arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchmarks due to data contamination, insufficient complexity, and prohibitive human labor costs. Here we present SPM-Bench, an original, PhD-level multimodal benchmark specifically designed for scanning probe microscopy (SPM). We propose a fully automated data synthesis pipeline that ensures both high authority and low-cost. By employing Anchor-Gated Sieve (AGS) technology, we efficiently extract high-v
The rapid advancement of large language models necessitates specialized benchmarks to accurately assess their capabilities and limitations in scientific domains.
This benchmark addresses a critical gap in evaluating LLM proficiency for complex scientific tasks, enabling more reliable development and deployment of AI in research.
The introduction of SPM-Bench provides a high-authority, low-cost method to benchmark LLMs in scanning probe microscopy, moving beyond general reasoning tasks.
- · AI developers
- · Materials science researchers
- · Scientific instrument manufacturers
- · LLMs with inadequate scientific data training
- · Generic AI benchmarking strategies
Improved LLM performance in specialized scientific tasks due to better targeted training and evaluation.
Accelerated discovery and analysis in fields like materials science and nanotechnology through AI-assisted microscopy.
Enhanced automation of scientific research workflows, potentially reducing human labor costs and accelerating the pace of innovation across multiple scientific disciplines.
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Read at arXiv cs.AI