arXiv:2603.16663v3 Announce Type: replace-cross
Abstract: The AIED community envisions AI evolving “from tools to teammates,” yet our understanding of AI teammates remains limited to dyadic human-AI interactions. We offer a different vantage point: a …
arXiv:2604.02954v1 Announce Type: cross
Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graphs. Leveraging communi…
arXiv:2505.15323v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly evaluated on multiple-choice question answering (MCQA) tasks using *first-token probability* (FTP), which selects the answer option whose initial token h…
arXiv:2508.08645v2 Announce Type: replace
Abstract: As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use agents that mimic human interactions from graphical …
arXiv:2604.02490v1 Announce Type: cross
Abstract: Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolving threats. Existing…
arXiv:2509.07553v3 Announce Type: replace
Abstract: With the rapid progress of multimodal large language models, operating system (OS) agents become increasingly capable of automating tasks through on-device graphical user interfaces (GUIs). However, …
arXiv:2604.03212v1 Announce Type: new
Abstract: Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seasons, cities, and sensors. Despite recent progress, many…
arXiv:2604.02707v1 Announce Type: cross
Abstract: Humanoid robot technologies have demonstrated immense potential for minimally invasive surgery (MIS). Unlike dedicated multi-arm surgical platforms, the inherent dual-arm configuration of humanoid robo…
arXiv:2604.03058v1 Announce Type: cross
Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like “am I in the wrong?” rather than providing genuine assessment. We hypothesize that this behavior arises from incorrect ass…
arXiv:2604.02692v1 Announce Type: new
Abstract: Accurate document parsing requires both robust content recognition and a stable parser interface. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full detector…