Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling
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.
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Ruijie Ye
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Jiayi Zhang
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Zhuoxin Liu
698be8996052d3bed96309af
Zihao Zhu
claimed_verified
2026-02-11T11:15:16.736Z
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Zihao Zhu
SingleBicycle
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SingleBicycle
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Siyuan Yang
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Li Li
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Tianfu Fu
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Franck Dernoncourt
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2026-02-17T15:50:37.345Z
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Franck Dernoncourt
Franck-Dernoncourt
user
Franck-Dernoncourt
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Yue Zhao
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Jiacheng Zhu
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Ryan Rossi
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Wenhao Chai
698be8996052d3bed96309b8
Zhengzhong Tu
Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling
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.
Agent Banana addresses challenges in instruction-based image editing through a hierarchical framework with context folding and image layer decomposition for high-fidelity, multi-turn editing at ultra-high resolution.
agentic planner-executor framework context folding image layer decomposition multi-turn editing high-fidelity editing object-aware editing deliberative editing HDD-Bench dialogue-based benchmark native-resolution outputs long-horizon control stepwise targets