Author

Matteo Chinazzi

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#machine learning Preprint Sep 2026

Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models

Evaluating the capability and efficiency of LLMs from the DeepSeek-R1-Distill model family across four classes of arithmetic and algorithmic reasoning problems reveals potential limitations of naive scaling as a strategy for developing more capable AI systems.

Moritz Laber, Zohair Shafi, Germans Savcisens et al. · 0 citations

#machine learning Sep 2025

SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA

SimulRAG, a simulator-based RAG framework with a generalized retrieval interface that translates between text and simulator parameters/outputs, is proposed, which improves informativeness and factuality over the strongest adapted RAG baselines, while UE+SBA enhances claim-level efficiency and quality.

Haozhou Xu, D. Wu, M. Chinazzi et al. · arXiv.org · 3 citations