OpenRaiser/Intern-Atlas huggingface dataset:
Triangle Detection in H-Free Graphs
Venue: arXiv cs.DS 2025
Year: 2025

We initiate the study of combinatorial algorithms for Triangle Detection in $H$-free graphs. The goal is to decide if a graph that forbids a fixed pattern $H$ as a subgraph contains a triangle, using only "combinatorial" methods that notably exclude fast matrix multiplication. Our work aims to classify which patterns admit a subcubic speedup, working towards a dichotomy theorem. On the lower bound side, we show that if $H$ is not $3$-colorable or contains more than one triangle, the complexity of the problem remains unchanged, and no combinatorial speedup is likely possible. On the upper bound side, we develop an embedding approach that results in a strongly subcubic, combinatorial algorithm for a rich class of "embeddable" patterns. Specifically, for an embeddable pattern of size $k$, our algorithm runs in $\tilde O(n^{3-\frac{1}{2^{k-3}}})$ time, where $\tilde O(\cdot)$ hides poly-logarithmic factors. This algorithm also extends to listing all the triangles within the same time bound. We supplement this main result with two generalizations: 1) A generalization to patterns that are embeddable up to a single obstacle that arises from a triangle in the pattern. This completes our classification for small patterns, yielding a dichotomy theorem for all patterns of size up to eight. 2) An $H$-sensitive algorithm for embeddable patterns, which runs faster when the number of copies of $H$ is significantly smaller than the maximum possible $Ω(n^k)$. Finally, we focus on the special case of odd cycles. We present specialized Triangle Detection algorithms that are very efficient: 1) A combinatorial algorithm for $C_{2k+1}$-free graphs that runs in $\tilde O(m+n^{1+2/k})$ time for every $k\geq2$, where $m$ is the number of edges in the graph. 2) A combinatorial $C_5$-sensitive algorithm that runs in $\tilde O(n^2+n^{4/3}t^{1/3})$ time, where $t$ is the number of $5$-cycles in the graph.
Social-Media Based Personas Challenge: Hybrid Prediction of Common and Rare User Actions on Bluesky
Venue: arXiv cs.CL 2025
Year: 2025

Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at COLM 2025.
Persistent BitTorrent Trackers
Venue: arXiv cs.CR 2025
Year: 2025

Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down (whether by operator choice, technical failure, or legal action) users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with cryptographic attestations. Receiving peers sign receipts for transferred pieces, which the tracker aggregates and verifies before updating on-chain reputation. Trackers run in Trusted Execution Environments (TEEs) to guarantee correct aggregation and prevent manipulation of state. If a tracker is unavailable, peers use an authenticated Distributed Hash Table (DHT) for discovery: the on-chain reputation acts as a Public Key Infrastructure (PKI), so peers can verify each other and maintain access control without the tracker. This design persists reputation across tracker failures and makes it portable to new instances through single-hop migration in factory-deployed contracts. We formalize the security requirements, prove correctness under standard cryptographic assumptions, and evaluate a prototype on Intel TDX. Measurements show that transfer receipts adds less than 6\% overhead with typical piece sizes, and signature aggregation speeds up verification by $2.5\times$.
Leveraging CVAE for Joint Configuration Estimation of Multifingered Grippers from Point Cloud Data
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents an efficient approach for determining the joint configuration of a multifingered gripper solely from the point cloud data of its poly-articulated chain, as generated by visual sensors, simulations or even generative neural networks. Well-known inverse kinematics (IK) techniques can provide mathematically exact solutions (when they exist) for joint configuration determination based solely on the fingertip pose, but often require post-hoc decision-making by considering the positions of all intermediate phalanges in the gripper's fingers, or rely on algorithms to numerically approximate solutions for more complex kinematics. In contrast, our method leverages machine learning to implicitly overcome these challenges. This is achieved through a Conditional Variational Auto-Encoder (CVAE), which takes point cloud data of key structural elements as input and reconstructs the corresponding joint configurations. We validate our approach on the MultiDex grasping dataset using the Allegro Hand, operating within 0.05 milliseconds and achieving accuracy comparable to state-of-the-art methods. This highlights the effectiveness of our pipeline for joint configuration estimation within the broader context of AI-driven techniques for grasp planning.
Framework Matters: Energy Efficiency of UI Automation Testing Frameworks
Venue: arXiv cs.SE 2025
Year: 2025

We examine per action energy consumption across four web user interface (UI) automation testing frameworks to determine whether consistent tendencies can guide energy-aware test design. Using a controlled client-server setup with external power metering, we repeat each UI action (refresh, click variants, checkbox, drag&drop, input-text, scroll) 35 times. Across each of the actions, energy costs vary by both framework and action. Puppeteer is the most efficient for left-click, right-click, double-click, checkbox, and input-text; Selenium is the most efficient for refresh and scroll; Nightwatch is generally the least energy efficient. The energy cost of performing the same action varied by up to a factor of six depending on the framework. This indicates that providing transparency of energy consumption for UI automation testing frameworks allows developers to make informed, energy-aware decisions when testing a specific UI action.
FORWARD: Dataset of a forwarder operating in rough terrain
Venue: arXiv cs.RO 2025
Year: 2025

We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The vehicle was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, vehicle position with centimeter accuracy, and crane use while the vehicle operates in forest areas, aerially laser-scanned with a resolution of around 1500 points per square meter. Production log files (StanForD standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weights, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding or handling obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field.
Loomis Painter: Reconstructing the Painting Process
Venue: arXiv cs.CV 2025
Year: 2025

Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they struggle to generalize across media and often show temporal or structural inconsistencies, hindering faithful reproduction of human creative workflows. To address this, we propose a unified framework for multi-media painting process generation with a semantics-driven style control mechanism that embeds multiple media into a diffusion models conditional space and uses cross-medium style augmentation. This enables consistent texture evolution and process transfer across styles. A reverse-painting training strategy further ensures smooth, human-aligned generation. We also build a large-scale dataset of real painting processes and evaluate cross-media consistency, temporal coherence, and final-image fidelity, achieving strong results on LPIPS, DINO, and CLIP metrics. Finally, our Perceptual Distance Profile (PDP) curve quantitatively models the creative sequence, i.e., composition, color blocking, and detail refinement, mirroring human artistic progression.
Anomaly Pattern-guided Transaction Bug Testing in Relational Databases
Venue: arXiv cs.DB 2025
Year: 2025

Concurrent transaction processing is a fundamental capability of Relational Database Management Systems (RDBMSs), widely utilized in applications requiring high levels of parallel user interaction, such as banking systems, e-commerce platforms, and telecommunications infrastructure. Isolation levels offer a configurable mechanism to manage the interaction between concurrent transactions, enabling varying degrees of consistency and performance trade-offs. These isolation guarantees are supported by all major RDBMSs. However, testing transaction behavior under different isolation levels remains a significant challenge due to two primary reasons. First, automatically generating test transactions that can effectively expose bugs in transaction handling logic is non-trivial, as such bugs are typically triggered under specific transactional constraints. Second, detecting logic anomalies in transaction outcomes is difficult because the correct execution results are often unknown for randomly generated transactions. To address these challenges, we propose an anomaly pattern-guided testing approach for uncovering transaction bugs in RDBMSs. Our solution tackles the first challenge by introducing a test case generation technique guided by predefined anomaly patterns, which increases the likelihood of exposing transactional bugs. For the second challenge, we present a two-phase detection process, involving explicit error detection and implicit error detection, to identify bugs in transaction execution. We have implemented our approach in a tool, APTrans, and evaluated it on three widely-used RDBMSs: MySQL, MariaDB, and OceanBase. APTrans successfully identified 13 previously unknown transaction-related bugs, 11 of which have been confirmed by the respective development teams.
IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
Venue: arXiv cs.RO 2025
Year: 2025

While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks largely focus on passive, static household environments and evaluate only isolated capabilities, failing to capture holistic performance in dynamic, real-world complexity. To fill this gap, we present IndustryNav, the first dynamic industrial navigation benchmark for active spatial reasoning. IndustryNav leverages 12 manually created, high-fidelity Unity warehouse scenarios featuring dynamic objects and human movement. Our evaluation employs a PointGoal navigation pipeline that effectively combines egocentric vision with global odometry to assess holistic local-global planning. Crucially, we introduce the "collision rate" and "warning rate" metrics to measure safety-oriented behaviors and distance estimation. A comprehensive study of nine state-of-the-art VLLMs (including models such as GPT-5-mini, Claude-4.5, and Gemini-2.5) reveals that closed-source models maintain a consistent advantage; however, all agents exhibit notable deficiencies in robust path planning, collision avoidance and active exploration. This highlights a critical need for embodied research to move beyond passive perception and toward tasks that demand stable planning, active exploration, and safe behavior in dynamic, real-world environment.
Selective Rotary Position Embedding
Venue: arXiv cs.CL 2025
Year: 2025

Position information is essential for language modeling. In softmax transformers, Rotary Position Embeddings (\textit{RoPE}) encode positions through \textit{fixed-angle} rotations, while in linear transformers, order is handled via input-dependent (selective) gating that decays past key-value associations. Selectivity has generally been shown to improve language-related tasks. Inspired by this, we introduce \textit{Selective RoPE}, an \textit{input-dependent} rotary embedding mechanism, that generalizes \textit{RoPE}, and enables rotation in \textit{arbitrary angles} for both linear and softmax transformers. We show that softmax attention already performs a hidden form of these rotations on query-key pairs, uncovering an implicit positional structure. We further show that in state-space models and gated linear transformers, the real part manages forgetting while the imaginary part encodes positions through rotations. We validate our method by equipping gated transformers with \textit{Selective RoPE}, demonstrating that its input-dependent rotations improve performance in language modeling and on difficult sequence tasks like copying, state tracking, and retrieval.
Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Face verification is a significant component of identity authentication in various applications including online banking and secure access to personal devices. The majority of the existing face image datasets often suffer from notable biases related to race, gender, and other demographic characteristics, limiting the effectiveness and fairness of face verification systems. In response to these challenges, we propose a comprehensive methodology that integrates advanced generative models to create varied and diverse high-quality synthetic face images. This methodology emphasizes the representation of a diverse range of facial traits, ensuring adherence to characteristics permissible in identity card photographs. Furthermore, we introduce the Diverse and Inclusive Faces for Verification (DIF-V) dataset, comprising 27,780 images of 926 unique identities, designed as a benchmark for future research in face verification. Our analysis reveals that existing verification models exhibit biases toward certain genders and races, and notably, applying identity style modifications negatively impacts model performance. By tackling the inherent inequities in existing datasets, this work not only enriches the discussion on diversity and ethics in artificial intelligence but also lays the foundation for developing more inclusive and reliable face verification technologies
Sparse Mixture-of-Experts for Multi-Channel Imaging: Are All Channel Interactions Required?
Venue: arXiv cs.CV 2025
Year: 2025

Vision Transformers ($\text{ViTs}$) have become the backbone of vision foundation models, yet their optimization for multi-channel domains - such as cell painting or satellite imagery - remains underexplored. A key challenge in these domains is capturing interactions between channels, as each channel carries different information. While existing works have shown efficacy by treating each channel independently during tokenization, this approach naturally introduces a major computational bottleneck in the attention block - channel-wise comparisons leads to a quadratic growth in attention, resulting in excessive $\text{FLOPs}$ and high training cost. In this work, we shift focus from efficacy to the overlooked efficiency challenge in cross-channel attention and ask: "Is it necessary to model all channel interactions?". Inspired by the philosophy of Sparse Mixture-of-Experts ($\text{MoE}$), we propose MoE-ViT, a Mixture-of-Experts architecture for multi-channel images in $\text{ViTs}$, which treats each channel as an expert and employs a lightweight router to select only the most relevant experts per patch for attention. Proof-of-concept experiments on real-world datasets - JUMP-CP and So2Sat - demonstrate that $\text{MoE-ViT}$ achieves substantial efficiency gains without sacrificing, and in some cases enhancing, performance, making it a practical and attractive backbone for multi-channel imaging.
Fluid Antenna System-Enabled UAV-to-Ground Communications
Venue: arXiv cs.IT 2025
Year: 2025

Fluid antenna systems (FAS) have emerged as a revolutionary technology offering enhanced spatial diversity within a compact form factor. Concurrently, unmanned aerial vehicles (UAVs) are integral to future networks, necessitating channel models that capture both multipath fading and shadowing. This letter presents a novel performance analysis of a UAV-to-ground link, where the receiver is equipped with an $N$-port FAS operating over the challenging double-shadowing fading channel. By adapting a tractable eigenvalue-based approximation for the correlated FAS ports, we derive new analytical expressions for the end-to-end signal-to-noise ratio statistics, namely the cumulative distribution function and the probability density function. Based on these statistics, we present exact integral expressions for the outage probability, average bit error rate, and average channel capacity. We further derive new, tractable closed-form solutions for the average bit error rate and capacity for the practical dual-rank, independent but non-identically distributed case. Finally, a key asymptotic analysis reveals that the system achieves a multiplicative diversity order of $G_d = M \times d$, which is precisely the product of the FAS spatial rank $M$ and the intrinsic channel diversity order $d$. Simulation results are provided to validate the high accuracy of our entire theoretical framework.
CREST: Improving Interpretability and Effectiveness of Troubleshooting at Ericsson through Criterion-Specific Trouble Report Retrieval
Venue: arXiv cs.SE 2025
Year: 2025

The rapid evolution of the telecommunication industry necessitates efficient troubleshooting processes to maintain network reliability, software maintainability, and service quality. Trouble Reports (TRs), which document issues in Ericsson's production system, play a critical role in facilitating the timely resolution of software faults. However, the complexity and volume of TR data, along with the presence of diverse criteria that reflect different aspects of each fault, present challenges for retrieval systems. Building on prior work at Ericsson, which utilized a two-stage workflow, comprising Initial Retrieval (IR) and Re-Ranking (RR) stages, this study investigates different TR observation criteria and their impact on the performance of retrieval models. We propose \textbf{CREST} (\textbf{C}riteria-specific \textbf{R}etrieval via \textbf{E}nsemble of \textbf{S}pecialized \textbf{T}R models), a criterion-driven retrieval approach that leverages specialized models for different TR fields to improve both effectiveness and interpretability, thereby enabling quicker fault resolution and supporting software maintenance. CREST utilizes specialized models trained on specific TR criteria and aggregates their outputs to capture diverse and complementary signals. This approach leads to enhanced retrieval accuracy, better calibration of predicted scores, and improved interpretability by providing relevance scores for each criterion, helping users understand why specific TRs were retrieved. Using a subset of Ericsson's internal TRs, this research demonstrates that criterion-specific models significantly outperform a single model approach across key evaluation metrics. This highlights the importance of all targeted criteria used in this study for optimizing the performance of retrieval systems.
DS-Span: Single-Phase Discriminative Subgraph Mining for Efficient Graph Embeddings
Venue: arXiv cs.LG 2025
Year: 2025

Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraph-based methods provide an interpretable bridge between symbolic pattern discovery and continuous embedding learning. Yet, existing frequent or discriminative subgraph mining approaches often suffer from redundant multi-phase pipelines, high computational cost, and weak coupling between mined structures and their discriminative relevance. We propose DS-Span, a single-phase discriminative subgraph mining framework that unifies pattern growth, pruning, and supervision-driven scoring within one traversal of the search space. DS-Span introduces a coverage-capped eligibility mechanism that dynamically limits exploration once a graph is sufficiently represented, and an information-gain-guided selection that promotes subgraphs with strong class-separating ability while minimizing redundancy. The resulting subgraph set serves as an efficient, interpretable basis for downstream graph embedding and classification. Extensive experiments across benchmarks demonstrate that DS-Span generates more compact and discriminative subgraph features than prior multi-stage methods, achieving higher or comparable accuracy with significantly reduced runtime. These results highlight the potential of unified, single-phase discriminative mining as a foundation for scalable and interpretable graph representation learning.