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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
PGT generates synthetic tasks via geometric overlays on images to supply dense visual supervision, improving spatial and relational understanding in MLLMs by up to 20% on targeted benchmarks.
A Nash equilibrium framework for training-free multimodal step verification that uses cross-modal agreement and disagreement signals for filtering and ranking reasoning steps.
citing papers explorer
-
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.
-
PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs
PGT generates synthetic tasks via geometric overlays on images to supply dense visual supervision, improving spatial and relational understanding in MLLMs by up to 20% on targeted benchmarks.
-
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.