{"slug": "can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study", "title": "Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference", "summary": "Researchers introduced BudgetDoc, the first multimodal benchmark for model-budget-performance trade-offs in document tasks, and trained DRB (Document-Reasoning Balancer), a 1B-parameter pre-flight estimator using SigLIP-2 and Qwen3-0.6B, achieving a 0.753 weighted F1. In dynamic budget allocation across five frontier models and three datasets, DRB matched or improved F1 scores in 9 of 15 configurations compared to always-maximum-budget baselines while reducing cost.", "body_md": "arXiv:2608.18591v1 Announce Type: cross\nAbstract: Uniformly allocating inference reasoning budgets to LLMs is expensive and prone to over-thinking penalties; especially in document tasks where visual layouts drive complexity. To address this, we introduce BudgetDoc, the first multimodal benchmark providing explicit supervision for model-budget-performance trade-offs across three document tasks. Using BudgetDoc, we train DRB (Document-Reasoning Balancer), an approx. 1B-parameter pre-flight estimator (SigLIP-2 + Qwen3-0.6B) that predicts ordinal model performance across budget levels, achieving a 0.753 weighted F1. When dynamically allocating reasoning budgets across five frontier models and three datasets, DRB matches or improves F1 scores compared to always-maximum-budget baselines in 9 of 15 configurations while drastically reducing cost. Finally, preliminary evaluations demonstrate DRB's potential to generalize to cross-model selection.", "url": "https://wpnews.pro/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study", "canonical_source": "https://www.machinebrief.com/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-pe-u594", "published_at": "2026-08-20 04:00:00+00:00", "updated_at": "2026-08-20 06:14:13.542831+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["BudgetDoc", "DRB", "SigLIP-2", "Qwen3-0.6B"], "alternates": {"html": "https://wpnews.pro/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study", "markdown": "https://wpnews.pro/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study.md", "text": "https://wpnews.pro/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study.txt", "jsonld": "https://wpnews.pro/news/can-a-lightweight-multimodal-model-estimate-llm-reasoning-performance-a-study.jsonld"}}