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TerraAgent: Demonstration-Grounded Multi-Agent Reasoning for Long-Context Remote Sensing Visual Question Answering
Author1, Author2, Author3, Author4, Author5
RAGmultimodalreasoning
2610.12517
Builder Relevance
8h ago80%
Abstract
This paper presents AVA, a framework that enhances remote sensing visual question answering by improving accuracy and reducing errors through demonstration retrieval and multi-step reasoning.
Reality Card
Core Claim
AVA improves average accuracy by up to 2.40 points over the strongest same-setting baselines while significantly reducing the prior-only answer rate and hallucination rate.
Method / Result
Increases visual-dependency score from 0.28 to 0.49.
Limitations
The framework's performance is evaluated without fine-tuning, which may limit its adaptability to specific use cases.
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