TerraAgent: Demonstration-Grounded Multi-Agent Reasoning for Long-Context Remote Sensing Visual Question Answering

Author1, Author2, Author3, Author4, Author5

RAGmultimodalreasoning

2610.12517

Builder Relevance

80%

2h ago

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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