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This framework provides a balanced approach to research idea generation, achieving high-quality outcomes in the experiment by dynamically navigating the trade-offs among novelty, feasibility, and effectiveness.
ConVLM is presented, which improves LVLM reasoning through Group Relative Policy Optimization-based reinforcement learning with a novel consistency reward, and ConVBench, a complex vision-centric reasoning benchmark in which each image is paired with two logically equivalent questions across six categories.
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