OpenRaiser/Intern-Atlas huggingface dataset:
SQLGovernor: An LLM-powered SQL Toolkit for Real World Application
Venue: arXiv cs.DB 2025
Year: 2025

SQL queries in real world analytical environments, whether written by humans or generated automatically often suffer from syntax errors, inefficiency, or semantic misalignment, especially in complex OLAP scenarios. To address these challenges, we propose SQLGovernor, an LLM powered SQL toolkit that unifies multiple functionalities, including syntax correction, query rewriting, query modification, and consistency verification within a structured framework enhanced by knowledge management. SQLGovernor introduces a fragment wise processing strategy to enable fine grained rewriting and localized error correction, significantly reducing the cognitive load on the LLM. It further incorporates a hybrid self learning mechanism guided by expert feedback, allowing the system to continuously improve through DBMS output analysis and rule validation. Experiments on benchmarks such as BIRD and BIRD CRITIC, as well as industrial datasets, show that SQLGovernor consistently boosts the performance of base models by up to 10%, while minimizing reliance on manual expertise. Deployed in production environments, SQLGovernor demonstrates strong practical utility and effective performance.
Visual Analysis of Time-Dependent Observables in Cell Signaling Simulations
Venue: arXiv cs.HC 2025
Year: 2025

The ability of a cell to communicate with its environment is essential for key cellular functions like replication, metabolism, or cell fate decisions. The involved molecular mechanisms are highly dynamic and difficult to capture experimentally. Simulation studies offer a valuable means for exploring and predicting how cell signaling processes unfold. We present a design study on the visual analysis of such studies to support 1) modelers in calibrating model parameters such that the simulated signal responses over time reflect reference behavior from cell biology research and 2) cell biologists in exploring the influence of receptor trafficking on the efficiency of signal transmission within the cell. We embed time series plots into parallel coordinates to enable a simultaneous analysis of model parameters and temporal outputs. A usage scenario illustrates how our approach assists with typical tasks such as assessing the plausibility of temporal outputs or their sensitivity across model configurations.
The Role of Legacy Mobile Networks in Infrastructure Resilience: Evidence from the Southern Brazil Flood
Venue: arXiv cs.NI 2025
Year: 2025

This paper investigates the resilience of mobile communication networks during the extreme flooding that affected Rio Grande do Sul, Brazil, in May 2024. Based on regulatory data and technical insights from operators, the study identifies the leading causes of mobile network disruptions, primarily related to flooding and prolonged power outages. The results reveal the significant vulnerability of modern networks (4G/5G) during the event and the essential role played by legacy technologies (2G/3G) in sustaining basic connectivity under adverse conditions. The findings underscore the necessity of disaster-aware infrastructure planning, taking into account the ongoing significance of legacy systems, diversified power supply strategies, and resilient network designs to enhance service continuity during future crises.
Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Venue: arXiv cs.LG 2025
Year: 2025

Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Raw acceleration signals were segmented into non-overlapping 10-second windows, and then a total of 104 handcrafted features were extracted. Four DL algorithms-Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Units (GRU), and One-Dimensional Convolutional Neural Network (1D-CNN)-were trained using raw acceleration signals and with handcrafted features extracted from these signals to predict 24-hour movement behavior categories. The handcrafted features were also used to train classical ML algorithms, namely Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Artificial Neural Network (ANN), and Decision Tree (DT) for classifying 24-hour movement behavior intensities. Results: LSTM, BiLSTM, and GRU showed an overall accuracy of approximately 85% when trained with raw acceleration signals, and 1D-CNN an overall accuracy of approximately 80%. When trained on handcrafted features, the overall accuracy for both DL and classical ML algorithms ranged from 70% to 81%. Overall, there was a higher confusion in classification of MVPA and LPA, compared to sleep and sedentary categories. Conclusion: DL methods with raw acceleration signals had only slightly better performance in predicting 24-hour movement behavior intensities, compared to when DL and classical ML were trained with handcrafted features.
A Cycle Walk for Sampling Measures on Spanning Forests for Redistricting
Venue: arXiv cs.SI 2025
Year: 2025

We introduce a new Markov Chain called the Cycle Walk for sampling measures of graph partitions where the partition elements have roughly equal size. Such Markov Chains are of current interest in the generation and evaluation of political districts. We present numerical evidence that this chain can efficiently sample target distributions that have been difficult for existing sampling Markov chains.
Memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE
Venue: arXiv cs.PF 2025
Year: 2025

We present memshare\footnote{The Software package is published as a CRAN package under https://CRAN.R-project.org/package=memshare, a package that enables shared memory multicore computation in R by allocating buffers in C++ shared memory and exposing them to R through ALTREP views. We compare memshare to SharedObject (Bioconductor) discuss semantics and safety, and report a 2x speedup over SharedObject with no additional resident memory in a column wise apply benchmark. Finally, we illustrate a downstream analytics use case: feature selection by mutual information in which densities are estimated per feature via Pareto Density Estimation (PDE). The analytical use-case is an RNA seq dataset consisting of N=10,446 cases and d=19,637 gene expressions requiring roughly n_threads * 10GB of memory in the case of using parallel R sessions. Such and larger use-cases are common in big data analytics and make R feel limiting sometimes which is mitigated by the addition of the library presented in this work.
Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations
Venue: arXiv cs.CR 2025
Year: 2025

As Large Language Model (LLM) agents become increasingly capable of automating complex, multi-step tasks, the need for robust, secure, and predictable architectural patterns is paramount. This paper provides a comprehensive guide to the ``Plan-then-Execute'' (P-t-E) pattern, an agentic design that separates strategic planning from tactical execution. We explore the foundational principles of P-t-E, detailing its core components - the Planner and the Executor - and its architectural advantages in predictability, cost-efficiency, and reasoning quality over reactive patterns like ReAct (Reason + Act). A central focus is placed on the security implications of this design, particularly its inherent resilience to indirect prompt injection attacks by establishing control-flow integrity. We argue that while P-t-E provides a strong foundation, a defense-in-depth strategy is necessary, and we detail essential complementary controls such as the Principle of Least Privilege, task-scoped tool access, and sandboxed code execution. To make these principles actionable, this guide provides detailed implementation blueprints and working code references for three leading agentic frameworks: LangChain (via LangGraph), CrewAI, and AutoGen. Each framework's approach to implementing the P-t-E pattern is analyzed, highlighting unique features like LangGraph's stateful graphs for re-planning, CrewAI's declarative tool scoping for security, and AutoGen's built-in Docker sandboxing. Finally, we discuss advanced patterns, including dynamic re-planning loops, parallel execution with Directed Acyclic Graphs (DAGs), and the critical role of Human-in-the-Loop (HITL) verification, to offer a complete strategic blueprint for architects, developers, and security engineers aiming to build production-grade, resilient, and trustworthy LLM agents.
Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network
Venue: arXiv cs.CV 2025
Year: 2025

Isolated Sign Language Recognition (ISLR) is challenged by gestures that are morphologically similar yet semantically distinct, a problem rooted in the complex interplay between hand shape and motion trajectory. Existing methods, often relying on a single reference frame, struggle to resolve this geometric ambiguity. This paper introduces Dual-SignLanguageNet (DSLNet), a dual-reference, dual-stream architecture that decouples and models gesture morphology and trajectory in separate, complementary coordinate systems. The architecture processes these streams through specialized networks: a topology-aware graph convolution models the view-invariant shape from a wrist-centric frame, while a Finsler geometry-based encoder captures the context-aware trajectory from a facial-centric frame. These features are then integrated via a geometry-driven optimal transport fusion mechanism. DSLNet sets a new state-of-the-art, achieving 93.70%, 89.97%, and 99.79% accuracy on the challenging WLASL-100, WLASL-300, and LSA64 datasets, respectively, with significantly fewer parameters than competing models.
Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data
Venue: arXiv cs.LG 2025
Year: 2025

\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliability of codes used for training and prediction. \textbf{Objective:} To develop a Signal Fidelity Index (SFI) quantifying diagnostic data quality at the patient level in dementia, and to test SFI-aware calibration for improving model performance across heterogeneous datasets without outcome labels. \textbf{Methods:} We built a simulation framework generating 2,500 synthetic datasets, each with 1,000 patients and realistic demographics, encounters, and coding patterns based on dementia risk factors. The SFI was derived from six interpretable components: diagnostic specificity, temporal consistency, entropy, contextual concordance, medication alignment, and trajectory stability. SFI-aware calibration applied a multiplicative adjustment, optimized across 50 simulation batches. \textbf{Results:} At the optimal parameter ($α$ = 2.0), SFI-aware calibration significantly improved all metrics (p $<$ 0.001). Gains ranged from 10.3\% for Balanced Accuracy to 32.5\% for Recall, with notable increases in Precision (31.9\%) and F1-score (26.1\%). Performance approached reference standards, with F1-score and Recall within 1\% and Balanced Accuracy and Detection Rate improved by 52.3\% and 41.1\%, respectively. \textbf{Conclusions:} Diagnostic signal decay is a tractable barrier to model generalization. SFI-aware calibration provides a practical, label-free strategy to enhance prediction across healthcare contexts, particularly for large-scale administrative datasets lacking outcome labels.
Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference
Venue: arXiv cs.AI 2025
Year: 2025

Multi-agent systems (MAS) are critical for automating complex tasks, yet their practical deployment is severely hampered by the challenge of failure attribution. Current diagnostic tools, which rely on statistical correlations, are fundamentally inadequate; on challenging benchmarks like Who\&When, state-of-the-art methods achieve less than 15\% accuracy in locating the root-cause step of a failure. To address this critical gap, we introduce the first failure attribution framework for MAS grounded in multi-granularity causal inference. Our approach makes two key technical contributions: (1) a performance causal inversion principle, which correctly models performance dependencies by reversing the data flow in execution logs, combined with Shapley values to accurately assign agent-level blame; (2) a novel causal discovery algorithm, CDC-MAS, that robustly identifies critical failure steps by tackling the non-stationary nature of MAS interaction data. The framework's attribution results directly fuel an automated optimization loop, generating targeted suggestions whose efficacy is validated via counterfactual simulations. Evaluations on the Who\&When and TRAIL benchmarks demonstrate a significant leap in performance. Our method achieves up to 36.2\% step-level accuracy. Crucially, the generated optimizations boost overall task success rates by an average of 22.4\%. This work provides a principled and effective solution for debugging complex agent interactions, paving the way for more reliable and interpretable multi-agent systems.
Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Venue: arXiv cs.LG 2025
Year: 2025

Identifying cortical regions critical for speech is essential for safe brain surgery in or near language areas. While Electrical Stimulation Mapping (ESM) remains the clinical gold standard, it is invasive and time-consuming. To address this, we analyzed intracranial electrocorticographic (ECoG) data from 16 participants performing speech tasks and developed machine learning models to directly predict if the brain region underneath each ECoG electrode is critical. Ground truth labels indicating speech arrest were derived independently from Electrical Stimulation Mapping (ESM) and used to train classification models. Our framework integrates neural activity signals, anatomical region labels, and functional connectivity features to capture both local activity and network-level dynamics. We found that models combining region and connectivity features matched the performance of the full feature set, and outperformed models using either type alone. To classify each electrode, trial-level predictions were aggregated using an MLP applied to histogram-encoded scores. Our best-performing model, a trial-level RBF-kernel Support Vector Machine together with MLP-based aggregation, achieved strong accuracy on held-out participants (ROC-AUC: 0.87, PR-AUC: 0.57). These findings highlight the value of combining spatial and network information with non-linear modeling to improve functional mapping in presurgical evaluation.
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Venue: arXiv cs.LG 2025
Year: 2025

Post-training language models (LMs) with reinforcement learning (RL) can enhance their complex reasoning capabilities without supervised fine-tuning, as demonstrated by DeepSeek-R1-Zero. However, effectively utilizing RL for LMs requires significant parallelization to scale-up inference, which introduces non-trivial technical challenges (e.g. latency, memory, and reliability) alongside ever-growing financial costs. We present Swarm sAmpling Policy Optimization (SAPO), a fully decentralized and asynchronous RL post-training algorithm. SAPO is designed for decentralized networks of heterogenous compute nodes, where each node manages its own policy model(s) while "sharing" rollouts with others in the network; no explicit assumptions about latency, model homogeneity, or hardware are required and nodes can operate in silo if desired. As a result, the algorithm avoids common bottlenecks in scaling RL post-training while also allowing (and even encouraging) new possibilities. By sampling rollouts "shared" across the network, it enables "Aha moments" to propagate, thereby bootstrapping the learning process. In this paper we show SAPO achieved cumulative reward gains of up to 94% in controlled experiments. We also share insights from tests on a network with thousands of nodes contributed by Gensyn community members running the algorithm on diverse hardware and models during an open-source demo.
X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates
Venue: arXiv cs.CL 2025
Year: 2025

Multi-turn-to-single-turn (M2S) compresses iterative red-teaming into one structured prompt, but prior work relied on a handful of manually written templates. We present X-Teaming Evolutionary M2S, an automated framework that discovers and optimizes M2S templates through language-model-guided evolution. The system pairs smart sampling from 12 sources with an LLM-as-judge inspired by StrongREJECT and records fully auditable logs. Maintaining selection pressure by setting the success threshold to $θ= 0.70$, we obtain five evolutionary generations, two new template families, and 44.8% overall success (103/230) on GPT-4.1. A balanced cross-model panel of 2,500 trials (judge fixed) shows that structural gains transfer but vary by target; two models score zero at the same threshold. We also find a positive coupling between prompt length and score, motivating length-aware judging. Our results demonstrate that structure-level search is a reproducible route to stronger single-turn probes and underscore the importance of threshold calibration and cross-model evaluation. Code, configurations, and artifacts are available at https://github.com/hyunjun1121/M2S-x-teaming.
DEQuify your force field: More efficient simulations using deep equilibrium models
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning force fields show great promise in enabling more accurate molecular dynamics simulations compared to manually derived ones. Much of the progress in recent years was driven by exploiting prior knowledge about physical systems, in particular symmetries under rotation, translation, and reflections. In this paper, we argue that there is another important piece of prior information that, thus fa,r hasn't been explored: Simulating a molecular system is necessarily continuous, and successive states are therefore extremely similar. Our contribution is to show that we can exploit this information by recasting a state-of-the-art equivariant base model as a deep equilibrium model. This allows us to recycle intermediate neural network features from previous time steps, enabling us to improve both accuracy and speed by $10\%-20\%$ on the MD17, MD22, and OC20 200k datasets, compared to the non-DEQ base model. The training is also much more memory efficient, allowing us to train more expressive models on larger systems.
SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit promising capabilities such as object recognition, common-sense reasoning, and contextual understanding-capabilities that align with the nuanced requirements of social robot navigation. However, it remains unclear whether VLMs can accurately understand complex social navigation scenes (e.g., inferring the spatial-temporal relations among agents and human intentions), which is essential for safe and socially compliant robot navigation. While some recent works have explored the use of VLMs in social robot navigation, no existing work systematically evaluates their ability to meet these necessary conditions. In this paper, we introduce the Social Navigation Scene Understanding Benchmark (SocialNav-SUB), a Visual Question Answering (VQA) dataset and benchmark designed to evaluate VLMs for scene understanding in real-world social robot navigation scenarios. SocialNav-SUB provides a unified framework for evaluating VLMs against human and rule-based baselines across VQA tasks requiring spatial, spatiotemporal, and social reasoning in social robot navigation. Through experiments with state-of-the-art VLMs, we find that while the best-performing VLM achieves an encouraging probability of agreeing with human answers, it still underperforms simpler rule-based approach and human consensus baselines, indicating critical gaps in social scene understanding of current VLMs. Our benchmark sets the stage for further research on foundation models for social robot navigation, offering a framework to explore how VLMs can be tailored to meet real-world social robot navigation needs. An overview of this paper along with the code and data can be found at https://larg.github.io/socialnav-sub .
Optimization of geometric hypergraph embedding
Venue: arXiv cs.SI 2025
Year: 2025

We consider the problem of embedding the nodes of a hypergraph into Euclidean space under the assumption that the interactions arose through closeness to unknown hyperedge centres. In this way, we tackle the inverse problem associated with the generation of geometric random hypergraphs. We propose two new spectral algorithms; both of these exploit the connection between hypergraphs and bipartite graphs. The assumption of an underlying geometric structure allows us to define a concrete measure of success that can be used to optimize the embedding via gradient descent. Synthetic tests show that this approach accurately reveals geometric structure that is planted in the data, and tests on real hypergraphs show that the approach is also useful for the downstream tasks of detecting spurious or missing data and node clustering.
Extended Version: Security and Privacy Perceptions of Pakistani Facebook Matrimony Group Users
Venue: arXiv cs.SI 2025
Year: 2025

In Pakistan, where dating apps are subject to censorship, Facebook matrimony groups -- also referred to as marriage groups -- serve as alternative virtual spaces for members to search for potential life partners. To participate in these groups, members often share sensitive personal information such as photos, addresses, and phone numbers, which exposes them to risks such as fraud, blackmail, and identity theft. To better protect users of Facebook matrimony groups, we need to understand aspects related to user safety, such as how users perceive risks, what influences their trust in sharing personal information, and how they navigate security and privacy concerns when seeking potential partners online. In this study, through 23 semi-structured interviews, we explore how Pakistani users of Facebook matrimony groups perceive and navigate risks of sharing personal information, and how cultural norms and expectations influence their behavior in these groups. We find elevated privacy concerns among participants, leading them to share limited personal information and creating mistrust among potential partners. Many also expressed concerns about the authenticity of profiles and major security risks, such as identity theft, harassment, and social judgment. Our work highlights the challenges of safely navigating Facebook matrimony groups in Pakistan and offers recommendations for such as implementing stronger identity verification by group admins, enforcing stricter cybersecurity laws, clear platform guidelines to ensure accountability, and technical feature enhancements -- including restricting screenshots, picture downloads, and implementing anonymous chats -- to protect user data and build trust.