OpenRaiser/Intern-Atlas huggingface dataset:
Generative Large Language Models for Knowledge Representation: A Systematic Review of Concept Map Generation
Venue: arXiv cs.CY 2025
Year: 2025

The rise of generative large language models (LLMs) has opened new opportunities for automating knowledge representation through concept maps, a long-standing pedagogical tool valued for fostering meaningful learning and higher-order thinking. Traditional construction of concept maps is labor-intensive, requiring significant expertise and time, limiting their scalability in education. This review systematically synthesizes the emerging body of research on LLM-enabled concept map generation, focusing on two guiding questions: (a) What methods and technical features of LLMs are employed to construct concept maps? (b) What empirical evidence exists to validate their educational utility? Through a comprehensive search across major databases and AI-in-education conference proceedings, 28 studies meeting rigorous inclusion criteria were analyzed using thematic synthesis. Findings reveal six major methodological categories: human-in-the-loop systems, weakly supervised learning models, fine-tuned domain-specific LLMs, pre-trained LLMs with prompt engineering, hybrid systems integrating knowledge bases, and modular frameworks combining symbolic and statistical tools. Validation strategies ranged from quantitative metrics (precision, recall, F1-score, semantic similarity) to qualitative evaluations (expert review, learner feedback). Results indicate LLM-generated maps hold promise for scalable, adaptive, and pedagogically relevant knowledge visualization, though challenges remain regarding validity, interpretability, multilingual adaptability, and classroom integration. Future research should prioritize interdisciplinary co-design, empirical classroom trials, and alignment with instructional practices to realize their full educational potential.
Adaptive and Iterative Point Cloud Denoising with Score-Based Diffusion Model
Venue: arXiv cs.CV 2025
Year: 2025

Point cloud denoising task aims to recover the clean point cloud from the scanned data coupled with different levels or patterns of noise. The recent state-of-the-art methods often train deep neural networks to update the point locations towards the clean point cloud, and empirically repeat the denoising process several times in order to obtain the denoised results. It is not clear how to efficiently arrange the iterative denoising processes to deal with different levels or patterns of noise. In this paper, we propose an adaptive and iterative point cloud denoising method based on the score-based diffusion model. For a given noisy point cloud, we first estimate the noise variation and determine an adaptive denoising schedule with appropriate step sizes, then invoke the trained network iteratively to update point clouds following the adaptive schedule. To facilitate this adaptive and iterative denoising process, we design the network architecture and a two-stage sampling strategy for the network training to enable feature fusion and gradient fusion for iterative denoising. Compared to the state-of-the-art point cloud denoising methods, our approach obtains clean and smooth denoised point clouds, while preserving the shape boundary and details better. Our results not only outperform the other methods both qualitatively and quantitatively, but also are preferable on the synthetic dataset with different patterns of noises, as well as the real-scanned dataset.
Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
Venue: arXiv cs.RO 2025
Year: 2025

This study presents a system integration approach for planning schedules, sequences, tasks, and motions for reconfigurable robots to automatically disassemble constrained structures in a non-destructive manner. Such systems must adapt their configuration and coordination to the target structure, but the large and complex search space makes them prone to local optima. To address this, we integrate multiple robot arms equipped with different types of tools, together with a rotary stage, into a reconfigurable setup. This flexible system is based on a hierarchical optimization method that generates plans meeting multiple preferred conditions under mandatory requirements within a realistic timeframe. The approach employs two many-objective genetic algorithms for sequence and task planning with motion evaluations, followed by constraint programming for scheduling. Because sequence planning has a much larger search space, we introduce a chromosome initialization method tailored to constrained structures to mitigate the risk of local optima. Simulation results demonstrate that the proposed method effectively solves complex problems in reconfigurable robotic disassembly.
TypedSchematics: A Block-based PCB Design Tool with Real-time Detection of Common Connection Errors
Venue: arXiv cs.HC 2025
Year: 2025

Within PCB design, the reuse of circuit design blocks is a major preventing factor inhibiting beginners from reusing designs made by experts, a common practice in software but non-existent in circuit design at large. Despite efforts to improve reusability (e.g. block-based PCB design) by platforms such as SparkFun ALC and Altium Upverter, they lack merging techniques that safely guide users in connecting different circuit blocks without requiring assistance from third-party engineers. In this paper, we propose TypedSchematics, a block-based standalone PCB design tool that supports beginners create their own PCBs by providing a language syntax for typing circuit blocks with circuit data that addresses multiple challenges, from real-time detection of connection errors to automated composition and user-scalable libraries of circuit blocks. Through a user study, we demonstrate TypedSchematics improvements in design support for merging circuit blocks compared to Fusion 360. Three PCBs designed with TypedSchematics further showcase our tool capabilities, one designed by high school students demonstrates the potential of TypedSchematics to significantly lower the PCB design skill-floor.
What Gets Measured Gets Managed: Mitigating Supply Chain Attacks with a Link Integrity Management System
Venue: arXiv cs.CR 2025
Year: 2025

The web continues to grow, but dependency-monitoring tools and standards for resource integrity lag behind. Currently, there exists no robust method to verify the integrity of web resources, much less in a generalizable yet performant manner, and supply chains remain one of the most targeted parts of the attack surface of web applications. In this paper, we present the design of LiMS, a transparent system to bootstrap link integrity guarantees in web browsing sessions with minimal overhead. At its core, LiMS uses a set of customizable integrity policies to declare the (un)expected properties of resources, verifies these policies, and enforces them for website visitors. We discuss how basic integrity policies can serve as building blocks for a comprehensive set of integrity policies, while providing guarantees that would be sufficient to defend against recent supply chain attacks detailed by security industry reports. Finally, we evaluate our open-sourced prototype by simulating deployments on a representative sample of 450 domains that are diverse in ranking and category. We find that our proposal offers the ability to bootstrap marked security improvements with an overall overhead of hundreds of milliseconds on initial page loads, and negligible overhead on reloads, regardless of network speeds. In addition, from examining archived data for the sample sites, we find that several of the proposed policy building blocks suit their dependency usage patterns, and would incur minimal administrative overhead.
Efficient Algorithms for Disjoint Shortest Paths Problem and its Extensions
Venue: arXiv cs.DS 2025
Year: 2025

We study the 2-Disjoint Shortest Paths (2-DSP) problem: given a directed weighted graph and two terminal pairs $(s_1,t_1)$ and $(s_2,t_2)$, decide whether there exist vertex-disjoint shortest paths between each pair. Building on recent advances in disjoint shortest paths for DAGs and undirected graphs (Akmal et al. 2024), we present an $O(mn \log n)$ time algorithm for this problem in weighted directed graphs that do not contain negative or zero weight cycles. This algorithm presents a significant improvement over the previously known $O(m^5n)$ time bound (Berczi et al. 2017). Our approach exploits the algebraic structure of polynomials that enumerate shortest paths between terminal pairs. A key insight is that these polynomials admit a recursive decomposition, enabling efficient evaluation via dynamic programming over fields of characteristic two. Furthermore, we demonstrate how to report the corresponding paths in $O(mn^2 \log n)$ time. In addition, we extend our techniques to a more general setting: given two terminal pairs $(s_1, t_1)$ and $(s_2, t_2)$ in a directed graph, find the minimum possible number of vertex intersections between any shortest path from $s_1$ to $t_1$ and $s_2$ to $t_2$. We call this the Minimum 2-Disjoint Shortest Paths (Min-2-DSP) problem. We provide in this paper the first efficient algorithm for this problem, including an $O(m^2 n^3)$ time algorithm for directed graphs with positive edge weights, and an $O(m+n)$ time algorithm for DAGs and undirected graphs. Moreover, if the number of intersecting vertices is at least one, we show that it is possible to report the paths in the same $O(m+n)$ time. This is somewhat surprising, as there is no known $o(mn)$ time algorithm for explicitly reporting the paths if they are vertex-disjoint, and is left as an open problem in (Akmal et al. 2024).
ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System
Venue: arXiv cs.CR 2025
Year: 2025

We present ATLANTIS, the cyber reasoning system developed by Team Atlanta that won 1st place in the Final Competition of DARPA's AI Cyber Challenge (AIxCC) at DEF CON 33 (August 2025). AIxCC (2023-2025) challenged teams to build autonomous cyber reasoning systems capable of discovering and patching vulnerabilities at the speed and scale of modern software. ATLANTIS integrates large language models (LLMs) with program analysis -- combining symbolic execution, directed fuzzing, and static analysis -- to address limitations in automated vulnerability discovery and program repair. Developed by researchers at Georgia Institute of Technology, Samsung Research, KAIST, and POSTECH, the system addresses core challenges: scaling across diverse codebases from C to Java, achieving high precision while maintaining broad coverage, and producing semantically correct patches that preserve intended behavior. We detail the design philosophy, architectural decisions, and implementation strategies behind ATLANTIS, share lessons learned from pushing the boundaries of automated security when program analysis meets modern AI, and release artifacts to support reproducibility and future research.
Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking
Venue: arXiv cs.LG 2025
Year: 2025

Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant privacy risks, especially from data reconstruction attacks that recover original inputs from intermediate representations. Existing defenses using noise injection often degrade model performance. To overcome these challenges, we present PM-SFL, a scalable and privacy-preserving SFL framework that incorporates Probabilistic Mask training to add structured randomness without relying on explicit noise. This mitigates data reconstruction risks while maintaining model utility. To address data heterogeneity, PM-SFL employs personalized mask learning that tailors submodel structures to each client's local data. For system heterogeneity, we introduce a layer-wise knowledge compensation mechanism, enabling clients with varying resources to participate effectively under adaptive model splitting. Theoretical analysis confirms its privacy protection, and experiments on image and wireless sensing tasks demonstrate that PM-SFL consistently improves accuracy, communication efficiency, and robustness to privacy attacks, with particularly strong performance under data and system heterogeneity.
HDC-X: Efficient Medical Data Classification for Embedded Devices
Venue: arXiv cs.LG 2025
Year: 2025

Energy-efficient medical data classification is essential for modern disease screening, particularly in home and field healthcare where embedded devices are prevalent. While deep learning models achieve state-of-the-art accuracy, their substantial energy consumption and reliance on GPUs limit deployment on such platforms. We present HDC-X, a lightweight classification framework designed for low-power devices. HDC-X encodes data into high-dimensional hypervectors, aggregates them into multiple cluster-specific prototypes, and performs classification through similarity search in hyperspace. We evaluate HDC-X across three medical classification tasks; on heart sound classification, HDC-X is $350\times$ more energy-efficient than Bayesian ResNet with less than 1% accuracy difference. Moreover, HDC-X demonstrates exceptional robustness to noise, limited training data, and hardware error, supported by both theoretical analysis and empirical results, highlighting its potential for reliable deployment in real-world settings. Code is available at https://github.com/jianglanwei/HDC-X.
Chameleon: Integrated Sensing and Communication with Sub-Symbol Beam Switching in mmWave Networks
Venue: arXiv cs.NI 2025
Year: 2025

Next-generation cellular networks are envisioned to integrate sensing capabilities with communication, particularly in the millimeter-wave (mmWave) spectrum, where beamforming using large-scale antenna arrays enables directional signal transmissions for improved spatial multiplexing. In current 5G networks, however, beamforming is typically designed either for communication or sensing (e.g., beam training during link establishment). In this paper, we present Chameleon, a novel framework that augments and rapidly switches beamformers during each demodulation reference signal (DMRS) symbol to achieve integrated sensing and communication (ISAC) in 5G mmWave networks. Each beamformer introduces an additional sensing beam toward target angles while maintaining the communication beams toward multiple users. We implement Chameleon on a 28 GHz software-defined radio testbed supporting over-the-air 5G physical downlink shared channel (PDSCH) transmissions. Extensive experiments in open environments show that Chameleon achieves multi-user communication with a sum data rate of up to 0.80 Gbps across two users. Simultaneously, Chameleon employs a beamformer switching interval of only 0.24 μs, therefore producing a 31x31-point 2D imaging within just 0.875 ms. Leveraging machine learning, Chameleon further enables object localization with median errors of 0.14 m (distance) and 0.24° (angle), and material classification with 99.0% accuracy.
SWE-QA: Can Language Models Answer Repository-level Code Questions?
Venue: arXiv cs.CL 2025
Year: 2025

Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 576 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 11 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. As a prototype application, we further develop SWE-QA-Agent, an agentic framework in which LLM agents reason and act to find answers automatically. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs, particularly our SWE-QA-Agent framework, in addressing repository-level QA, while also revealing open challenges and pointing to future research directions.
Chameleon: A Surface-Anchored Smartphone AR Prototype with Visually Blended Mobile Display
Venue: arXiv cs.HC 2025
Year: 2025

Augmented reality (AR) is often realized through head-mounted displays, offering immersive but egocentric experiences. While smartphone-based AR is more accessible, it remains limited to handheld, single-user interaction. We introduce Chameleon, a prototype AR system that transforms smartphones into surface-anchored displays for co-located use. When placed flat, the phone creates a transparency illusion and anchors digital content visible to multiple users. Chameleon supports natural repositioning on the surface without external hardware by combining two techniques: (1) Background Acquisition uses opportunistic sensing and language model-assisted pattern generation to blend with surrounding surfaces, and (2) Real-Time Position Tracking augments inertial sensing to maintain spatial stability. This work shows how lightweight sensing can support casual, collaborative AR experiences using existing devices.
AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production
Venue: arXiv cs.AI 2025
Year: 2025

With the growing adoption of Large Language Models (LLMs) in automating complex, multi-agent workflows, organizations face mounting risks from errors, emergent behaviors, and systemic failures that current evaluation methods fail to capture. We present AgentCompass, the first evaluation framework designed specifically for post-deployment monitoring and debugging of agentic workflows. AgentCompass models the reasoning process of expert debuggers through a structured, multi-stage analytical pipeline: error identification and categorization, thematic clustering, quantitative scoring, and strategic summarization. The framework is further enhanced with a dual memory system-episodic and semantic-that enables continual learning across executions. Through collaborations with design partners, we demonstrate the framework's practical utility on real-world deployments, before establishing its efficacy against the publicly available TRAIL benchmark. AgentCompass achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations, underscoring its role as a robust, developer-centric tool for reliable monitoring and improvement of agentic systems in production.
On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code
Venue: arXiv cs.SE 2025
Year: 2025

Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write/execute the actual code with minimal human intervention. Key to this process are agent manifests, configuration files (such as Claude.md) that provide agents with essential project context, identity, and operational rules. However, the lack of comprehensive and accessible documentation for creating these manifests presents a significant challenge for developers. We analyzed 253 Claude.md files from 242 repositories to identify structural patterns and common content. Our findings show that manifests typically have shallow hierarchies with one main heading and several subsections, with content dominated by operational commands, technical implementation notes, and high-level architecture.
Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks
Venue: arXiv cs.CV 2025
Year: 2025

Finding smell references in historic artworks is a challenging problem. Beyond artwork-specific challenges such as stylistic variations, their recognition demands exceptionally detailed annotation classes, resulting in annotation sparsity and extreme class imbalance. In this work, we explore the potential of synthetic data generation to alleviate these issues and enable accurate detection of smell-related objects. We evaluate several diffusion-based augmentation strategies and demonstrate that incorporating synthetic data into model training can improve detection performance. Our findings suggest that leveraging the large-scale pretraining of diffusion models offers a promising approach for improving detection accuracy, particularly in niche applications where annotations are scarce and costly to obtain. Furthermore, the proposed approach proves to be effective even with relatively small amounts of data, and scaling it up provides high potential for further enhancements.
LEAP: LLM Inference on Scalable PIM-NoC Architecture with Balanced Dataflow and Fine-Grained Parallelism
Venue: arXiv cs.AR 2025
Year: 2025

Large language model (LLM) inference has been a prevalent demand in daily life and industries. The large tensor sizes and computing complexities in LLMs have brought challenges to memory, computing, and databus. This paper proposes a computation/memory/communication co-designed non-von Neumann accelerator by aggregating processing-in-memory (PIM) and computational network-on-chip (NoC), termed LEAP. The matrix multiplications in LLMs are assigned to PIM or NoC based on the data dynamicity to maximize data locality. Model partition and mapping are optimized by heuristic design space exploration. Dedicated fine-grained parallelism and tiling techniques enable high-throughput dataflow across the distributed resources in PIM and NoC. The architecture is evaluated on Llama 1B/8B/13B models and shows $\sim$2.55$\times$ throughput (tokens/sec) improvement and $\sim$71.94$\times$ energy efficiency (tokens/Joule) boost compared to the A100 GPU.
The Complexity of Finding and Counting Subtournaments
Venue: arXiv cs.CC 2025
Year: 2025

We study the complexity of counting and finding small tournament patterns inside large tournaments. Given a fixed tournament $T$ of order $k$, we write ${\#}\text{IndSub}_{\text{To}}(\{T\})$ for the problem whose input is a tournament $G$ and the task is to compute the number of subtournaments of $G$ that are isomorphic to $T$. Previously, Yuster [Yus25] obtained that ${\#}\text{IndSub}_{\text{To}}(\{T\})$ is hard to compute for random tournaments $T$. We consider a new approach that uses linear combinations of subgraph-counts [CDM17] to obtain a finer analysis of the complexity of ${\#}\text{IndSub}_{\text{To}}(\{T\})$. We show that for all tournaments $T$ of order $k$ the problem ${\#}\text{IndSub}_{\text{To}}(\{T\})$ is always at least as hard as counting $\lfloor 3k/4 \rfloor$-cliques. This immediately yields tight bounds under ETH. Further, we consider the parameterized version of ${\#}\text{IndSub}_{\text{To}}(\mathcal{T})$ where we only consider patterns $T \in \mathcal{T}$ and that is parameterized by the pattern size $|V(T)|$. We show that ${\#}\text{IndSub}_{\text{To}}(\mathcal{T})$ is ${\#}W[1]$-hard as long as $\mathcal{T}$ contains infinitely many tournaments.