#01Aug 25, 2026
cs.CV
DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton
Jintao Cheng, Weibin Li
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-temporal dependencies. We formalize this capability as \textbf{multi-agent tactical reasoning} and introduce \textbf{DoublesEval}, a diagnostic evaluation framework that leverages professional doubles badminton as a structurally tractable testbed. DoublesEval employs a key-moment-based protocol that decomposes rallies into tactically salient instants and probes models across four interpretable dimensions: atomic recognition, intra-segment composite understanding, cross-segment causal reasoning, and high-level tactical abstraction. This design isolates \emph{where} reasoning fails, rather than merely measuring answer correctness. To address observed failure modes, we propose \textbf{TacticCheck}, a lightweight constraint-guided test-time consistency checker that reranks candidate answers using the model's own lower-level tactical predictions, requiring no parameter updates or ground-truth labels at inference time. Evaluating four representative open-source VLMs on 60 curated rallies (yielding $\sim$9.6K structured instances) via a zero-shot protocol, we find that models remain weak across all diagnostic levels, with especially clear bottlenecks in spatial state, interaction binding, and terminal evidence. TacticCheck delivers consistent gains across all evaluated models, while still leaving a substantial gap to robust tactical reasoning. These results highlight the need for structured, interaction-aware evaluation paradigms for next-generation VLMs. The source code is available in \href{https://github.com/Chengjt1999/DoublesEval}{\textcolor{blue}{our GitHub repository}}.
#02Aug 25, 2026
cs.CV
Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
Angelos Georgakis, Valentina Kanaki, Giorgos Giannopoulos and 4 more
A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or clouds. Additionally, this data suffers from the fundamental trade-off between spatial and temporal resolution, which remains largely unresolved, making the acquisition of continuous high-resolution satellite observations of clouds an ongoing challenge. This work addresses this challenge by proposing two Deep Learning super-resolution methods for the accurate downscaling of SEVIRI cloud mask products, as well as a novel cross-sensor cloud mask dataset called SEVMOD-CM, created by spatially and temporally matching MODIS and SEVIRI satellite observations. The two proposed models are a CNN-based (SpatialCNN) and a GAN-based (SpatialGAN) Neural Network. Trained on the SEVIRI spectral and cloud mask products, the proposed methods predict the corresponding MODIS Cloud masks, achieving a 4x spatial enhancement across sensor domains. Both approaches are evaluated experimentally, and compared against the standard bicubic interpolation upsampling technique. The experimental results demonstrate the value of the proposed models and dataset for the remote sensing community, highlighting the benefits of applying super-resolution techniques to geostationary-derived cloud mask products for applications such as atmospheric monitoring, weather forecasting, disaster risk reduction, solar energy forecasting, and climate research.
#03Aug 25, 2026
cs.CV
X-MULTI: VLM-based Imaging Factor Disentanglement for Factor-Aware Image Synthesis
Sonali Godavarthy, Matthias Neuwirth-Trapp, Tim-Felix Faasch and 4 more
Imaging factor disentanglement in text-to-image generation aims to independently control image acquisition properties such as types of camera lenses, sensor types, viewpoints, and domains to enable combinatorial generalization. This should let the model synthesize novel factor combinations unobserved in the training data, such as pairing a fisheye lens with an event sensor never observed in training data. Recent work, MULTI, introduced learnable, factor-specific embeddings to disentangle imaging factors, along with the Factor Alignment Accuracy (FAA) metric to evaluate disentanglement quality. We identify and address two independent limitations. First, MULTI's pixel-level reconstruction objective supervises the model only on observed imaging factor combinations, providing no direct training signal for novel combinations. We therefore propose X-MULTI, which uses a pretrained vision-language model (VLM) to supervise novel factor combinations synthesized during training. Second, we show the FAA metric exhibits severe cross-factor correlation leakage, misrepresenting true disentanglement quality. We therefore propose Improved-FAA (I-FAA), which employs factor-specific augmentation strategies to break these correlations and enables more rigorous evaluation. Experiments demonstrate that X-MULTI achieves improved factor alignment on novel combinations compared to MULTI. Moreover, we show that correlation leakage in FAA distorts the evaluation of true factor disentanglement and I-FAA reduces this leakage and therefore provides a more robust assessment of factor alignment.
#04Aug 25, 2026
cs.CV
LeFlow: Generative Latent Flow Planning for World Models
Hsiang-Wei Huang, Jianxu Shangguan, Junbin Lu and 1 more
Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online trajectory optimization for action planning: for every state-goal pair, an iterative optimizer is run from scratch to search for optimal action sequences, treating the world model as a black-box simulator. This approach pays the full iterative optimization cost anew at every replanning step and reuses no planning experience across queries. In this work, we ask whether planning itself can be amortized once a latent world model has been learned. We present LeFlow, which learns a reusable latent trajectory prior operating directly in the latent dynamics space from the world model. LeFlow recasts planning as conditional latent trajectory generation: a rectified-flow model imagines a future latent path between the current and goal embeddings, an inverse dynamics decoder turns latent transitions into action chunks, and the frozen world model verifies each candidate by autoregressive rollout. Across four major goal-conditioned pixel-control benchmarks, LeFlow replaces iterative action-space optimization with amortized latent planning and fixed-budget rollout selection, achieving consistent success-rate gains with an order-of-magnitude reduction in planning time. Our results argue that latent world models should support not only prediction but reusable planning priors. Our code is available at https://github.com/hsiangwei0903/LeFlow.
#05Aug 25, 2026
eess.IV
Model Effect or Label Effect? Refined Annotations and a Human-Referenced Benchmark for Pulmonary Embolism Segmentation
Qihang Sun, Zhongxiao Liu, Bailiang Jian and 6 more
Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes, and to establish a human-referenced framework. Materials and Methods: This retrospective study screened 166 voxel-annotated CT pulmonary angiography cases from CADPE (n=91), FUMPE (n=35), and READ (n=40); 149 were included. A primary rater annotated PE by protocol, and a senior thoracic radiologist reviewed and revised all segmentations. Three additional raters at three centers annotated a 15-case subset. The label effect was measured by evaluating two pretrained nnU-Net models (nnU-Net-A, nnU-Net-B) against original and refined annotations. The model effect was measured by comparing the same architecture trained on different dataset combinations with annotations fixed. The benchmark model (nnPE) was trained with leave-one-dataset-out and pooled five-fold cross-validation. Four metric categories were analyzed with case-paired Wilcoxon signed-rank tests, Benjamini-Hochberg correction, and bootstrap 95% CIs. Results: Changing only the annotation increased mean DSC by 0.143 (0.122-0.166) for nnU-Net-A and 0.188 (0.163-0.213) for nnU-Net-B (both P < .001), whereas changing training-dataset composition changed DSC by 0.028. The label effect exceeded the model effect on CADPE and FUMPE and was 0.045 on READ. Within-mask attenuation SD fell in all three datasets after re-annotation (all P < .001). nnPE reached DSC 0.72 +/- 0.22 on pooled cross-validation but scored below all four annotators across 52 paired comparisons (all corrected P < .05). Conclusion: Evaluation annotations affected measured PE segmentation performance at least as much as model training choices. A human-referenced evaluation framework is publicly available for future study.