1. The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge

   http://arxiv.org/abs/2601.07237v1 · arxiv

   This paper summarizes the ICASSP 2026 Automatic Song Aesthetics Evaluation (ASAE) Challenge, which focuses on predicting the subjective aesthetic scores of AI-generated songs. The challenge consists of two tracks: Track 1 targets the prediction of the overall musicality score, while Track 2 focuses

2. The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report

   http://arxiv.org/abs/2604.03198v1 · arxiv

   This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26

3. ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims

   http://arxiv.org/abs/2603.26449v1 · arxiv

   Automatically verifying climate-related claims against scientific literature is a challenging task, complicated by the specialised nature of scholarly evidence and the diversity of rhetorical strategies underlying climate disinformation. ClimateCheck 2026 is the second iteration of a shared task add

4. The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

   http://arxiv.org/abs/2604.14558v1 · arxiv

   This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\t

5. TTLab at AlexandriaX-2026: A Fine-Tuned Surface Tagger for Arabic Machine-Translation Error-Span Detection and Classification

   http://arxiv.org/abs/2609.29633v1 · arxiv

   We present TTLab's submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as token-level classification over surface forms, preserving character offsets to ensure exact alignment with the evaluation metric. To handle severe label

6. CuriosAI Submission to the CASTLE Challenge at EgoVis 2026

   http://arxiv.org/abs/2605.27800v1 · arxiv

   CASTLE 2026 asks 185 multiple-choice questions over 600+ hours of synchronized multi-view egocentric video. We explore two approaches on top of a shared multimodal preprocessing layer, including per-person timelines, speaker-resolved transcripts, and multi-VLM caption ensembles. Approach A, SVA: Sea

7. Claim2Source at CheckThat! 2026: Improving Multilingual Scientific Claim-Source Retrieval with Verification-based Re-Ranking

   http://arxiv.org/abs/2607.04043v1 · arxiv

   Multilingual scientific claim-source retrieval aims to identify the scientific publication supporting a claim shared on social media. This task is challenging because claims often differ from source publications in terms of language, wording, and level of detail, which weakens the connection between

8. The Interspeech 2026 Audio Encoder Capability Challenge for Large Audio Language Models

   http://arxiv.org/abs/2603.22728v1 · arxiv

   This paper presents the Interspeech 2026 Audio Encoder Capability Challenge, a benchmark specifically designed to evaluate and advance the performance of pre-trained audio encoders as front-end modules for Large Audio Language Models (LALMs). While LALMs have shown remarkable understanding of comple

9. Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches

   http://arxiv.org/abs/1205.0451v2 · arxiv

   Smartphones have recently gained significant popularity in heavy mobile processing while users are increasing their expectations toward rich computing experience. However, resource limitations and current mobile computing advancements hinder this vision. Therefore, resource-intensive application exe

10. AutoMine Solution for AV2 2026 Scenario Mining Challenge

    http://arxiv.org/abs/2606.11874v1 · arxiv

    With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-driven evaluation. In this paper, we propose AutoMine, a robust self-refining scenario mining method based on LLMs and V

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