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ImageRSI: Verification-Driven Recursive Self-Improvement for Agentic Image Generation
Shuo Han, Zhilin Wang, Peng Ling +5
Complex text-to-image requests remain difficult to satisfy reliably. Verifying and repairing an image can fix an individual output, but a successful repair does not necessarily car…
A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
Yating Gao, Zhenlong Zhu, Wen Zihao +6
Few-step text-to-image generators produce high-quality images with few network forward passes, yet struggle with complex prompts involving multiple subjects, interactions, spatial…
Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning
Hao-Xuan Ma, Yihao Liu, Yutao Sun +8
Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to all editable latent tokens. F…
Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models
Yanting Miao, Yutao Sun, Dexin Wang +8
Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However…
Rationale Matters: Learning Transferable Rubrics via Proxy-Guided Critique for VLM Reward Models
Weijie Qiu, Dai Guan, Junxin Wang +6
Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict…
Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models
Junxin Wang, Dai Guan, Weijie Qiu +7
Vision-language process reward models (VL-PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test-time scaling. However, they often funct…