SciGen-Verifier brings explainable, reasoning-driven verification to scientific image generation
Jiali Chen · hf · 2026-09-29
This work tackles verifying AI-generated scientific images (circuits, geometric constructions, plots), where errors stem from domain knowledge and multi-step reasoning rather than surface artifacts. Contributions: the SciGen-Verify benchmark with a three-tier protocol (judgment → explanation → corrective editing instruction); SciGen-Verifier, a reasoning-driven verifier trained via cold-start SFT plus curriculum-based two-stage RL with rubric-guided process rewards; and competitive performance against much larger proprietary models, usable as an online critic for iterative image correction.
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