arXiv:2603.20253v2 Announce Type: replace-cross Abstract: Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources. As a result, metrics like pass@k become impractical under realistic budget constraints. To address this gap, we introduce SimulCost, the first benchmark targeting cost-sensitive parameter tuning in physics simulations. SimulCost compares LLM tuning cost-sensitive parameters against traditional scanning approach in both accuracy and computational cost, spanning 2,947 single-round (initial gu
The rapid advancement of large language models (LLMs) and their application to complex scientific problems, coupled with increasing computational costs, necessitates new benchmarks for efficiency and effectiveness.
This benchmark addresses a critical gap in evaluating AI agents for scientific tasks by considering real-world costs like simulation time, moving beyond just token cost, which is crucial for practical implementation in fields like physics and engineering.
The focus for evaluating LLM agents in scientific applications shifts from purely performance-based metrics to cost-aware metrics, promoting more efficient and resource-conscious AI development for specialized domains.
- · AI developers focused on scientific applications
- · Compute infrastructure providers
- · Research institutions with budget constraints
- · Physics simulation software vendors
- · LLM agents optimized purely for accuracy without cost consideration
- · Organizations with inefficient simulation pipelines
Scientific LLM agents will be developed with an inherent focus on computational and resource efficiency.
This could lead to optimized hardware and software co-design specifically for cost-effective scientific AI simulations.
Reduced simulation costs could accelerate scientific discovery and engineering innovation by lowering barriers to entry for complex modeling.
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Read at arXiv cs.LG