arXiv:2607.02032v1 Announce Type: cross Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic LLM benchmarks that test individual capabilities (e.g., reasoning, code generation) are fast and cheap to run. In this paper, we investigate whether performance on expensive agentic benchmarks can be accurately predicted by the performance on a small, carefully selected subset of atomic evaluation instances. We intr
The rapid development and increasing complexity of LLM agents necessitate more efficient and cost-effective evaluation methods to accelerate progress and adoption.
The high cost and time required for evaluating LLM agents are critical bottlenecks preventing faster iteration and broader integration, making efficient evaluation a key enabler.
A more efficient and accessible proxy for evaluating agentic capabilities could significantly lower the barrier to entry for LLM evaluation, speeding up research and development.
- · LLM developers
- · AI researchers
- · Cloud providers offering AI services
- · Startups building agentic AI
The ability to quickly and cheaply evaluate agent performance will accelerate the development cycle of AI agents.
Faster development could lead to a more rapid deployment and integration of autonomous AI agents across various industries.
This acceleration might further intensify the competition in the AI agent space and lead to more sophisticated and capable agentic systems emerging sooner.
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