2 papers
cs.LG2026
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context
Bo Li, Yue Yang, Yujin Huang +6
Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do not fully address dynamic gra…
cs.CV2026
PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps
Junlin Long, Zeyu Zhang, Xu Deng +5
Embodied visual navigation, where an agent perceives a complex environment and acts to reach a goal from raw sensory input, underpins a wide range of applications such as household…