OctoT2I: A Self-Evolving Agentic Text-to-Image Router

• URL: http://arxiv.org/abs/2606.01803v1
• Date: Mon, 01 Jun 2026 07:21:01 GMT
• Title: OctoT2I: A Self-Evolving Agentic Text-to-Image Router
• Abstract summary: We introduce OctoT2I, a novel agentic framework that reformulates the Text-to-Image task as a joint optimization of generation quality and inference efficiency.<n>OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory.<n>Experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval.
• Score: 37.128136650887654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns from single-model scaling. Agentic T2I methods emerged to alleviate this bottleneck by using multiple models. However, existing agentic T2I methods suffer from three key challenges: reliance on expensive handcrafted priors or human annotations, rigid single-path decision mechanisms, and a neglect of inference efficiency. To address these challenges, we introduce OctoT2I, a novel agentic framework that reformulates the T2I task as a joint optimization of generation quality and inference efficiency. OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory. This strategy is enabled by a knowledge base built from scratch by our novel Self-Evolving Mechanism. This mechanism, which requires no human supervision, first autonomously defines foundational Conceptual Dimensions (eg, style, color, count) and then intelligently explores their combinations via an iterative" Propose--Solve--Evaluate--Learn"(PSEL) loop. The PSEL loop efficiently discovers each tool's capability frontier, driving continuous improvement without external guidance. Extensive experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval while delivering a 90.3% inference speedup and a 56.6% energy-efficiency gain over the leading baseline (Flow-GRPO), striking an exceptional balance between performance and efficiency. Code and models will be made available.

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