1. UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering

   http://arxiv.org/abs/2608.27467v1 · arxiv

   We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. We participated in Subtasks 2 (evidence identification), 3 (answer generation), and 4 (answer-evidence alignment). For Subtasks 2 and 3, we propose an answer-firs

2. AutoRestTest at the SBFT 2026 Tool Competition

   http://arxiv.org/abs/2607.01063v1 · arxiv

   Large input spaces and complex inter-operation dependencies make black-box REST API testing challenging. AutoRestTest combines a Semantic Property Dependency Graph, multi-agent reinforcement learning, and large language models to intelligently explore large API input spaces. In the SBFT 2026 REST Le

3. YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

   http://arxiv.org/abs/2501.13400v4 · arxiv

   Note: This is a preliminary version of the manuscript. The final, peer-reviewed, and substantially revised version has been published in Jurnal RESTI. Readers are encouraged to access and cite the published version: DOI: https://doi.org/10.29207/resti.v10i2.6598 In the field of deep learning-based

4. Second MOASEI Competition at AAMAS'2026: A Technical Report

   http://arxiv.org/abs/2607.03399v1 · arxiv

   We describe the 2026 Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a benchmark event for evaluating multi-agent decision-making under open-system conditions. Building on the inaugural 2025 competition, the 2026 edition retained wildfire fighting, cybersecurity, and ride-

5. Proceedings of HLPP 2026: 19th International Symposium on High-Level Parallel Programming and Applications

   http://arxiv.org/abs/2607.12917v1 · arxiv

   This volume contains the ten peer-reviewed papers presented at HLPP 2026, the 19th International Symposium on High-Level Parallel Programming and Applications, held on 9-10 July 2026 at the Institut Henri Poincare in Paris, France. The symposium covers high-level approaches to parallel programming:

6. NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report

   http://arxiv.org/abs/2604.17070v2 · arxiv

   This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in images. Rip currents are hazardous nearshore flows that cause many beach-related fatalities worldwide, yet remain difficult to identify because their

7. ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification

   http://arxiv.org/abs/2607.28637v1 · arxiv

   This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-H

8. Status of the $S_8$ Tension: A 2026 Review of Probe Discrepancies

   http://arxiv.org/abs/2602.12238v2 · arxiv

   The parameter $S_8 \equiv σ_8 (Ω_m/0.3)^{0.5}$ quantifies the amplitude of matter density fluctuations. A persistent discrepancy exists between early-universe CMB observations and late-universe probes. This review assesses the ``$S_8$ tension'' against a new 2026 baseline: a unified ``Combined CMB''

9. Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

   http://arxiv.org/abs/2609.39975v1 · arxiv

   This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. BioASQ is an international challenge series that supports progress in biomedical language processing tasks ranging from semantic

10. VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion

    http://arxiv.org/abs/2607.11706v1 · arxiv

    Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-proce

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