OpenRaiser/Intern-Atlas huggingface dataset:
SPID-Chain: A Smart Contract-Enabled, Polar-Coded Interoperable DAG Chain
Venue: arXiv cs.DC 2025
Year: 2025

As the digital landscape evolves, Web3 has gained prominence, highlighting the critical role of decentralized, interconnected, and verifiable digital ecosystems. This paper introduces SPID-Chain, a novel interoperability consensus designed for Web3, which employs a directed acyclic graph (DAG) of blockchains to facilitate seamless integration across multiple blockchains. Within SPID-Chain, each blockchain maintains its own consensus and processes transactions via an intra-consensus mechanism that incorporates event-driven smart contracts (EDSC) and Polar codes for optimized computation distribution. This mechanism is complemented by a division of committee and worker nodes, enhancing transaction processing efficiency within individual chains. For inter-blockchain consensus, SPID-Chain utilizes a DAG structure where blockchains append blocks containing cross-chain transactions. These blocks are then processed through the inter-consensus mechanism orchestrated by the blockchains. Extensive simulations validate the efficacy of our scheme in terms of throughput, scalability, decentralization, and security. Our results showcase SPID-Chain's potential to enable fluid interactions and transactions across diverse blockchain networks, aligning with the foundational goals of Web3.
Automating RT Planning at Scale: High Quality Data For AI Training
Venue: arXiv cs.HC 2025
Year: 2025

Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Varian Eclipse. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations is proposed. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. To our best knowledge, this dataset features more than 10 times number of plans compared to the largest existing well-curated public dataset. Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.
Survey on Monocular Metric Depth Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Monocular Depth Estimation (MDE) enables spatial understanding, 3D reconstruction, and autonomous navigation, yet deep learning approaches often predict only relative depth without a consistent metric scale. This limitation reduces reliability in applications such as visual SLAM, precise 3D modeling, and view synthesis. Monocular Metric Depth Estimation (MMDE) overcomes this challenge by producing depth maps with absolute scale, ensuring geometric consistency and enabling deployment without additional calibration. This survey reviews the evolution of MMDE, from geometry-based methods to state-of-the-art deep models, with emphasis on the datasets that drive progress. Key benchmarks, including KITTI, NYU-D, ApolloScape, and TartanAir, are examined in terms of modality, scene type, and application domain. Methodological advances are analyzed, covering domain generalization, boundary preservation, and the integration of synthetic and real data. Techniques such as unsupervised and semi-supervised learning, patch-based inference, architectural innovations, and generative modeling are evaluated for their strengths and limitations. By synthesizing current progress, highlighting the importance of high-quality datasets, and identifying open challenges, this survey provides a structured reference for advancing MMDE and supporting its adoption in real-world computer vision systems.
Cross-Entropy Attacks to Language Models via Rare Event Simulation
Venue: arXiv cs.CL 2025
Year: 2025

Black-box textual adversarial attacks are challenging due to the lack of model information and the discrete, non-differentiable nature of text. Existing methods often lack versatility for attacking different models, suffer from limited attacking performance due to the inefficient optimization with word saliency ranking, and frequently sacrifice semantic integrity to achieve better attack outcomes. This paper introduces a novel approach to textual adversarial attacks, which we call Cross-Entropy Attacks (CEA), that uses Cross-Entropy optimization to address the above issues. Our CEA approach defines adversarial objectives for both soft-label and hard-label settings and employs CE optimization to identify optimal replacements. Through extensive experiments on document classification and language translation problems, we demonstrate that our attack method excels in terms of attacking performance, imperceptibility, and sentence quality.
LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems
Venue: arXiv cs.SE 2025
Year: 2025

Thorough simulation testing is crucial for validating the correct behavior of small Uncrewed Aerial Systems (sUAS) across multiple scenarios, including adverse weather conditions (such as wind, and fog), diverse settings (hilly terrain, or urban areas), and varying mission profiles (surveillance, tracking). While various sUAS simulation tools exist to support developers, the entire process of creating, executing, and analyzing simulation tests remains a largely manual and cumbersome task. Developers must identify test scenarios, set up the simulation environment, integrate the System under Test (SuT) with simulation tools, formulate mission plans, and collect and analyze results. These labor-intensive tasks limit the ability of developers to conduct exhaustive testing across a wide range of scenarios. To alleviate this problem, in this paper, we propose AutoSimTest, a Large Language Model (LLM)-driven framework, where multiple LLM agents collaborate to support the sUAS simulation testing process. This includes: (1) creating test scenarios that subject the SuT to unique environmental contexts; (2) preparing the simulation environment as per the test scenario; (3) generating diverse sUAS missions for the SuT to execute; and (4) analyzing simulation results and providing an interactive analytics interface. Further, the design of the framework is flexible for creating and testing scenarios for a variety of sUAS use cases, simulation tools, and SuT input requirements. We evaluated our approach by (a) conducting simulation testing of PX4 and ArduPilot flight-controller-based SuTs, (b) analyzing the performance of each agent, and (c) gathering feedback from sUAS developers. Our findings indicate that AutoSimTest significantly improves the efficiency and scope of the sUAS testing process, allowing for more comprehensive and varied scenario evaluations while reducing the manual effort.
From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
Venue: arXiv cs.CL 2025
Year: 2025

Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that increasing test-time computational resources can further improve performance. In this work, we introduce Aggregation Fine-Tuning (AFT), a supervised finetuning paradigm where the model learns to synthesize multiple draft responses, referred to as proposals, into a single, refined answer, termed aggregation. At inference time, a propose-and-aggregate strategy further boosts performance by iteratively generating proposals and aggregating them. Empirical evaluations on benchmark datasets show that AFT-trained models substantially outperform standard SFT. Notably, an AFT model, fine-tuned from Llama3.1-8B-Base with only 64k data, achieves a 41.3% LC win rate on AlpacaEval 2, surpassing significantly larger LLMs such as Llama3.1-405B-Instruct and GPT4. By combining sequential refinement and parallel sampling, the propose-and-aggregate framework scales inference-time computation in a flexible manner. Overall, These findings position AFT as a promising approach to unlocking additional capabilities of LLMs without resorting to increasing data volume or model size.
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Dynamic graph representation learning plays a crucial role in understanding evolving behaviors. However, existing methods often struggle with flexibility, adaptability, and the preservation of temporal and structural dynamics. To address these issues, we propose Community-aware Temporal Walks (CTWalks), a novel framework for representation learning on continuous-time dynamic graphs. CTWalks integrates three key components: a community-based parameter-free temporal walk sampling mechanism, an anonymization strategy enriched with community labels, and an encoding process that leverages continuous temporal dynamics modeled via ordinary differential equations (ODEs). This design enables precise modeling of both intra- and inter-community interactions, offering a fine-grained representation of evolving temporal patterns in continuous-time dynamic graphs. CTWalks theoretically overcomes locality bias in walks and establishes its connection to matrix factorization. Experiments on benchmark datasets demonstrate that CTWalks outperforms established methods in temporal link prediction tasks, achieving higher accuracy while maintaining robustness.
Channel Resolvability Using Multiplicative Weight Update Algorithm
Venue: arXiv cs.IT 2025
Year: 2025

We study the channel resolvability problem, which is used to prove strong converse of identification via channel. Channel resolvability has been solved by only random coding in the literature. We prove channel resolvability using the multiplicative weight update algorithm. This is the first approach to channel resolvability using non-random coding.
Equilibria under Dynamic Benchmark Consistency in Non-Stationary Multi-Agent Systems
Venue: arXiv cs.GT 2025
Year: 2025

We formulate and study a general time-varying multi-agent system where players repeatedly compete under incomplete information. Our work is motivated by scenarios commonly observed in online advertising and retail marketplaces, where agents and platform designers optimize algorithmic decision-making in dynamic competitive settings. In these systems, no-regret algorithms that provide guarantees relative to \emph{static} benchmarks can perform poorly and the distributions of play that emerge from their interaction do not correspond anymore to static solution concepts such as coarse correlated equilibria. Instead, we analyze the interaction of \textit{dynamic benchmark} consistent policies that have performance guarantees relative to \emph{dynamic} sequences of actions, and through a novel \textit{tracking error} notion we delineate when their empirical joint distribution of play can approximate an evolving sequence of static equilibria. In systems that change sufficiently slowly (sub-linearly in the horizon length), we show that the resulting distributions of play approximate the sequence of coarse correlated equilibria, and apply this result to establish improved welfare bounds for smooth games. On a similar vein, we formulate internal dynamic benchmark consistent policies and establish that they approximate sequences of correlated equilibria. Our findings therefore suggest that in a broad range of multi-agent systems where non-stationarity is prevalent, algorithms designed to compete with dynamic benchmarks can improve both individual and welfare guarantees, and their emerging dynamics approximate a sequence of static equilibrium outcomes.
Enhancing Adversarial Transferability via Component-Wise Transformation
Venue: arXiv cs.CV 2025
Year: 2025

Deep Neural Networks (DNNs) are highly vulnerable to adversarial examples, which pose significant challenges in security-sensitive applications. Among various adversarial attack strategies, input transformation-based attacks have demonstrated remarkable effectiveness in enhancing adversarial transferability. However, existing methods still perform poorly across different architectures, even though they have achieved promising results within the same architecture. This limitation arises because, while models of the same architecture may focus on different regions of the object, the variation is even more pronounced across different architectures. Unfortunately, current approaches fail to effectively guide models to attend to these diverse regions. To address this issue, this paper proposes a novel input transformation-based attack method, termed Component-Wise Transformation (CWT). CWT applies interpolation and selective rotation to individual image blocks, ensuring that each transformed image highlights different target regions, thereby improving the transferability of adversarial examples. Extensive experiments on the standard ImageNet dataset show that CWT consistently outperforms state-of-the-art methods in both attack success rates and stability across CNN- and Transformer-based models.
Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, the integration of deep learning techniques with remote sensing technology has revolutionized the way natural hazards, such as floods, are monitored and managed. However, existing methods for flood segmentation using remote sensing data often overlook the utility of correlative features among multispectral satellite information. In this study, we introduce a progressive cross attention network (ProCANet), a deep learning model that progressively applies both self- and cross-attention mechanisms to multispectral features, generating optimal feature combinations for flood segmentation. The proposed model was compared with state-of-the-art approaches using Sen1Floods11 dataset and our bespoke flood data generated for the Citarum River basin, Indonesia. Our model demonstrated superior performance with the highest Intersection over Union (IoU) score of 0.815. Our results in this study, coupled with the ablation assessment comparing scenarios with and without attention across various modalities, opens a promising path for enhancing the accuracy of flood analysis using remote sensing technology.
Construction of Simultaneously Good Polar Codes and Polar Lattices
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we investigate the simultaneous goodness of polar codes and polar lattices. The simultaneous goodness of a lattice or a code means that it is optimal for both channel coding and source coding simultaneously. The existence of such kind of lattices was proven by using random lattice ensembles. Our work provides an explicit construction based on the polarization technique.
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization
Venue: arXiv cs.AI 2025
Year: 2025

Graph-structured combinatorial challenges are inherently difficult due to their nonlinear and intricate nature, often rendering traditional computational methods ineffective or expensive. However, these challenges can be more naturally tackled by humans through visual representations that harness our innate ability for spatial reasoning. In this study, we propose transforming graphs into images to preserve their higher-order structural features accurately, revolutionizing the representation used in solving graph-structured combinatorial tasks. This approach allows machines to emulate human-like processing in addressing complex combinatorial challenges. By combining the innovative paradigm powered by multimodal large language models (MLLMs) with simple search techniques, we aim to develop a novel and effective framework for tackling such problems. Our investigation into MLLMs spanned a variety of graph-based tasks, from combinatorial problems like influence maximization to sequential decision-making in network dismantling, as well as addressing six fundamental graph-related issues. Our findings demonstrate that MLLMs exhibit exceptional spatial intelligence and a distinctive capability for handling these problems, significantly advancing the potential for machines to comprehend and analyze graph-structured data with a depth and intuition akin to human cognition. These results also imply that integrating MLLMs with simple optimization strategies could form a novel and efficient approach for navigating graph-structured combinatorial challenges without complex derivations, computationally demanding training and fine-tuning.
Linear Feedback Control Systems for Iterative Prompt Optimization in Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) have revolutionized various applications by generating outputs based on given prompts. However, achieving the desired output requires iterative prompt refinement. This paper presents a novel approach that draws parallels between the iterative prompt optimization process in LLMs and feedback control systems. We iteratively refine the prompt by treating the deviation between the LLM output and the desired result as an error term until the output criteria are met. This process is akin to a feedback control system, where the LLM, despite being non-linear and non-deterministic, is managed using principles from linear feedback control systems. We explore the application of different types of controllers within this framework, providing a mathematical foundation for integrating linear feedback control mechanisms with LLMs.
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Venue: arXiv cs.CV 2025
Year: 2025

Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.
Foreign object segmentation in chest x-rays through anatomy-guided shape insertion
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we tackle the challenge of instance segmentation for foreign objects in chest radiographs, commonly seen in postoperative follow-ups with stents, pacemakers, or ingested objects in children. The diversity of foreign objects complicates dense annotation, as shown in insufficient existing datasets. To address this, we propose the simple generation of synthetic data through (1) insertion of arbitrary shapes (lines, polygons, ellipses) with varying contrasts and opacities, and (2) cut-paste augmentations from a small set of semi-automatically extracted labels. These insertions are guided by anatomy labels to ensure realistic placements, such as stents appearing only in relevant vessels. Our approach enables networks to segment complex structures with minimal manually labeled data. Notably, it achieves performance comparable to fully supervised models while using 93\% fewer manual annotations.
Application of Machine Learning Techniques for Secure Traffic in NoC-based Manycores
Venue: arXiv cs.CR 2025
Year: 2025

Like most computer systems, a manycore can also be the target of security attacks. It is essential to ensure the security of the NoC since all information travels through its channels, and any interference in the traffic of messages can reflect on the entire chip, causing communication problems. Among the possible attacks on NoC, Denial of Service (DoS) attacks are the most cited in the literature. The state of the art shows a lack of work that can detect such attacks through learning techniques. On the other hand, these techniques are widely explored in computer network security via an Intrusion Detection System (IDS). In this context, the main goal of this document is to present the progress of a work that explores an IDS technique using machine learning and temporal series for detecting DoS attacks in NoC-based manycore systems. To fulfill this goal, it is necessary to extract traffic data from a manycore NoC and execute the learning techniques in the extracted data. However, while low-level platforms offer precision and slow execution, high-level platforms offer higher speed and data incompatible with reality. Therefore, a platform is being developed using the OVP tool, which has a higher level of abstraction. To solve the low precision problem, the developed platform will have its data validated with a low-level platform.