AI RESEARCH
Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
arXiv CS.CV
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ArXi:2604.17473v1 Announce Type: new Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions. While recent Video Large Language Models(Video-LLMs) have largely advanced VLN, they remain highly susceptible to State Drift in long scenarios. In these cases, the agent's internal state drifts away from the true task execution state, leading to aimless wandering and failure to execute essential maneuvers in the instruction.