OpenRaiser/Intern-Atlas huggingface dataset:
Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging
Venue: arXiv cs.CE 2025
Year: 2025

Understanding the architecture of the human heart requires analysis of its microstructural organization across scales. With the advent of high-resolution imaging techniques such as synchrotron-based tomography, it has become possible to visualize entire hearts at micron-scale resolution. However, translating these large, complex volumetric datasets into interpretable, quantitative descriptors of cardiac organization remains a major challenge. Here we present cardiotensor, an open-source Python package designed to quantify 3D cardiomyocyte orientation in whole- or partial-heart imaging datasets. It provides efficient, scalable implementations of structure tensor analysis, enabling extraction of directional metrics such as helical angle (HA), intrusion angle (IA), and fractional anisotropy (FA). The package supports datasets reaching teravoxel-scale and is optimized for high-performance computing environments, including parallel and chunk-based processing pipelines. In addition, cardiotensor includes tractography functionality to reconstruct continuous cardiomyocyte trajectories. This enables multi-scale myoaggregate visualization down to the myocyte level, depending on resolution. These capabilities enable detailed structural mapping of cardiac tissue, supporting the assessment of anatomical continuity and regional organization.
VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However, existing benchmarks focus on English-only document retrieval or only consider multilingual question-answering on a single-page image. To bridge this gap, we introduce VisR-Bench, a multilingual benchmark designed for question-driven multimodal retrieval in long documents. Our benchmark comprises over 35K high-quality QA pairs across 1.2K documents, enabling fine-grained evaluation of multimodal retrieval. VisR-Bench spans sixteen languages with three question types (figures, text, and tables), offering diverse linguistic and question coverage. Unlike prior datasets, we include queries without explicit answers, preventing models from relying on superficial keyword matching. We evaluate various retrieval models, including text-based methods, multimodal encoders, and MLLMs, providing insights into their strengths and limitations. Our results show that while MLLMs significantly outperform text-based and multimodal encoder models, they still struggle with structured tables and low-resource languages, highlighting key challenges in multilingual visual retrieval.
StreetWeave: A Declarative Grammar for Street-Overlaid Visualization of Multivariate Data
Venue: arXiv cs.HC 2025
Year: 2025

The visualization and analysis of street and pedestrian networks are important to various domain experts, including urban planners, climate researchers, and health experts. This has led to the development of new techniques for street and pedestrian network visualization, expanding how data can be shown and understood more effectively. Despite their increasing adoption, there is no established design framework to guide the creation of these visualizations while addressing the diverse requirements of various domains. When exploring a feature of interest, domain experts often need to transform, integrate, and visualize a combination of thematic data (e.g., demographic, socioeconomic, pollution) and physical data (e.g., zip codes, street networks), often spanning multiple spatial and temporal scales. This not only complicates the process of visual data exploration and system implementation for developers but also creates significant entry barriers for experts who lack a background in programming. With this in mind, in this paper, we reviewed 45 studies utilizing street-overlaid visualizations to understand how they are used. Through qualitative coding of these visualizations, we analyzed three key aspects of street and pedestrian network visualization usage: the analytical purpose they serve, the visualization approaches employed, and the data sources used in their creation. Building on this design space, we introduce StreetWeave, a declarative grammar for designing custom visualizations of multivariate spatial network data across multiple resolutions. We demonstrate how StreetWeave can be used to create various street-overlaid visualizations, enabling effective exploration and analysis of spatial data. StreetWeave is available at https://urbantk.org/streetweave.
Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
Venue: arXiv cs.HC 2025
Year: 2025

What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue -- whether between humans, between human and AI, or among groups -- as a living system with interpretable structure that can be visualized, compared, and understood. Unlike traditional conversation analysis that reduces rich interaction to statistical summaries, our approach reveals the temporal architecture of dialogue through biological metaphors. Linguistic complexity flows through strand thickness, emotional trajectories cascade through color gradients, conversational relevance forms through connecting elements, and topic coherence maintains structural integrity through helical patterns. Through exploratory analysis of therapeutic conversations and historically significant human-AI dialogues, we demonstrate how this visualization approach reveals interaction patterns that traditional methods miss. Our work contributes a new creative framework for understanding communication that bridges data visualization, human-computer interaction, and the fundamental question of what makes dialogue meaningful in an age where humans increasingly converse with artificial minds.
A Benchmark for Databases with Varying Value Lengths
Venue: arXiv cs.DB 2025
Year: 2025

The performance of database management systems (DBMS) is traditionally evaluated using benchmarks that focus on workloads with (almost) fixed record lengths. However, some real-world workloads in key/value stores, document databases, and graph databases exhibit significant variability in value lengths, which can lead to performance anomalies, particularly when popular records grow disproportionately large. Existing benchmarks fail to account for this variability, leaving an important aspect of DBMS behavior underexplored. In this paper, we address this gap by extending the Yahoo! Cloud Serving Benchmark (YCSB) to include an "extend" operation, which appends data to record fields, simulating the growth of values over time. Using this modified benchmark, we have measured the performance of three popular DBMS backends: MongoDB, MariaDB with the InnoDB storage engine, and MariaDB with the MyRocks storage engine. Our experiments alternate between extending values and executing query workloads, revealing significant performance differences driven by storage engine design and their handling of variable-sized values. Our key contribution is the introduction of a novel benchmarking approach to evaluate the impact of growing value sizes and isolate the effect of querying data with a distribution of data sizes from any cost associated with accessing data after a history of updates. This highlights the need for more representative benchmarks that capture the dynamic nature of real-world workloads, providing valuable guidance for both practitioners and researchers.
FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
Venue: arXiv cs.MM 2025
Year: 2025

Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advancements in general video understanding. This difficulty arises primarily from the scarcity of datasets with sufficiently rich and domain-specific annotations. To bridge this gap, we introduce FineBadminton, a novel and large-scale dataset featuring a unique multi-level semantic annotation hierarchy (Foundational Actions, Tactical Semantics, and Decision Evaluation) for comprehensive badminton understanding. The construction of FineBadminton is powered by an innovative annotation pipeline that synergistically combines MLLM-generated proposals with human refinement. We also present FBBench, a challenging benchmark derived from FineBadminton, to rigorously evaluate MLLMs on nuanced spatio-temporal reasoning and tactical comprehension. Together, FineBadminton and FBBench provide a crucial ecosystem to catalyze research in fine-grained video understanding and advance the development of MLLMs in sports intelligence. Furthermore, we propose an optimized baseline approach incorporating Hit-Centric Keyframe Selection to focus on pivotal moments and Coordinate-Guided Condensation to distill salient visual information. The results on FBBench reveal that while current MLLMs still face significant challenges in deep sports video analysis, our proposed strategies nonetheless achieve substantial performance gains. The project homepage is available at https://finebadminton.github.io/FineBadminton/.
Extended AB Algorithms for Bistatic Integrated Sensing and Communications Systems
Venue: arXiv cs.IT 2025
Year: 2025

Integrated sensing and communication (ISAC) is pivotal for next-generation wireless networks, rendering the computation of rate-distortion trade-off in ISAC systems critically important. In this paper, we propose the extended Arimoto-Blahut (AB) algorithms to calculate the rate-distortion trade-off in bistatic ISAC systems, which overcome the limitation of existing AB algorithms in handling non-convex constraints. Specifically, we introduce auxiliary variables to transform non-convex distortion constraints into linear constraints, prove that the reformulated linearly-constrained optimization problem maintains the same optimal solution as the original problem, and develop extended AB algorithms for both squared error and logarithmic loss distortion metrics based on the framework of AB algorithm. Numerical results validate the effectiveness of the proposed algorithm.
Adaptive Cache Enhancement for Test-Time Adaptation of Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models (VLMs) exhibit remarkable zero-shot generalization but suffer performance degradation under distribution shifts in downstream tasks, particularly in the absence of labeled data. Test-Time Adaptation (TTA) addresses this challenge by enabling online optimization of VLMs during inference, eliminating the need for annotated data. Cache-based TTA methods exploit historical knowledge by maintaining a dynamic memory cache of low-entropy or high-confidence samples, promoting efficient adaptation to out-of-distribution data. Nevertheless, these methods face two critical challenges: (1) unreliable confidence metrics under significant distribution shifts, resulting in error accumulation within the cache and degraded adaptation performance; and (2) rigid decision boundaries that fail to accommodate substantial distributional variations, leading to suboptimal predictions. To overcome these limitations, we introduce the Adaptive Cache Enhancement (ACE) framework, which constructs a robust cache by selectively storing high-confidence or low-entropy image embeddings per class, guided by dynamic, class-specific thresholds initialized from zero-shot statistics and iteratively refined using an exponential moving average and exploration-augmented updates. This approach enables adaptive, class-wise decision boundaries, ensuring robust and accurate predictions across diverse visual distributions. Extensive experiments on 15 diverse benchmark datasets demonstrate that ACE achieves state-of-the-art performance, delivering superior robustness and generalization compared to existing TTA methods in challenging out-of-distribution scenarios.
MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmark
Venue: arXiv cs.AI 2025
Year: 2025

LLMs' capabilities are enhanced by using function calls to integrate various data sources or API results into the context window. Typical tools include search, web crawlers, maps, financial data, file systems, and browser usage, etc. Integrating these data sources or functions requires a standardized method. The Model Context Protocol (MCP) provides a standardized way to supply context to LLMs. However, the evaluation of LLMs and AI Agents' MCP tool use abilities suffer from several issues. First, there's a lack of comprehensive datasets or benchmarks to evaluate various MCP tools. Second, the diverse formats of response from MCP tool call execution further increase the difficulty of evaluation. Additionally, unlike existing tool-use benchmarks with high success rates in functions like programming and math functions, the success rate of real-world MCP tool is not guaranteed and varies across different MCP servers. Furthermore, the LLMs' context window also limits the number of available tools that can be called in a single run, because the textual descriptions of tool and the parameters have long token length for an LLM to process all at once. To help address the challenges of evaluating LLMs' performance on calling MCP tools, we propose MCPToolBench++, a large-scale, multi-domain AI Agent tool use benchmark. As of July 2025, this benchmark is build upon marketplace of over 4k MCP servers from more than 40 categories, collected from the MCP marketplaces and GitHub communities. The datasets consist of both single-step and multi-step tool calls across different categories. We evaluated SOTA LLMs with agentic abilities on this benchmark and reported the results.
Towards Comprehensible Recommendation with Large Language Model Fine-tuning
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems have become increasingly ubiquitous in daily life. While traditional recommendation approaches primarily rely on ID-based representations or item-side content features, they often fall short in capturing the underlying semantics aligned with user preferences (e.g., recommendation reasons for items), leading to a semantic-collaborative gap. Recently emerged LLM-based feature extraction approaches also face a key challenge: how to ensure that LLMs possess recommendation-aligned reasoning capabilities and can generate accurate, personalized reasons to mitigate the semantic-collaborative gap. To address these issues, we propose a novel Content Understanding from a Collaborative Perspective framework (CURec), which generates collaborative-aligned content features for more comprehensive recommendations. \method first aligns the LLM with recommendation objectives through pretraining, equipping it with instruction-following and chain-of-thought reasoning capabilities. Next, we design a reward model inspired by traditional recommendation architectures to evaluate the quality of the recommendation reasons generated by the LLM. Finally, using the reward signals, CURec fine-tunes the LLM through RL and corrects the generated reasons to ensure their accuracy. The corrected reasons are then integrated into a downstream recommender model to enhance comprehensibility and recommendation performance. Extensive experiments on public benchmarks demonstrate the superiority of CURec over existing methods.
An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View
Venue: arXiv cs.CV 2025
Year: 2025

In dental cone-beam computed tomography (CBCT), compact and cost-effective system designs often use small detectors, resulting in a truncated field of view (FOV) that does not fully encompass the patient's head. In iterative reconstruction approaches, the discrepancy between the actual projection and the forward projection within the truncated FOV accumulates over iterations, leading to significant degradation in the reconstructed image quality. In this study, we propose a two-stage approach to mitigate truncation artifacts in dental CBCT. In the first stage, we employ Implicit Neural Representation (INR), leveraging its superior representation power, to generate a prior image over an extended region so that its forward projection fully covers the patient's head. To reduce computational and memory burdens, INR reconstruction is performed with a coarse voxel size. The forward projection of this prior image is then used to estimate the discrepancy due to truncated FOV in the measured projection data. In the second stage, the discrepancy-corrected projection data is utilized in a conventional iterative reconstruction process within the truncated region. Our numerical results demonstrate that the proposed two-grid approach effectively suppresses truncation artifacts, leading to improved CBCT image quality.
Enhancing Egocentric Object Detection in Static Environments using Graph-based Spatial Anomaly Detection and Correction
Venue: arXiv cs.CV 2025
Year: 2025

In many real-world applications involving static environments, the spatial layout of objects remains consistent across instances. However, state-of-the-art object detection models often fail to leverage this spatial prior, resulting in inconsistent predictions, missed detections, or misclassifications, particularly in cluttered or occluded scenes. In this work, we propose a graph-based post-processing pipeline that explicitly models the spatial relationships between objects to correct detection anomalies in egocentric frames. Using a graph neural network (GNN) trained on manually annotated data, our model identifies invalid object class labels and predicts corrected class labels based on their neighbourhood context. We evaluate our approach both as a standalone anomaly detection and correction framework and as a post-processing module for standard object detectors such as YOLOv7 and RT-DETR. Experiments demonstrate that incorporating this spatial reasoning significantly improves detection performance, with mAP@50 gains of up to 4%. This method highlights the potential of leveraging the environment's spatial structure to improve reliability in object detection systems.
InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information
Venue: arXiv cs.CL 2025
Year: 2025

We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task central to real-world applications such as scientific reporting, financial analysis, and public policy dashboards. Unlike prior benchmarks focusing on isolated, visually uniform charts, InterChart challenges models with diverse question types ranging from entity inference and trend correlation to numerical estimation and abstract multi-step reasoning grounded in 2-3 thematically or structurally related charts. We organize the benchmark into three tiers of increasing difficulty: (1) factual reasoning over individual charts, (2) integrative analysis across synthetically aligned chart sets, and (3) semantic inference over visually complex, real-world chart pairs. Our evaluation of state-of-the-art open and closed-source VLMs reveals consistent and steep accuracy declines as chart complexity increases. We find that models perform better when we decompose multi-entity charts into simpler visual units, underscoring their struggles with cross-chart integration. By exposing these systematic limitations, InterChart provides a rigorous framework for advancing multimodal reasoning in complex, multi-visual environments.
Grasp-HGN: Grasping the Unexpected
Venue: arXiv cs.RO 2025
Year: 2025

For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. To advance next-generation prosthetic hand control design, it is crucial to address current shortcomings in robustness to out of lab artifacts, and generalizability to new environments. Due to the fixed number of object to interact with in existing datasets, contrasted with the virtually infinite variety of objects encountered in the real world, current grasp models perform poorly on unseen objects, negatively affecting users' independence and quality of life. To address this: (i) we define semantic projection, the ability of a model to generalize to unseen object types and show that conventional models like YOLO, despite 80% training accuracy, drop to 15% on unseen objects. (ii) we propose Grasp-LLaVA, a Grasp Vision Language Model enabling human-like reasoning to infer the suitable grasp type estimate based on the object's physical characteristics resulting in a significant 50.2% accuracy over unseen object types compared to 36.7% accuracy of an SOTA grasp estimation model. Lastly, to bridge the performance-latency gap, we propose Hybrid Grasp Network (HGN), an edge-cloud deployment infrastructure enabling fast grasp estimation on edge and accurate cloud inference as a fail-safe, effectively expanding the latency vs. accuracy Pareto. HGN with confidence calibration (DC) enables dynamic switching between edge and cloud models, improving semantic projection accuracy by 5.6% (to 42.3%) with 3.5x speedup over the unseen object types. Over a real-world sample mix, it reaches 86% average accuracy (12.2% gain over edge-only), and 2.2x faster inference than Grasp-LLaVA alone.
MLego: Interactive and Scalable Topic Exploration Through Model Reuse
Venue: arXiv cs.DB 2025
Year: 2025

With massive texts on social media, users and analysts often rely on topic modeling techniques to quickly extract key themes and gain insights. Traditional topic modeling techniques, such as Latent Dirichlet Allocation (LDA), provide valuable insights but are computationally expensive, making them impractical for real-time data analysis. Although recent advances in distributed training and fast sampling methods have improved efficiency, real-time topic exploration remains a significant challenge. In this paper, we present MLego, an interactive query framework designed to support real-time topic modeling analysis by leveraging model materialization and reuse. Instead of retraining models from scratch, MLego efficiently merges materialized topic models to construct approximate results at interactive speeds. To further enhance efficiency, we introduce a hierarchical plan search strategy for single queries and an optimized query reordering technique for batch queries. We integrate MLego into a visual analytics prototype system, enabling users to explore large-scale textual datasets through interactive queries. Extensive experiments demonstrate that MLego significantly reduces computation costs while maintaining high-quality topic modeling results. MLego enhances existing visual analytics approaches, which primarily focus on user-driven topic modeling, by enabling real-time, query-driven exploration. This complements traditional methods and bridges the gap between scalable topic modeling and interactive data analysis.