arXiv:2604.18076v1 Announce Type: new
Abstract: Diffusion-based image synthesis has emerged as a promising source of synthetic training data for AI-based object detection and classification. In this work, we investigate whether images generated with d…
arXiv:2601.19792v3 Announce Type: replace
Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to collaborate remains limited by a critical deficit: an…
arXiv:2601.21257v2 Announce Type: replace
Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multiple LMs collaborate, compose, and complement each o…
arXiv:2602.11092v2 Announce Type: replace
Abstract: Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical explora…
arXiv:2604.18578v1 Announce Type: new
Abstract: Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robustness across domains. However, there is a significan…
arXiv:2604.08626v2 Announce Type: replace
Abstract: Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection–recovering the extent, location, and orientatio…
arXiv:2602.06221v2 Announce Type: replace
Abstract: Multiple-choice question answering (MCQA) is standard in NLP, but benchmarks lack rigorous quality control. We present BenchMarker, an education-inspired toolkit using LLM judges to flag three common…
arXiv:2505.23941v4 Announce Type: replace
Abstract: Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may notoriously sway their outputs towards wrong or biased answer…
arXiv:2604.16813v1 Announce Type: cross
Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insufficiently characterized. To address this gap, we intro…
arXiv:2604.10321v2 Announce Type: replace
Abstract: In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academic research, most m…