Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning

• URL: http://arxiv.org/abs/2605.14876v2
• Date: Fri, 15 May 2026 05:10:39 GMT
• Title: Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning
• Abstract summary: Closed-Loop Visual Reasoning (CLVR) is a comprehensive system that couples visual-language logical planning with pixel-level diffusion generation.<n> CLVR introduces an automated data engine with step-level visual verification to synthesize reliable reasoning trajectories.<n>To mitigate the severe latency bottleneck caused by iterative denoising, we propose $$-Space Weight (DSWM)
• Score: 11.461705552241163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex semantics and faces diminishing returns from parameter scaling. While recent multi-step reasoning approaches show promise, they are hindered by ungrounded planning hallucinations lacking verification, monolithic post-hoc reflection, long-context optimization instabilities, and prohibitive inference latency. To overcome these bottlenecks, we propose the Closed-Loop Visual Reasoning (CLVR) framework, a comprehensive system that deeply couples visual-language logical planning with pixel-level diffusion generation. CLVR introduces an automated data engine with step-level visual verification to synthesize reliable reasoning trajectories, and proposes Proxy Prompt Reinforcement Learning (PPRL) to resolve long-context optimization instabilities by distilling interleaved multimodal histories into explicit reward signals for accurate causal attribution. Furthermore, to mitigate the severe latency bottleneck caused by iterative denoising, we propose $Δ$-Space Weight Merge (DSWM), a theoretically grounded method that fuses alignment weights with off-the-shelf distillation priors, reducing the per-step inference cost to just 4 NFEs without requiring expensive re-distillation. Extensive experiments demonstrate that CLVR outperforms existing open-source baselines across multiple benchmarks and approaches the performance of proprietary commercial models, unlocking general test-time scaling capabilities for complex visual generation.

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