OpenRaiser/Intern-Atlas huggingface dataset:
RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) have demonstrated powerful capabilities in general spatial understanding and reasoning. However, their fine-grained spatial understanding and reasoning capabilities in complex urban scenarios have not received significant attention in the fields of both research and industry. To fill this gap, we focus primarily on road markings as a typical example of fine-grained spatial elements under urban scenarios, given the essential role of the integrated road traffic network they form within cities. Around road markings and urban traffic systems, we propose RoadBench, a systematic benchmark that comprehensively evaluates MLLMs' fine-grained spatial understanding and reasoning capabilities using BEV and FPV image inputs. This benchmark comprises six tasks consisting of 9,121 strictly manually verified test cases. These tasks form a systematic evaluation framework that bridges understanding at local spatial scopes to global reasoning. They not only test MLLMs' capabilities in recognition, joint understanding, and reasoning but also assess their ability to integrate image information with domain knowledge. After evaluating 14 mainstream MLLMs, we confirm that RoadBench is a challenging benchmark for MLLMs while revealing significant shortcomings in existing MLLMs' fine-grained spatial understanding and reasoning capabilities within urban scenarios. In certain tasks, their performance even falls short of simple rule-based or random selection baselines. These findings, along with RoadBench itself, will contribute to the comprehensive advancement of spatial understanding capabilities for MLLMs. The benchmark code, example datasets, and raw evaluation results are available in the supplementary material.
Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retention is revisitation, i.e., when users return to view previously saved content, a behavior often sparked by personalized recommendations and user satisfaction. However, modeling and optimizing revisitation poses significant challenges. One core difficulty is accurate attribution: it is often unclear which specific user actions or content exposures trigger a revisit, since many confounding factors (e.g., content quality, user interface, notifications, or even changing user intent) can influence return behavior. Additionally, the scale and timing of revisitations introduce further complexity; users may revisit content days or even weeks after their initial interaction, requiring the system to maintain and associate extensive historical records across millions of users and sessions. These complexities render existing methods insufficient for robustly capturing and optimizing long-term revisitation. To address these gaps, we introduce a novel, lightweight, and interpretable framework for modeling revisitation behavior and optimizing long-term user retention in Pinterest's search-based recommendation context. By defining a surrogate attribution process that links saves to subsequent revisitations, we reduce noise in the causal relationship between user actions and return visits. Our scalable event aggregation pipeline enables large-scale analysis of user revisitation patterns and enhances the ranking system's ability to surface items with high retention value. Deployed on Pinterest's Related Pins surface to serve 500+ million users, the framework led to a significant lift of 0.1% in active users without additional computational costs.
A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS)
Venue: arXiv cs.CV 2025
Year: 2025

Cervical adenocarcinoma in situ (AIS) is a critical premalignant lesion whose accurate histopathological diagnosis is challenging. Early detection is essential to prevent progression to invasive cervical adenocarcinoma. In this study, we developed a deep learning-based virtual pathology assistant capable of distinguishing AIS from normal cervical gland histology using the CAISHI dataset, which contains 2240 expert-labeled H&E images (1010 normal and 1230 AIS). All images underwent Macenko stain normalization and patch-based preprocessing to enhance morphological feature representation. An EfficientNet-B3 convolutional neural network was trained using class-balanced sampling and focal loss to address dataset imbalance and emphasize difficult examples. The final model achieved an overall accuracy of 0.7323, with an F1-score of 0.75 for the Abnormal class and 0.71 for the Normal class. Grad-CAM heatmaps demonstrated biologically interpretable activation patterns, highlighting nuclear atypia and glandular crowding consistent with AIS morphology. The trained model was deployed in a Gradio-based virtual diagnostic assistant. These findings demonstrate the feasibility of lightweight, interpretable AI systems for cervical gland pathology, with potential applications in screening workflows, education, and low-resource settings.
Together, Then Apart: Revisiting Multimodal Survival Analysis via a Min-Max Perspective
Venue: arXiv cs.CV 2025
Year: 2025

Integrating heterogeneous modalities such as histopathology and genomics is central to advancing survival analysis, yet most existing methods prioritize cross-modal alignment through attention-based fusion mechanisms, often at the expense of modality-specific characteristics. This overemphasis on alignment leads to representation collapse and reduced diversity. In this work, we revisit multi-modal survival analysis via the dual lens of alignment and distinctiveness, positing that preserving modality-specific structure is as vital as achieving semantic coherence. In this paper, we introduce Together-Then-Apart (TTA), a unified min-max optimization framework that simultaneously models shared and modality-specific representations. The Together stage minimizes semantic discrepancies by aligning embeddings via shared prototypes, guided by an unbalanced optimal transport objective that adaptively highlights informative tokens. The Apart stage maximizes representational diversity through modality anchors and a contrastive regularizer that preserve unique modality information and prevent feature collapse. Extensive experiments on five TCGA benchmarks show that TTA consistently outperforms state-of-the-art methods. Beyond empirical gains, our formulation provides a new theoretical perspective of how alignment and distinctiveness can be jointly achieved in for robust, interpretable, and biologically meaningful multi-modal survival analysis.
A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies. This paper presents a new error temporal difference (ETD) algorithm for DRL to address the uncertainty in predictions,aiming to improve the performance of microgrid operations. First,a microgrid system integrated with renewable energy sources (RES) and energy storage systems (ESS), along with its Markov decision process (MDP), is modelled. Second, a predictive control approach based on a deep Q network (DQN) is presented, in which a weighted average algorithm and a new ETD algorithm are designed to quantify and address the prediction uncertainty, respectively. Finally, simulations on a realworld US dataset suggest that the developed ETD effectively improves the performance of DRL in optimizing microgrid operations.
A System to Automatically Generate Configuration Instructions for Network Elements from Network Configuration Models
Venue: arXiv cs.NI 2025
Year: 2025

In preparation for constructing or modifying information networks, network engineers develop configuration procedures for network devices according to network configuration specifications. However, as engineers typically create these procedures manually, the generated configuration procedures frequently diverge from the specified requirements. To improve this situation, this paper proposes a method for automatically generating configuration procedures consisting of network device configuration commands based on network configurations and their modification specifications. In this study, we employed the UML (Unified Modeling Language) object-oriented modeling language to develop a notation for network configuration modeling that ensures both strict specification adherence and ease of extension. Additionally, we implemented a method for automatically generating configuration procedures that match the specifications by utilizing network configuration models. As an evaluation experiment, we applied the proposed method to a configuration change scenario in a wide-area campus network at Shinshu University, where the network was migrated from static routing to dynamic routing using the OSPF protocol. As a result, all expected configuration procedures were obtained and a network exhibiting the intended behavior was successfully constructed.
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
Venue: arXiv cs.CV 2025
Year: 2025

The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of synthetic media. Existing methods primarily focus on image-level forgery detection, leaving generic video-level forgery detection largely underexplored. To advance video forensics, we propose a consolidated multimodal detection algorithm, named MM-Det++, specifically designed for detecting diffusion-generated videos. Our approach consists of two innovative branches and a Unified Multimodal Learning (UML) module. Specifically, the Spatio-Temporal (ST) branch employs a novel Frame-Centric Vision Transformer (FC-ViT) to aggregate spatio-temporal information for detecting diffusion-generated videos, where the FC-tokens enable the capture of holistic forgery traces from each video frame. In parallel, the Multimodal (MM) branch adopts a learnable reasoning paradigm to acquire Multimodal Forgery Representation (MFR) by harnessing the powerful comprehension and reasoning capabilities of Multimodal Large Language Models (MLLMs), which discerns the forgery traces from a flexible semantic perspective. To integrate multimodal representations into a coherent space, a UML module is introduced to consolidate the generalization ability of MM-Det++. In addition, we also establish a large-scale and comprehensive Diffusion Video Forensics (DVF) dataset to advance research in video forgery detection. Extensive experiments demonstrate the superiority of MM-Det++ and highlight the effectiveness of unified multimodal forgery learning in detecting diffusion-generated videos.
Muskie: Multi-view Masked Image Modeling for 3D Vision Pre-training
Venue: arXiv cs.CV 2025
Year: 2025

We present Muskie, a native multi-view vision backbone designed for 3D vision tasks. Unlike existing models, which are frame-wise and exhibit limited multi-view consistency, Muskie is designed to process multiple views simultaneously and introduce multi-view consistency in pre-training stage. Muskie is trained to reconstruct heavily masked content in one view by finding and utilizing geometric correspondences from other views. Through this pretext task and our proposed aggressive masking strategy, the model implicitly to learn view-invariant features and develop strong geometric understanding without any 3D supervision. Compared with state-of-the-art frame-wise backbones such as DINO, Muskie achieves higher multi-view correspondence accuracy. Furthermore, we demonstrate that using Muskie as a backbone consistently enhances performance on downstream 3D tasks, including camera pose estimation and pointmap reconstruction. Codes are publicly available at https://leo-frank.github.io/Muskie/
Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches attempt to mitigate bias by replacing the most attribute-correlated embedding coordinates with neutral values. However, our systematic analysis reveals three critical failures of this coordinate-wise approach: feature entanglement, poor cross-dataset generalization, and incomplete bias removal. We find that bias is not localized to a few coordinates but is instead distributed across a few linear subspaces. To address these limitations, we propose $\textbf{S}$ubspace $\textbf{P}$rojection $\textbf{D}$ebiasing ($\textbf{SPD}$), a geometrically principled framework that identifies and removes the entire subspace of linearly decodable bias while reinserting a neutral mean component to preserve semantic fidelity. Extensive experiments across zero-shot classification, text-to-image retrieval, and image generation validate the effectiveness of SPD: our method achieves more robust debiasing with an average improvement of $18.5\%$ across four fairness metrics, while maintaining minimal loss in task performance compared to the best debiasing baseline.
CAPIRE Intervention Lab: An Agent-Based Policy Simulation Environment for Curriculum-Constrained Engineering Programmes
Venue: arXiv cs.CY 2025
Year: 2025

Engineering programmes in Latin America combine high structural rigidity, intense assessment cultures and persistent socio-economic inequality, producing dropout rates that remain stubbornly high despite increasingly accurate early-warning models. Predictive learning analytics can identify students at risk, but they offer limited guidance on which concrete combinations of policies should be implemented, when, and for whom. This paper presents the CAPIRE Intervention Lab, an agent-based simulation environment designed to complement predictive models with in silico experimentation on curriculum and teaching policies in a Civil Engineering programme. The model is calibrated on 1,343 students from 15 cohorts in a six-year programme with 34 courses and 12 simulated semesters. Agents are initialised from empirically derived trajectory archetypes and embedded in a curriculum graph with structural friction indicators, including backbone completion, blocked credits and distance to graduation. Each agent evolves under combinations of three policy dimensions: (A) curriculum and assessment structure, (B) teaching and academic support, and (C) psychosocial and financial support. A 2x2x2 factorial design with 100 replications per scenario yields over 80,000 simulated trajectories. Results show that policy bundles targeting early backbone courses and blocked credits can reduce long-term dropout by approximately three percentage points and substantially increase the number of courses passed by structurally vulnerable archetypes, while leaving highly regular students almost unaffected. The Intervention Lab thus shifts learning analytics from static prediction towards dynamic policy design, offering institutions a transparent, extensible sandbox to test curriculum and teaching reforms before large-scale implementation.
GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set
Venue: arXiv cs.CL 2025
Year: 2025

This study introduce GeeSanBhava, a high-quality data set of Sinhala song comments extracted from YouTube manually tagged using Russells Valence-Arousal model by three independent human annotators. The human annotators achieve a substantial inter-annotator agreement (Fleiss kappa = 84.96%). The analysis revealed distinct emotional profiles for different songs, highlighting the importance of comment based emotion mapping. The study also addressed the challenges of comparing comment-based and song-based emotions, mitigating biases inherent in user-generated content. A number of Machine learning and deep learning models were pre-trained on a related large data set of Sinhala News comments in order to report the zero-shot result of our Sinhala YouTube comment data set. An optimized Multi-Layer Perceptron model, after extensive hyperparameter tuning, achieved a ROC-AUC score of 0.887. The model is a three-layer MLP with a configuration of 256, 128, and 64 neurons. This research contributes a valuable annotated dataset and provides insights for future work in Sinhala Natural Language Processing and music emotion recognition.
Graph Neural Networks vs Convolutional Neural Networks for Graph Domination Number Prediction
Venue: arXiv cs.LG 2025
Year: 2025

We investigate machine learning approaches to approximating the \emph{domination number} of graphs, the minimum size of a dominating set. Exact computation of this parameter is NP-hard, restricting classical methods to small instances. We compare two neural paradigms: Convolutional Neural Networks (CNNs), which operate on adjacency matrix representations, and Graph Neural Networks (GNNs), which learn directly from graph structure through message passing. Across 2,000 random graphs with up to 64 vertices, GNNs achieve markedly higher accuracy ($R^2=0.987$, MAE $=0.372$) than CNNs ($R^2=0.955$, MAE $=0.500$). Both models offer substantial speedups over exact solvers, with GNNs delivering more than $200\times$ acceleration while retaining near-perfect fidelity. Our results position GNNs as a practical surrogate for combinatorial graph invariants, with implications for scalable graph optimization and mathematical discovery.
LocaGen: Low-Overhead Indoor Localization Through Spatial Augmentation
Venue: arXiv cs.LG 2025
Year: 2025

Indoor localization systems commonly rely on fingerprinting, which requires extensive survey efforts to obtain location-tagged signal data, limiting their real-world deployability. Recent approaches that attempt to reduce this overhead either suffer from low representation ability, mode collapse issues, or require the effort of collecting data at all target locations. We present LocaGen, a novel spatial augmentation framework that significantly reduces fingerprinting overhead by generating high-quality synthetic data at completely unseen locations. LocaGen leverages a conditional diffusion model guided by a novel spatially aware optimization strategy to synthesize realistic fingerprints at unseen locations using only a subset of seen locations. To further improve our diffusion model performance, LocaGen augments seen location data based on domain-specific heuristics and strategically selects the seen and unseen locations using a novel density-based approach that ensures robust coverage. Our extensive evaluation on a real-world WiFi fingerprinting dataset shows that LocaGen maintains the same localization accuracy even with 30% of the locations unseen and achieves up to 28% improvement in accuracy over state-of-the-art augmentation methods.
Time-aware Motion Planning in Dynamic Environments with Conformal Prediction
Venue: arXiv cs.RO 2025
Year: 2025

Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments. The project page is available at https://time-aware-planning.github.io
Bayesian Calibration of Engine-out NOx Models for Engine-to-Engine Transferability
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of engine-out NOx is essential for meeting stringent emissions regulations and optimizing engine performance. Traditional approaches rely on models trained on data from a small number of engines, which can be insufficient in generalizing across an entire population of engines due to sensor biases and variations in input conditions. In real world applications, these models require tuning or calibration to maintain acceptable error tolerance when applied to other engines. This highlights the need for models that can adapt with minimal adjustments to accommodate engine-to-engine variability and sensor discrepancies. While previous studies have explored machine learning methods for predicting engine-out NOx, these approaches often fail to generalize reliably across different engines and operating environments. To address these issues, we propose a Bayesian calibration framework that combines Gaussian processes with approximate Bayesian computation to infer and correct sensor biases. Starting with a pre-trained model developed using nominal engine data, our method identifies engine specific sensor biases and recalibrates predictions accordingly. By incorporating these inferred biases, our approach generates posterior predictive distributions for engine-out NOx on unseen test data, achieving high accuracy without retraining the model. Our results demonstrate that this transferable modeling approach significantly improves the accuracy of predictions compared to conventional non-adaptive GP models, effectively addressing engine-to-engine variability and improving model generalizability.
Establishing Traceability Links between Release Notes & Software Artifacts: Practitioners' Perspectives
Venue: arXiv cs.SE 2025
Year: 2025

Maintaining traceability links between software release notes and corresponding development artifacts, e.g., pull requests (PRs), commits, and issues, is essential for managing technical debt and ensuring maintainability. However, in open-source environments where contributors work remotely and asynchronously, establishing and maintaining these links is often error-prone, time-consuming, and frequently overlooked. Our empirical study of GitHub repositories revealed that 47% of release artifacts lacked traceability links, and 12% contained broken links. To address this gap, we first analyzed release notes to identify their What, Why, and How information and assessed how these align with PRs, commits, and issues. We curated a benchmark dataset consisting of 3,500 filtered and validated traceability link instances. Then, we implemented LLM-based approaches to automatically establish traceability links of three pairs between release note contents & PRs, release note contents & PRs and release note contents & issues. By combining the time proximity feature, the LLM-based approach, e.g., Gemini 1.5 Pro, achieved a high Precision@1 value of 0.73 for PR traceability recovery. To evaluate the usability and adoption potential of this approach, we conducted an online survey involving 33 open-source practitioners. 16% of respondents rated as very important, and 68% as somewhat important for traceability maintenance.