Dimensions overall score 7.0

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• Existing training-free VLN agents relying on greedy frontier selection and passive spatial memory exhibit inefficient behaviors like local oscillation and redundant revisiting.limitation 90%
• The inefficiency in existing VLN agents stems from a lack of metacognitive capabilities, specifically the inability to monitor exploration progress, diagnose strategy failures, or adapt accordingly.technical 85%
• MetaNav integrates spatial memory, history-aware planning, and reflective correction to address inefficiencies in VLN.method 90%
• MetaNav's spatial memory builds a persistent 3D semantic map.method 90%
• MetaNav's history-aware planning penalizes revisiting to improve navigation efficiency.method 90%
• MetaNav's reflective correction detects stagnation and uses an LLM to generate corrective rules for future frontier selection.method 90%
• MetaNav achieves state-of-the-art performance on GOAT-Bench, HM3D-OVON, and A-EQA benchmarks.result 85%
• MetaNav reduces VLM queries by 20.7% compared to existing approaches.result 90%

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