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新论文 · v1arXiv:2611.03884

Timely Ground-Station Status Delivery via UAV-Assisted Data Collection and Hierarchical RL

Xiangjian Gao, Mohammad Moltafet, Hamid R. Sadjadpour

原文摘要

We consider a UAV-assisted status-update system in which a rotary-wing unmanned aerial vehicle (UAV) collects updates from multiple sensors, stores the newest update of each sensor, and forwards selected buffered updates to a ground station (GS). We minimize a weighted sum of the time-average GS-side age of information (AoI) and UAV energy consumption over a finite horizon. The optimization is subject to half-duplex operation, sensor and UAV energy-causality constraints, transmission-rate and power limits, and a terminal-location constraint. Because collection changes the information age at the UAV but does not reduce the GS-side AoI until the buffered update is forwarded, mobility, collection, and forwarding decisions are coupled over time. We use a hierarchical AoI-energy actor-critic (HAE-AC) controller in which a coarse-timescale actor selects return-feasible UAV movements, while a deterministic per-slot scheduler constructs feasible collection or forwarding sets and assigns the minimum transmit power required for each scheduled transmission. In a 100-sensor deployment, HAE-AC reduces the average GS-side AoI by about 22% relative to an adaptive staleness-gated benchmark. Compared with forward-upon-collection, selective buffered forwarding reduces the number of downlink bursts by roughly 30% while increasing the number of collections per sensor by more than 23%.

cs.IT · eess.SP · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.01691

Can MLLMs Perceive and Describe Medical Image Quality Beyond Scores?

Jiyao Liu, Jinjie Wei, Wanying Qu, Chenglong Ma, Junzhi Ning, Yunheng Li, Ying Chen, Jiafan Zhang 等 19 位作者

原文摘要

Medical Image Quality Assessment (IQA) serves as a critical safety gate for clinical AI, yet existing approaches rely on scalar metrics that fail to capture the descriptive, expert-like evaluation process central to clinical practice. We introduce MedQ-Bench, a comprehensive benchmark for language-based medical IQA evaluation with MLLMs, establishing a perception--description paradigm across 5 imaging modalities and over 40 quality attributes. The benchmark comprises two complementary tasks: MedQ-Perception probes low-level visual attribute discrimination via 2,600 expert-annotated perceptual queries; MedQ-Description covers both no-reference and comparison description sub-tasks with 708 expert-annotated samples, evaluated through a four-dimensional judging protocol. Comprehensive evaluation of 23 state-of-the-art MLLMs reveals that current models exhibit only preliminary and unstable capabilities, and highlights the urgent need for targeted optimization of MLLMs in medical IQA. Project & Dataset: https://github.com/liujiyaoFDU/MedQ-Bench

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35842

MedVTok: A General-Purpose Medical Visual Tokenizer

Chenglong Ma, Yuanfeng Ji, Junzhi Ning, Jiyao Liu, Yujie Wei, Shaohao Rui, Chaoyang Zhang, Wenjie Li 等 21 位作者

原文摘要

Multimodal medical AI requires a common visual interface for encoding heterogeneous imaging evidence for patient condition modeling. However, existing medical visual tokenizers are often tied to a specific dimension and imaging modality, forcing multimodal systems to rely on fragmented and poorly reusable representations. We introduce MedVTok, a general-purpose visual tokenizer that maps 2D images and 3D volumes from diverse imaging modalities into a unified token space. Building such a tokenizer poses two key challenges: preserving anatomical structure across dimensions and capturing clinical semantics across modalities. To this end, MedVTok introduces (i) slice differential regularization, which explicitly models inter-slice anatomical coherence missing in previous per-pixel/per-voxel supervision, and (ii) multi-expert representation alignment, which integrates knowledge from multiple medical expert encoders while retaining modality-specific complementary knowledge. Trained on 28M images and 70K volumes across 9 imaging modalities, MedVTok supports classification, retrieval, segmentation, synthesis, visual question answering, and medical report generation through a single tokenizer. Across 24 benchmarks, MedVTok achieves state-of-the-art performance, demonstrating a scalable token interface that unifies medical image dimension, modality, and task usage.

eess.IV · cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1088ACL Anthology · ACL 2026

LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models

Jian Gao, Richeng Xuan Tencent, Zhaolu Kang, Dingshi Liao, Wenxin Huang, Zongmou Huang, Yangdi Xu, Bowen Qin Beijing Academy of Artificial Intelligence 等 12 位作者

原文摘要

The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill this gap, we introduce LaoBench, the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao. LaoBench contains 17,000+ expert-curated samples across three dimensions: culturally grounded knowledge application, curriculum-aligned K12 education, and bilingual translation among Lao, Chinese, and English. It includes open-source and held-out subsets, where the held-out portion enables secure black-box evaluation via a controlled service to improve fairness and data security. We construct LaoBench with a hybrid pipeline that combines expert authoring with agent-assisted verification, ensuring linguistic accuracy, cultural relevance, and educational validity. We evaluate diverse state-of-the-art open-source and closed-source LLMs, and find that even strong multilingual models lag behind human experts, particularly in culturally grounded reasoning and translation fidelity. We hope LaoBench will catalyze research on Lao and other underrepresented Southeast Asian languages for more inclusive multilingual evaluation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表