GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection 事件

GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection arXiv:2605.28534v1 Announce Type: new Abstract: Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by a lack of world knowledge about GUI operations. Existing solutions typically rely on expensive multi-agent scaffolding or conventional post-training paradigms, such as Su