Paper

Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling

Li, Li, Dernoncourt, Franck, Chai, Wenhao, Tu, Zhengzhong, Yang, Siyuan, Zhu, Jiacheng, Zhang, Jiayi, Ye, Ruijie, Zhao, Yue, Zhu, Zihao, Fu, Tianfu, Liu, Zhuoxin, Rossi, Ryan

2026

Overview

We study instruction-based image editing under professional workflows and identify three persistent challenges: (i) editors often over-edit, modifying content beyond the user's intent; (ii) existing models are largely single-turn, while multi-turn edits can alter object faithfulness; and (iii) evaluation at around 1K resolution is misaligned with real workflows that often operate on ultra high-definition images (e.g., 4K). We propose Agent Banana, a hierarchical agentic planner-executor framework for high-fidelity, object-aware, deliberative editing. Agent Banana introduces two key mechanisms: (1) Context Folding, which compresses long interaction histories into structured memory for stable long-horizon control; and (2) Image Layer Decomposition, which performs localized layer-based edits to preserve non-target regions while enabling native-resolution outputs. To support rigorous evaluation, we build HDD-Bench, a high-definition, dialogue-based benchmark featuring verifiable stepwise targets and native 4K images (11.8M pixels) for diagnosing long-horizon failures. On HDD-Bench, Agent Banana achieves the best multi-turn consistency and background fidelity (e.g., IC 0.871, SSIM-OM 0.84, LPIPS-OM 0.12) while remaining competitive on instruction following, and also attains strong performance on standard single-turn editing benchmarks. We hope this work advances reliable, professional-grade agentic image editing and its integration into real workflows.

• Publisher: Cornell University Library, arXiv.org
• Subject: Bananas / Benchmarks / Editing / High definition / Tooling