OpenRaiser/Intern-Atlas huggingface dataset:
Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization
Venue: arXiv cs.CV 2025
Year: 2025

In intelligent cartographic generation tasks empowered by generative models, the authenticity of synthesized maps constitutes a critical determinant. Concurrently, the selection of appropriate evaluation metrics to quantify map authenticity emerges as a pivotal research challenge. Current methodologies predominantly adopt computer vision-based image assessment metrics to compute discrepancies between generated and reference maps. However, conventional visual similarity metrics-including L1, L2, SSIM, and FID-primarily operate at pixel-level comparisons, inadequately capturing cartographic global features and spatial correlations, consequently inducing semantic-structural artifacts in generated outputs. This study introduces a novel Map Feature Perception Metric designed to evaluate global characteristics and spatial congruence between synthesized and target maps. Diverging from pixel-wise metrics, our approach extracts elemental-level deep features that comprehensively encode cartographic structural integrity and topological relationships. Experimental validation demonstrates MFP's superior capability in evaluating cartographic semantic features, with classification-enhanced implementations outperforming conventional loss functions across diverse generative frameworks. When employed as optimization objectives, our metric achieves performance gains ranging from 2% to 50% across multiple benchmarks compared to traditional L1, L2, and SSIM baselines. This investigation concludes that explicit consideration of cartographic global attributes and spatial coherence substantially enhances generative model optimization, thereby significantly improving the geographical plausibility of synthesized maps.
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

One of the key tasks in machine learning for tabular data is feature engineering. Although it is vital for improving the performance of models, it demands considerable human expertise and deep domain knowledge, making it labor-intensive endeavor. To address this issue, we propose a novel framework, \textbf{FeRG-LLM} (\textbf{Fe}ature engineering by \textbf{R}eason \textbf{G}eneration \textbf{L}arge \textbf{L}anguage \textbf{M}odels), a large language model designed to automatically perform feature engineering at an 8-billion-parameter scale. We have constructed two-stage conversational dialogues that enable language models to analyze machine learning tasks and discovering new features, exhibiting their Chain-of-Thought (CoT) capabilities. We use these dialogues to fine-tune Llama 3.1 8B model and integrate Direct Preference Optimization (DPO) to receive feedback improving quality of new features and the model's performance. Our experiments show that FeRG-LLM performs comparably to or better than Llama 3.1 70B on most datasets, while using fewer resources and achieving reduced inference time. It outperforms other studies in classification tasks and performs well in regression tasks. Moreover, since it does not rely on cloud-hosted LLMs like GPT-4 with extra API costs when generating features, it can be deployed locally, addressing security concerns.
Is Journal Citation Indicator a good metric for Art & Humanities Journals currently?
Venue: arXiv cs.DL 2025
Year: 2025

Probably Not. Journal Citation Indicator (JCI) was introduced to address the limitations of traditional metrics like the Journal Impact Factor (JIF), particularly its inability to normalize citation impact across different disciplines. This study reveals that JCI faces significant challenges in field normalization for Art & Humanities journals, as evidenced by much lower correlations with a more granular, paper-level metric, CNCI-CT. A detailed analysis of Architecture journals highlights how journal-level misclassification and the interdisciplinary nature of content exacerbate these issues, leading to less reliable evaluations. We recommend improving journal classification systems or adopting paper-level normalization methods, potentially supported by advanced AI techniques, to enhance the accuracy and effectiveness of JCI for Art & Humanities disciplines.
Multi-Pass Streaming Lower Bounds for Approximating Max-Cut
Venue: arXiv cs.DS 2025
Year: 2025

In the Max-Cut problem in the streaming model, an algorithm is given the edges of an unknown graph $G = (V,E)$ in some fixed order, and its goal is to approximate the size of the largest cut in $G$. Improving upon an earlier result of Kapralov, Khanna and Sudan, it was shown by Kapralov and Krachun that for all $\varepsilon>0$, no $o(n)$ memory streaming algorithm can achieve a $(1/2+\varepsilon)$-approximation for Max-Cut. Their result holds for single-pass streams, i.e.~the setting in which the algorithm only views the stream once, and it was open whether multi-pass access may help. The state-of-the-art result along these lines, due to Assadi and N, rules out arbitrarily good approximation algorithms with constantly many passes and $n^{1-δ}$ space for any $δ>0$. We improve upon this state-of-the-art result, showing that any non-trivial approximation algorithm for Max-Cut requires either polynomially many passes or polynomially large space. More specifically, we show that for all $\varepsilon>0$, a $k$-pass streaming $(1/2+\varepsilon)$-approximation algorithm for Max-Cut requires $Ω_{\varepsilon}\left(n^{1/3}/k\right)$ space. This result leads to a similar lower bound for the Maximum Directed Cut problem, showing the near optimality of the algorithm of [Saxena, Singer, Sudan, Velusamy, SODA 2025]. Our lower bounds proceed by showing a communication complexity lower bound for the Distributional Implicit Hidden Partition (DIHP) Problem, introduced by Kapralov and Krachun. While a naive application of the discrepancy method fails, we identify a property of protocols called ``globalness'', and show that (1) any protocol for DIHP can be turned into a global protocol, (2) the discrepancy of a global protocol must be small. The second step is the more technically involved step in the argument, and therein we use global hypercontractive inequalities.
GMapLatent: Geometric Mapping in Latent Space
Venue: arXiv cs.CV 2025
Year: 2025

Cross-domain generative models based on encoder-decoder AI architectures have attracted much attention in generating realistic images, where domain alignment is crucial for generation accuracy. Domain alignment methods usually deal directly with the initial distribution; however, mismatched or mixed clusters can lead to mode collapse and mixture problems in the decoder, compromising model generalization capabilities. In this work, we innovate a cross-domain alignment and generation model that introduces a canonical latent space representation based on geometric mapping to align the cross-domain latent spaces in a rigorous and precise manner, thus avoiding mode collapse and mixture in the encoder-decoder generation architectures. We name this model GMapLatent. The core of the method is to seamlessly align latent spaces with strict cluster correspondence constraints using the canonical parameterizations of cluster-decorated latent spaces. We first (1) transform the latent space to a canonical parameter domain by composing barycenter translation, optimal transport merging and constrained harmonic mapping, and then (2) compute geometric registration with cluster constraints over the canonical parameter domains. This process realizes a bijective (one-to-one and onto) mapping between newly transformed latent spaces and generates a precise alignment of cluster pairs. Cross-domain generation is then achieved through the aligned latent spaces embedded in the encoder-decoder pipeline. Experiments on gray-scale and color images validate the efficiency, efficacy and applicability of GMapLatent, and demonstrate that the proposed model has superior performance over existing models.
Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT
Venue: arXiv cs.GR 2025
Year: 2025

Robust light transport algorithms, particularly bidirectional path tracing (BDPT), face significant challenges when dealing with specular or highly glossy involved paths. BDPT constructs the full path by connecting sub-paths traced individually from the light source and camera. However, it remains difficult to sample by connecting vertices on specular and glossy surfaces with narrow-lobed BSDF, as it poses severe constraints on sampling in the feasible direction. To address this issue, we propose a novel approach, called \emph{proxy sampling}, that enables efficient sub-path connection of these challenging paths. When a low-contribution specular/glossy connection occurs, we drop out the problematic neighboring vertex next to this specular/glossy vertex from the original path, then retrace an alternative sub-path as a proxy to complement this incomplete path. This newly constructed complete path ensures that the connection adheres to the constraint of the narrow lobe within the BSDF of the specular/glossy surface. Unbiased reciprocal estimation is the key to our method to obtain a probability density function (PDF) reciprocal to ensure unbiased rendering. We derive the reciprocal estimation method and provide an efficiency-optimized setting for efficient sampling and connection. Our method provides a robust tool for substituting problematic paths with favorable alternatives while ensuring unbiasedness. We validate this approach in the probabilistic connections BDPT for addressing specular-involved difficult paths. Experimental results have proved the effectiveness and efficiency of our approach, showcasing high-performance rendering capabilities across diverse settings.
An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) frequently produce factually inaccurate outputs - a phenomenon known as hallucination - which limits their accuracy in knowledge-intensive NLP tasks. Retrieval-augmented generation and agentic frameworks such as Reasoning and Acting (ReAct) can address this issue by giving the model access to external knowledge. However, LLMs often fail to remain faithful to retrieved information. Mitigating this is critical, especially if LLMs are required to reason about the retrieved information. Recent research has explored training-free decoding strategies to improve the faithfulness of model generations. We present a systematic analysis of how the combination of the ReAct framework and decoding strategies (i.e., DeCoRe, DoLa, and CAD) can influence the faithfulness of LLM-generated answers. Our results show that combining an agentic framework for knowledge retrieval with decoding methods that enhance faithfulness can increase accuracy on the downstream Multi-Hop Question Answering tasks. For example, we observe an F1 increase from 19.5 to 32.6 on HotpotQA when using ReAct and DoLa.
Improving underwater semantic segmentation with underwater image quality attention and muti-scale aggregation attention
Venue: arXiv cs.CV 2025
Year: 2025

Underwater image understanding is crucial for both submarine navigation and seabed exploration. However, the low illumination in underwater environments degrades the imaging quality, which in turn seriously deteriorates the performance of underwater semantic segmentation, particularly for outlining the object region boundaries. To tackle this issue, we present UnderWater SegFormer (UWSegFormer), a transformer-based framework for semantic segmentation of low-quality underwater images. Firstly, we propose the Underwater Image Quality Attention (UIQA) module. This module enhances the representation of highquality semantic information in underwater image feature channels through a channel self-attention mechanism. In order to address the issue of loss of imaging details due to the underwater environment, the Multi-scale Aggregation Attention(MAA) module is proposed. This module aggregates sets of semantic features at different scales by extracting discriminative information from high-level features,thus compensating for the semantic loss of detail in underwater objects. Finally, during training, we introduce Edge Learning Loss (ELL) in order to enhance the model's learning of underwater object edges and improve the model's prediction accuracy. Experiments conducted on the SUIM and DUT-USEG (DUT) datasets have demonstrated that the proposed method has advantages in terms of segmentation completeness, boundary clarity, and subjective perceptual details when compared to SOTA methods. In addition, the proposed method achieves the highest mIoU of 82.12 and 71.41 on the SUIM and DUT datasets, respectively. Code will be available at https://github.com/SAWRJJ/UWSegFormer.
CoRanking: Collaborative Ranking with Small and Large Ranking Agents
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated superior listwise ranking performance. However, their superior performance often relies on large-scale parameters (\eg, GPT-4) and a repetitive sliding window process, which introduces significant efficiency challenges. In this paper, we propose \textbf{CoRanking}, a novel collaborative ranking framework that combines small and large ranking models for efficient and effective ranking. CoRanking first employs a small-size reranker to pre-rank all the candidate passages, bringing relevant ones to the top part of the list (\eg, top-20). Then, the LLM listwise reranker is applied to only rerank these top-ranked passages instead of the whole list, substantially enhancing overall ranking efficiency. Although more efficient, previous studies have revealed that the LLM listwise reranker have significant positional biases on the order of input passages. Directly feed the top-ranked passages from small reranker may result in the sub-optimal performance of LLM listwise reranker. To alleviate this problem, we introduce a passage order adjuster trained via reinforcement learning, which reorders the top passages from the small reranker to align with the LLM's preferences of passage order. Extensive experiments on three IR benchmarks demonstrate that CoRanking significantly improves efficiency (reducing ranking latency by about 70\%) while achieving even better effectiveness compared to using only the LLM listwise reranker.
La perversidad inducida
Venue: arXiv cs.SI 2025
Year: 2025

The title of this essay, Induced perversity, La perversidad inducida (in Spanish), refers to the side effects of sharing information on social media when it turns out to be false; or when judgments are made about people, events, companies, or facts that can produce effects often leading to consequences that were not initially foreseen. In digital environments, on the internet of linked data, harrassment politics and the cancellation culture are not isolated phenomena; they should be related to the evolution of the web, the emergence of the platform-driven economy, and the change of users' behaviour. Risks are not only a product of technology or the emergence of artificial intelligence. Ethical problems arise in a hybrid society, where the adaptation of human behavior to human-machine interaction (HMI), and the ethical, social, and legal ways of regulating them have yet to be learned. This contribution complements the contents of the article: La regimentacion tecnologica. Inteligencia artificial, fascismo, agresionn y sociedad democratica. TeKnoKultura. Revista de Cultura Digital y Movimientos Sociales, no 22, 2-2025, monografico sobre poder y politica de la inteligencia artificial. https://revistas.ucm.es/index.php/TEKN/article/view/98266
Spatially-Embedded Lens Visualization: A Design Space
Venue: arXiv cs.GR 2025
Year: 2025

Lens visualization has been a prominent research area in the visualization community, fueled by the continuous need to mitigate visual clutter and occlusion resulting from the increase in data volume. Interactive lenses for spatial data, particularly, challenge designers to conceive design strategies to support the analysis of high-density, multifaceted data with spatial referents. Despite their relevance, there is a lack of systematic understanding regarding the various design elements that compose spatially-embedded lens visualizations. To fill in this gap, we unify these components under a common hood in the form of a design space, which we propose in this paper. Building our knowledge on top of the initial insights gained from Tominski et al.'s survey [57], we construct a design space spanning 7 dimensions through our analysis of 45 papers published in the visualization community over the past 15 years. We describe each design dimension through representative examples and examine the range of design choices available within each, discussing their benefits and pitfalls that affect lens performance and usability. In doing so, we offer a cohesive catalog of considerations for designers-both when examining existing lenses and when conceptualizing novel spatially-embedded lens visualizations. We conclude by shedding light on regions of the design space that remain largely understudied, revealing open opportunities for future research.
VideoGen-Eval: Agent-based System for Video Generation Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement of video generation has rendered existing evaluation systems inadequate for assessing state-of-the-art models, primarily due to simple prompts that cannot showcase the model's capabilities, fixed evaluation operators struggling with Out-of-Distribution (OOD) cases, and misalignment between computed metrics and human preferences. To bridge the gap, we propose VideoGen-Eval, an agent evaluation system that integrates LLM-based content structuring, MLLM-based content judgment, and patch tools designed for temporal-dense dimensions, to achieve a dynamic, flexible, and expandable video generation evaluation. Additionally, we introduce a video generation benchmark to evaluate existing cutting-edge models and verify the effectiveness of our evaluation system. It comprises 700 structured, content-rich prompts (both T2V and I2V) and over 12,000 videos generated by 20+ models, among them, 8 cutting-edge models are selected as quantitative evaluation for the agent and human. Extensive experiments validate that our proposed agent-based evaluation system demonstrates strong alignment with human preferences and reliably completes the evaluation, as well as the diversity and richness of the benchmark.
CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Referring Remote Sensing Image Segmentation (RRSIS) is a challenging task, aiming to segment specific target objects in remote sensing (RS) images based on a given language expression. Existing RRSIS methods typically employ coarse-grained unidirectional alignment approaches to obtain multimodal features, and they often overlook the critical role of language features as contextual information during the decoding process. Consequently, these methods exhibit weak object-level correspondence between visual and language features, leading to incomplete or erroneous predicted masks, especially when handling complex expressions and intricate RS image scenes. To address these challenges, we propose a fine-grained cross-modal alignment and decoding Transformer, CADFormer, for RRSIS. Specifically, we design a semantic mutual guidance alignment module (SMGAM) to achieve both vision-to-language and language-to-vision alignment, enabling comprehensive integration of visual and textual features for fine-grained cross-modal alignment. Furthermore, a textual-enhanced cross-modal decoder (TCMD) is introduced to incorporate language features during decoding, using refined textual information as context to enhance the relationship between cross-modal features. To thoroughly evaluate the performance of CADFormer, especially for inconspicuous targets in complex scenes, we constructed a new RRSIS dataset, called RRSIS-HR, which includes larger high-resolution RS image patches and semantically richer language expressions. Extensive experiments on the RRSIS-HR dataset and the popular RRSIS-D dataset demonstrate the effectiveness and superiority of CADFormer. Datasets and source codes will be available at https://github.com/zxk688.
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
Venue: arXiv cs.CV 2025
Year: 2025

We present OpenDriveVLA, a Vision Language Action model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially grounded driving actions by leveraging multimodal inputs, including 2D and 3D instance-aware visual representations, ego vehicle states, and language commands. To bridge the modality gap between driving visual representations and language embeddings, we introduce a hierarchical vision language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Furthermore, we incorporate structured agent environment ego interaction modeling into the autoregressive decoding process, enabling the model to capture fine-grained spatial dependencies and behavior-aware dynamics critical for reliable trajectory planning. Extensive experiments on the nuScenes dataset demonstrate that OpenDriveVLA achieves state-of-the-art results across open-loop trajectory planning and driving-related question answering tasks. Qualitative analyses further illustrate its capability to follow high-level driving commands and generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving.
Navigating with Haptic Gloves: Investigating Strategies for Horizontal and Vertical Movement Guidance
Venue: arXiv cs.HC 2025
Year: 2025

Navigating peripersonal space requires reaching targets in both horizontal (e.g., desks) and vertical (e.g., shelves) layouts with high precision. We developed a haptic glove to aid peri-personal target navigation and investigated the effectiveness of different feedback delivery methods. Twenty-two participants completed target navigation tasks under various conditions, including scene layout (horizontal or vertical), guidance approach (two-tactor or worst-axis first), guidance metaphor (push or pull), and intensity mode (linear or zone) for conveying distance cues. Task completion time, hand trajectory distance, and the percentage of hand trajectory in a critical area were measured as performance outcomes, along with subjective feedback. Participants achieved significantly faster task completion times and covered less hand trajectory distance in the horizontal layout, worst-axis first approach, and pull metaphor conditions. Additionally, male participants demonstrated superior performance and reported lower levels of frustration compared to their female counterparts throughout the study. Intensity mode had no significant effect on the results. In summary, vibrating one tactor at a time (worst-axis first) and using the pull metaphor were the most effective methods of delivering vibrotactile feedback for peripersonal target navigation in both horizontal and vertical settings. Findings from this work can guide future development of haptic gloves for individuals with vision impairments, environments with visual limitations, and for accessibility and rehabilitation applications.