Visual Sufficiency Landscapes: Understanding Query-Conditioned Reliability in Visual Token Pruning

Key points

• This motivates a combinatorial question: how many visual subsets can preserve model behavior, and how does their abundance depend on the query?1
• We introduce visual sufficiency landscapes to characterize this abundance.1
• At a fixed layer and budget, visual sufficiency probability is the fraction of uniformly sampled subsets that jointly preserve the dense model's answer, reference-answer likelihood, and output distribution.1
• These results establish successful-subset abundance as a measurable dimension of local prunability and connect it to the identification of high-risk pruning conditions.1

Sources (1)

• [1]Visual Sufficiency Landscapes: Understanding Query-Conditioned Reliability in Visual Token Pruning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 02:46 AM

  Different visual token pruning strategies retain different subsets while maintaining similar task performance.

  This motivates a combinatorial question: how many visual subsets can preserve model behavior, and how does their abundance depend on the query?

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