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Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs

Research Efficiency & Systems

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TL;DR - SmartVL jointly adapts visual-token count and LLM computation to each input and inference budget. Coordinated resource allocation improves the accuracy-efficiency tradeoff over methods that optimize these dimensions independently.

  • Uses separate vision-token and LLM-compute controllers linked by a shared budget encoding.
  • Dynamically selects informative visual tokens and adjusts language-model computation.
  • Employs a differentiable latency estimator for end-to-end, budget-aware training.
  • Demonstrates stronger accuracy-efficiency Pareto frontiers across multiple multimodal benchmarks.

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Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs

arXiv cs.CV Pengcheng Wang, Zhiquan Wang, Jayoung Lee, Zhuoyan Xu, Ran Xu, Saurabh Bagchi, Yin Li, Somali Chaterji 2026-07-22 arXiv:2607.20357
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-04 10:43:34.115684 UTC

TL;DR - SmartVL jointly adapts visual-token count and LLM computation to each input and inference budget. Coordinated resource allocation improves the accuracy-efficiency tradeoff over methods that optimize these dimensions independently.

  • Uses separate vision-token and LLM-compute controllers linked by a shared budget encoding.
  • Dynamically selects informative visual tokens and adjusts language-model computation.
  • Employs a differentiable latency estimator for end-to-end, budget-aware training.
  • Demonstrates stronger accuracy-efficiency Pareto frontiers across multiple multimodal benchmarks.
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