# Explainable Verification of Scientific Image Generation

The work builds SciGen-Verify, a benchmark for explainable verification of scientific image generation (1,350 samples: 406 instruction following, 287 multidisciplinary reasoning, 657 world knowledge, with a three-tier cascading protocol over binary judgement, explanation, and editing instruction), and develops SciGen-Verifier on Qwen3-VL-8B-Instruct trained by cold-start supervised fine-tuning (48,311 instances) followed by a curriculum-based two-stage reinforcement learning pipeline (rubric-guided process reward, then outcome reward), reaching 86.28% overall judgement accuracy, 81.79% explanation accuracy, and 57.42% editing-instruction accuracy on the benchmark while also serving as an online critic for iterative image rectification.

The work builds SciGen-Verify, a benchmark for explainable verification of scientific image generation (1,350 samples: 406 instruction following, 287 multidisciplinary reasoning, 657 world knowledge, with a three-tier cascading protocol over binary judgement, explanation, and editing instruction), and develops SciGen-Verifier on Qwen3-VL-8B-Instruct trained by cold-start supervised fine-tuning (48,311 instances) followed by a curriculum-based two-stage reinforcement learning pipeline (rubric-guided process reward, then outcome reward), reaching 86.28% overall judgement accuracy, 81.79% explanation accuracy, and 57.42% editing-instruction accuracy on the benchmark while also serving as an online critic for iterative image rectification.