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QuadTok: Quadtree Visual Tokenizer for Autoregressive Image Generation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 05:18:24 UTC.

Briefings

QuadTok introduces a hierarchical quadtree structure for visual tokenization, enabling efficient autoregressive image generation. The system reduces token usage by 10% on ImageNet and 9% on COCO compared to fixed grids, while maintaining reconstruction fidelity. A 947M GPT-style model achieves a 2.08 gFID on ImageNet 256x256 benchmarks, and the framework supports zero-shot spatially controlled generation.

Key points

• QuadTok uses a hierarchical quadtree structure to balance 2D spatial binding with 1D sequence flexibility.
• The system saves 10% of tokens on ImageNet and 9% on COCO compared to fixed 256-token grids.

Context

This research presents a novel framework for visual tokenization and autoregressive image generation, addressing the gap between 2D spatial binding and 1D sequence-level flexibility in generative models.

Limitations

Evidence is limited to reported performance metrics and code availability; no additional background context or causal claims are supported by the provided source.

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