Survey maps a shift toward harder, more realistic AI agent evaluations
A new arXiv survey says evaluation of LLM-based agents is moving beyond simple task scores toward more realistic, longer-horizon tests. The authors analyze benchmarks and frameworks across planning, tool use, self-reflection, memory, web and software tasks, generalist agents, and evaluation frameworks. Their main takeaway is that the field is increasingly using continuously updated benchmarks that better reflect real deployment settings. The paper also flags recurring gaps: cost-efficiency, safety, robustness, and the need for finer-grained, scalable evaluation methods. The work is a survey rather than a new benchmark, but it gives a useful snapshot of where agent evaluation research is heading and what still remains undermeasured.
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Originally published 10/19/2026, 8:11:55 PM