Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 20·5 min read
Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference
TL;DR
BudgetDoc introduces a multimodal benchmark for estimating reasoning performance in large language models (LLMs), enabling dynamic budget allocation. The Document-Reasoning Balancer (DRB) achieves improved performance while reducing costs across various document tasks.
✦ Why It Matters
Implement DRB to optimize reasoning budgets in your document processing tasks today.
Key Takeaways
- Utilize BudgetDoc for multimodal document tasks.
- Adopt DRB to predict model performance efficiently.
- Reduce costs while maintaining or improving F1 scores.
- Explore cross-model selection capabilities of DRB.
How It Works
BudgetDoc provides a structured framework for evaluating how different reasoning budgets affect model performance in document tasks. DRB uses a combination of SigLIP-2 and Qwen3-0.6B to estimate performance, allowing for dynamic adjustments based on the complexity of the task and the model's capabilities.