arXiv:2604.18557v1 Announce Type: cross
Abstract: Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence, due to severe data scarcity, complexities in multi-agent coordination, and limited gen…
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:2506.01732v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are pre-trained on large data from different sources and domains. These datasets often contain trillions of tokens, including large portions of copyrighted or proprietary…
arXiv:2604.16841v1 Announce Type: new
Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In …
arXiv:2604.17306v1 Announce Type: new
Abstract: This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge aims to recover high-re…
arXiv:2505.19237v2 Announce Type: replace-cross
Abstract: Self-recognition — the ability to maintain an internal representation of one’s own body within the environment — underpins intelligent, autonomous behavior. As a foundational component of the…
arXiv:2604.16665v1 Announce Type: new
Abstract: Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional app-based systems, reliant on manual input, struggl…
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…
arXiv:2510.06133v2 Announce Type: replace
Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions while remasking the…
arXiv:2506.05606v5 Announce Type: replace
Abstract: Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating “believable” human behaviors, evaluating thei…