AI RESEARCH
EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
arXiv CS.CV
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ArXi:2604.18320v1 Announce Type: new Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degradation as model predictions drift, while template-based methods are confined to a static set of transformations that cannot adapt in difficulty or diversity. We contend that robust, continuous self-improvement requires not only deterministic external feedback independent of the model's internal certainty, but also a mechanism to perpetually diversify the.