure environments. The proposed approach provides a robust and flexible foundation for next-generation smart grid analytics.
Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics knowledge and patient-specific meta-information, which are critical for functional-semantic correlation mining. In this study, we introduce a meta-information guided cross-domain synergistic diffusion model (MiG-DM) that integrates comprehensive cross-modal priors to generate high-quality PET images. Specifically, a meta-information encoding module transforms clinical parameters into semantic prompts by considering patient characteristics, dose-related information, and semi-quantitative parameters, enabling cross-modal alignment between textual meta-information and image reconstruction. Additionally, the cross-domain architecture combines projection-domain and image-domain processing. In the projection domain, a specialized sinogram adapter captures global physical structures through convolution operations equivalent to global image-domain filtering. Experiments on the UDPET public dataset and clinical datasets with varying dose levels demonstrate that MiG-DM outperforms state-of-the-art methods in enhancing PET image quality and preserving physiological details.
Enhanced geometry prediction in laser directed energy deposition using meta-learning
Venue: arXiv cs.LG 2025
Year: 2025

Accurate bead geometry prediction in laser-directed energy deposition (L-DED) is often hindered by the scarcity and heterogeneity of experimental datasets collected under different materials, machine configurations, and process parameters. To address this challenge, a cross-dataset knowledge transfer model based on meta-learning for predicting deposited track geometry in L-DED is proposed. Specifically, two gradient-based meta-learning algorithms, i.e., Model-Agnostic Meta-Learning (MAML) and Reptile, are investigated to enable rapid adaptation to new deposition conditions with limited data. The proposed framework is performed using multiple experimental datasets compiled from peer-reviewed literature and in-house experiments and evaluated across powder-fed, wire-fed, and hybrid wire-powder L-DED processes. Results show that both MAML and Reptile achieve accurate bead height predictions on unseen target tasks using as few as three to nine training examples, consistently outperforming conventional feedforward neural networks trained under comparable data constraints. Across multiple target tasks representing different printing conditions, the meta-learning models achieve strong generalization performance, with R-squared values reaching up to approximately 0.9 and mean absolute errors between 0.03-0.08 mm, demonstrating effective knowledge transfer across heterogeneous L-DED settings.