OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras
Venue: arXiv cs.CV 2025
Year: 2025

Accurate 3D scene reconstruction is essential for numerous medical tasks. Given the challenges in obtaining ground truth data, there has been an increasing focus on self-supervised learning (SSL) for endoscopic depth estimation as a basis for scene reconstruction. While foundation models have shown remarkable progress in visual tasks, their direct application to the medical domain often leads to suboptimal results. However, the visual features from these models can still enhance endoscopic tasks, emphasizing the need for efficient adaptation strategies, which still lack exploration currently. In this paper, we introduce Endo3DAC, a unified framework for endoscopic scene reconstruction that efficiently adapts foundation models. We design an integrated network capable of simultaneously estimating depth maps, relative poses, and camera intrinsic parameters. By freezing the backbone foundation model and training only the specially designed Gated Dynamic Vector-Based Low-Rank Adaptation (GDV-LoRA) with separate decoder heads, Endo3DAC achieves superior depth and pose estimation while maintaining training efficiency. Additionally, we propose a 3D scene reconstruction pipeline that optimizes depth maps' scales, shifts, and a few parameters based on our integrated network. Extensive experiments across four endoscopic datasets demonstrate that Endo3DAC significantly outperforms other state-of-the-art methods while requiring fewer trainable parameters. To our knowledge, we are the first to utilize a single network that only requires surgical videos to perform both SSL depth estimation and scene reconstruction tasks. The code will be released upon acceptance.
SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models
Venue: arXiv cs.DC 2025
Year: 2025

Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in a so-called speculation phase, which are subsequently verified by the LLM in a verification phase. However, current state-of-the-art speculative decoding approaches have three key limitations: handling requests with varying difficulty using homogeneous SSMs, lack of robust support for batch processing, and insufficient holistic optimization for both speculation and verification phases. In this paper, we introduce SPIN, an efficient LLM inference serving system based on speculative decoding, designed to address these challenges through three main innovations. First, SPIN improves token speculation by using multiple heterogeneous SSMs, with a learning-based algorithm for SSM selection that operates without prior knowledge of request difficulty. Second, SPIN employs a request decomposition method to minimize batching overhead during LLM verification. Finally, SPIN orchestrates speculation and verification phases by pipelining their executions on GPUs to achieve further acceleration. Experimental results demonstrate that SPIN significantly outperforms state-of-the-art methods, achieving a performance increase of approximately 2.28X.
Modeling Face Emotion Perception from Naturalistic Face Viewing: Insights from Fixational Events and Gaze Strategies
Venue: arXiv cs.HC 2025
Year: 2025

Face Emotion Recognition (FER) is essential for social interactions and understanding others' mental states. Utilizing eye tracking to investigate FER has yielded insights into cognitive processes. In this study, we utilized an instructionless paradigm to collect eye movement data from 21 participants, examining two FER processes: free viewing and grounded FER. We analyzed fixational, pupillary, and microsaccadic events from eye movements, establishing their correlation with emotion perception and performance in the grounded task. By identifying regions of interest on the face, we explored the impact of eye-gaze strategies on face processing, their connection to emotions, and performance in emotion perception. During free viewing, participants displayed specific attention patterns for various emotions. In grounded tasks, where emotions were interpreted based on words, we assessed performance and contextual understanding. Notably, gaze patterns during free viewing predicted success in grounded FER tasks, underscoring the significance of initial gaze behavior. We also employed features from pre-trained deep-learning models for face recognition to enhance the scalability and comparability of attention analysis during free viewing across different datasets and populations. This method facilitated the prediction and modeling of individual emotion perception performance from minimal observations. Our findings advance the understanding of the link between eye movements and emotion perception, with implications for psychology, human-computer interaction, and affective computing, and pave the way for developing precise emotion recognition systems.
An Evaluation Framework for the FAIR Assessment tools in Open Science
Venue: arXiv cs.DB 2025
Year: 2025

Open science represents a transformative research approach essential for enhancing sustainability and impact. Data generation encompasses various methods, from automated processes to human-driven inputs, creating a rich and diverse landscape. Embracing the FAIR principles -- making data and, in general, artifacts (such as code, configurations, documentation, etc) findable, accessible, interoperable, and reusable -- ensures research integrity, transparency, and reproducibility, and researchers enhance the efficiency and efficacy of their endeavors, driving scientific innovation and the advancement of knowledge. Open Science Platforms OSP (i.e., technologies that publish data in a way that they are findable, accessible, interoperable, and reusable) are based on open science guidelines and encourage accessibility, cooperation, and transparency in scientific research. Evaluating OSP will yield sufficient data and artifacts to enable better sharing and arrangement, stimulating more investigation and the development of new platforms. In this paper, we propose an evaluation framework that results from evaluating twenty-two FAIR-a tools assessing the FAIR principles of OSP to identify differences, shortages, and possible efficiency improvements.
Information maximization for a broad variety of multi-armed bandit games
Venue: arXiv cs.LG 2025
Year: 2025

Information and free-energy maximization are physics principles that provide general rules for an agent to optimize actions in line with specific goals and policies. These principles are the building blocks for designing decision-making policies capable of efficient performance with only partial information. Notably, the information maximization principle has shown remarkable success in the classical bandit problem and has recently been shown to yield optimal algorithms for Gaussian and sub-Gaussian reward distributions. This article explores a broad extension of physics-based approaches to more complex and structured bandit problems. To this end, we cover three distinct types of bandit problems, where information maximization is adapted and leads to strong performance. Since the main challenge of information maximization lies in avoiding over-exploration, we highlight how information is tailored at various levels to mitigate this issue, paving the way for more efficient and robust decision-making strategies.
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer
Venue: arXiv cs.CL 2025
Year: 2025

This work explores optimizing transformer-based language models by integrating model compression techniques with inhibitor attention, a novel alternative attention mechanism. Inhibitor attention employs Manhattan distances and ReLU activations instead of the matrix multiplications and softmax activation of the conventional scaled dot-product attention. This shift offers potential computational and energy savings while maintaining model effectiveness. We propose further adjustments to improve the inhibitor mechanism's training efficiency and evaluate its performance on the DistilBERT architecture. Our knowledge distillation experiments indicate that the modified inhibitor transformer model can achieve competitive performance on standard NLP benchmarks, including General Language Understanding Evaluation (GLUE) and sentiment analysis tasks.
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. However, deep learning typically requires large training datasets, which are rarely available in astrophotography. Deep Image Prior (DIP) bypasses this constraint by performing blind training on a single image. Although effective in some cases, DIP often suffers from overfitting, artifact generation, and instability. To overcome these issues and improve general performance, this work proposes DIPLI - a framework that shifts from single-frame to multi-frame training using the Back Projection technique, combined with optical flow estimation via the TVNet model, and replaces deterministic predictions with unbiased Monte Carlo estimation obtained through Langevin dynamics. A comprehensive evaluation compares the method against Lucky Imaging, a classical computer vision technique still widely used in astronomical image reconstruction, DIP, the transformer-based model RVRT, and the diffusion-based model DiffIR2VR-Zero. Experiments on synthetic datasets demonstrate consistent improvements, with the method outperforming baselines for SSIM, PSNR, LPIPS, and DISTS metrics in the majority of cases. In addition to superior reconstruction quality, the model also requires far fewer input images than Lucky Imaging and is less prone to overfitting or artifact generation. Evaluation on real-world astronomical data, where domain shifts typically hinder generalization, shows that the method maintains high reconstruction quality, confirming practical robustness.
ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated their capabilities across various NLP tasks. Their potential in e-commerce is also substantial, evidenced by practical implementations such as platform search, personalized recommendations, and customer service. One primary concern associated with LLMs is their factuality (e.g., hallucination), which is urgent in e-commerce due to its significant impact on user experience and revenue. Despite some methods proposed to evaluate LLMs' factuality, issues such as lack of reliability, high consumption, and lack of domain expertise leave a gap between effective assessment in e-commerce. To bridge the evaluation gap, we propose ECKGBench, a dataset specifically designed to evaluate the capacities of LLMs in e-commerce knowledge. Specifically, we adopt a standardized workflow to automatically generate questions based on a large-scale knowledge graph, guaranteeing sufficient reliability. We employ the simple question-answering paradigm, substantially improving the evaluation efficiency by the least input and output tokens. Furthermore, we inject abundant e-commerce expertise in each evaluation stage, including human annotation, prompt design, negative sampling, and verification. Besides, we explore the LLMs' knowledge boundaries in e-commerce from a novel perspective. Through comprehensive evaluations of several advanced LLMs on ECKGBench, we provide meticulous analysis and insights into leveraging LLMs for e-commerce.
SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes SenseExpo, an efficient autonomous exploration framework based on a lightweight prediction network, which addresses the limitations of traditional methods in computational overhead and environmental generalization. By integrating Generative Adversarial Networks (GANs), Transformer, and Fast Fourier Convolution (FFC), we designed a lightweight prediction model with merely 709k parameters. Our smallest model achieves better performance on the KTH dataset than U-net (24.5M) and LaMa (51M), delivering PSNR 9.026 and SSIM 0.718, particularly representing a 38.7% PSNR improvement over the 51M-parameter LaMa model. Cross-domain testing demonstrates its strong generalization capability, with an FID score of 161.55 on the HouseExpo dataset, significantly outperforming comparable methods. Regarding exploration efficiency, on the KTH dataset,SenseExpo demonstrates approximately a 67.9% time reduction in exploration time compared to MapEx. On the MRPB 1.0 dataset, SenseExpo achieves 77.1% time reduction roughly compared to MapEx. Deployed as a plug-and-play ROS node, the framework seamlessly integrates with existing navigation systems, providing an efficient solution for resource-constrained devices.
Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose corrective in-context learning (CICL), an approach that incorporates a model's incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive experiments on text classification tasks demonstrate that CICL consistently underperforms standard ICL, with performance degrading as the proportion of corrections in the prompt increases. Our findings indicate that CICL introduces confusion by disrupting the model's task understanding, rather than refining its predictions. Additionally, we observe that presenting harder examples in standard ICL does not improve performance, suggesting that example difficulty alone may not be a reliable criterion for effective selection. By presenting these negative results, we provide important insights into the limitations of self-corrective mechanisms in LLMs and offer directions for future research.
DevOps Automation Pipeline Deployment with IaC (Infrastructure as Code)
Venue: arXiv cs.SE 2025
Year: 2025

DevOps pipeline is a set of automated tasks or processes or jobs that has tasks assigned to execute automatically that allow the Development team and Operations team to collaborate for building and deployment of the software or services. DevOps as a culture includes better collaboration between different teams within an organization and the removal of silos between them. This paper aims to streamline the current software development and deployment process that is being followed in most of today's generation DevOps deployment as Continuous Integration and Continuous Delivery (CI/CD) pipelines. Centered to the level of software development life cycle (SDLC), it also describes the current ambiguous definition to clarify the implementation of DevOps in practice along a sample CI/CD pipeline deployment. The further objective of the paper is to demonstrate the implementation strategy of DevOps Infrastructure as Code (IaC) and Pipeline as a code and the removal of ambiguity in the definition of DevOps Infrastructure as a Code methodology.
GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation
Venue: arXiv cs.AI 2025
Year: 2025

This study introduces GreenIQ, an AI-powered deep search platform designed to revolutionise carbon market intelligence through autonomous analysis and automated report generation. Carbon markets operate across diverse regulatory landscapes, generating vast amounts of heterogeneous data from policy documents, industry reports, academic literature, and real-time trading platforms. Traditional research approaches remain labour-intensive, slow, and difficult to scale. GreenIQ addresses these limitations through a multi-agent architecture powered by Large Language Models (LLMs), integrating five specialised AI agents: a Main Researcher Agent for intelligent information retrieval, a Report Writing Agent for structured synthesis, a Final Reviewer Agent for accuracy verification, a Data Visualisation Agent for enhanced interpretability, and a Translator Agent for multilingual adaptation. The system achieves seamless integration of structured and unstructured information with AI-driven citation verification, ensuring high transparency and reliability. GreenIQ delivers a 99.2\% reduction in processing time and a 99.7\% cost reduction compared to traditional research methodologies. A novel AI persona-based evaluation framework involving 16 domain-specific AI personas highlights its superior cross-jurisdictional analytical capabilities and regulatory insight generation. GreenIQ sets new standards in AI-driven research synthesis, policy analysis, and sustainability finance by streamlining carbon market research. It offers an efficient and scalable framework for environmental and financial intelligence, enabling more accurate, timely, and cost-effective decision-making in complex regulatory landscapes
Sustainable Open-Data Management for Field Research: A Cloud-Based Approach in the Underlandscape Project
Venue: arXiv cs.DB 2025
Year: 2025

Field-based research projects require a robust suite of ICT services to support data acquisition, documentation, storage, and dissemination. A key challenge lies in ensuring the sustainability of data management - not only during the project's funded period but also beyond its conclusion, when maintenance and support often depend on voluntary efforts. In the Underlandscape project, we tackled this challenge by extensively leveraging public cloud services while minimizing reliance on complex custom infrastructure. This paper provides a comprehensive overview of the project's final infrastructure, detailing the adopted data formats, the cloud-based solutions enabling data management, and the custom applications developed for system integration.
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation
Venue: arXiv cs.CL 2025
Year: 2025

Although existing fashionable generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, they often result in the inclusion of irrelevant and redundant tokens in rewritten utterances due to their inability to focus on critical tokens in dialogue context. Furthermore, the limited size of the training datasets also contributes to the insufficient training of the IUR model. To address the first issue, we propose a multi-task learning framework EO-IUR (Editing Operation-guided Incomplete Utterance Rewriting) that introduces the editing operation labels generated by sequence labeling module to guide generation model to focus on critical tokens. Furthermore, we introduce a token-level heterogeneous graph to represent dialogues. To address the second issue, we propose a two-dimensional utterance augmentation strategy, namely editing operation-based incomplete utterance augmentation and LLM-based historical utterance augmentation. The experimental results on three datasets demonstrate that our EO-IUR outperforms previous state-of-the-art (SOTA) baselines in both open-domain and task-oriented dialogue. The code will be available at https://github.com/Dewset/EO-IUR.
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement tools frequently suffer from degradation, including blurring and loss of detail, which can impede measurement accuracy.As a potential remedy, we in this paper propose a Semantic-Guided Global-Local Collaborative Network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pre-trained model to guide the super-resolution process, enhancing image detail quality effectively. Specifically,we propose a Semantic Guidance Module that seamlessly integrates the semantic priors into the super-resolution network, enabling the network to more adeptly capture and utilize semantic priors, thereby enhancing image details. To further explore both local and non-local interactions for improved detail rendition,we propose a Global-Local Collaborative Module, which features three Global and Local Detail Enhancement Modules, as well as a Hybrid Attention Mechanism to work together to efficiently learn more useful features. Our extensive experiments show that SGGLC-Net achieves competitive PSNR and SSIM values across multiple benchmark datasets, demonstrating higher performance with the multi-adds reduction of 12.81G compared to state-of-the-art lightweight super-resolution approaches. These improvements underscore the potential of our approach to enhance the precision and effectiveness of visual measurement systems. Codes are at https://github.com/fanamber831/SGGLC-Net.
DSLUT: An Asymmetric LUT and its Automatic Design Flow Based on Practical Functions
Venue: arXiv cs.AR 2025
Year: 2025

The conventional LUT is redundant since practical functions in real-world benchmarks only occupy a small proportion of all the functions. For example, there are only 3881 out of more than $10^{14}$ NPN classes of 6-input functions occurring in the mapped netlists of the VTR8 and Koios benchmarks. Therefore, we propose a novel LUT-like architecture, named DSLUT, with asymmetric inputs and programmable bits to efficiently implement the practical functions in domain-specific benchmarks instead of all the functions. The compact structure of the MUX Tree in the conventional LUT is preserved, while fewer programmable bits are connected to the MUX Tree according to the bit assignment generated by the proposed algorithm. A 6-input DSLUT with 26 SRAM bits is generated for evaluation, which is based on the practical functions of 39 circuits from the VTR8 and Koios benchmarks. After the synthesis flow of ABC, the post-synthesis results show that the proposed DSLUT6 architecture reduces the number of levels by 10.98% at a cost of 7.25% area overhead compared to LUT5 architecture, while LUT6 reduces 15.16% levels at a cost of 51.73% more PLB area. After the full VTR flow, the post-implementation results show that the proposed DSLUT6 can provide performance improvement by 4.59% over LUT5, close to 5.42% of LUT6 over LUT5, causing less area overhead (6.81% of DSLUT6 and 10.93% of LUT6).
PromptMobile: Efficient Promptus for Low Bandwidth Mobile Video Streaming
Venue: arXiv cs.NI 2025
Year: 2025

Traditional video compression algorithms exhibit significant quality degradation at extremely low bitrates. Promptus emerges as a new paradigm for video streaming, substantially cutting down the bandwidth essential for video streaming. However, Promptus is computationally intensive and can not run in real-time on mobile devices. This paper presents PromptMobile, an efficient acceleration framework tailored for on-device Promptus. Specifically, we propose (1) a two-stage efficient generation framework to reduce computational cost by 8.1x, (2) a fine-grained inter-frame caching to reduce redundant computations by 16.6%, (3) system-level optimizations to further enhance efficiency. The evaluations demonstrate that compared with the original Promptus, PromptMobile achieves a 13.6x increase in image generation speed. Compared with other streaming methods, PromptMobile achives an average LPIPS improvement of 0.016 (compared with H.265), reducing 60% of severely distorted frames (compared to VQGAN).