Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
A Nash equilibrium framework for training-free multimodal step verification that uses cross-modal agreement and disagreement signals for filtering and ranking reasoning steps.
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.
citing papers explorer
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Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.
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A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
A Nash equilibrium framework for training-free multimodal step verification that uses cross-modal agreement and disagreement signals for filtering and ranking reasoning steps.
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Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.