arXiv:2604.06269v2 Announce Type: replace-cross
Abstract: Automated single-cell annotation is difficult when the most abundant genes are not the most discriminative ones, or when a target state is poorly covered by a fixed reference atlas. GPTCelltype…
arXiv:2605.05460v1 Announce Type: new
Abstract: The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human…
arXiv:2604.26799v2 Announce Type: replace
Abstract: 3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-training compression…
arXiv:2605.06635v1 Announce Type: new
Abstract: Large language models (LLMs) power deep research agents that synthesize information from hundreds of web sources into cited reports, yet these citations cannot be reliably verified. Current approaches ei…
arXiv:2510.10241v2 Announce Type: replace
Abstract: Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised neural methods based …
arXiv:2605.06460v1 Announce Type: new
Abstract: Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve strong quality through fi…
arXiv:2605.06642v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which…
arXiv:2605.05957v1 Announce Type: new
Abstract: LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply rather than correct. We term this failure mode \emph{cor…
arXiv:2510.19316v2 Announce Type: replace
Abstract: Large Multimodal Models encode extensive factual knowledge in their pre-trained weights. However, its knowledge remains static and limited, unable to keep pace with real-world developments, which hin…
arXiv:2605.06407v1 Announce Type: cross
Abstract: Integrating speech understanding and generation is a pivotal step toward building unified speech models. However, the different representations required for these two tasks currently pose significant c…