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DART-VLN

model1 events
papersTODAY 04:00 UTC

DART-VLN tackles memory decay and looping in vision-language navigation agents

A new arXiv paper proposes a method called DART-VLN for memory-based agents that navigate environments using discrete vision-language instructions. It targets two failure modes seen at inference time: reliance on outdated stored information and agents getting stuck in repetitive behavior. The approach combines test-time memory decay with an anti-loop regularization technique to improve navigation robustness.

research/agents/vision-language/navigation