arXiv:2510.09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases. In this paper, we make two major steps towards the rigorous assessment of human-agent interactions. First, we propose PULSE, a framework for more efficient human-centric evaluation of agent designs, which comprises collecting user feedback, training an ML model to predict user satisfaction, and computing results by combining human satisfaction ratings with model-generated pseud
As LLM-powered agents proliferate, the critical need for human-centric evaluation methods to move beyond accuracy benchmarks is becoming increasingly urgent.
This development addresses a key bottleneck in the deployment and refinement of AI agents, enabling more robust, user-aligned, and effective real-world applications.
The proposed PULSE framework offers a structured approach to evaluate human-agent interaction, shifting focus from pure automation metrics to collaborative performance and user satisfaction.
- · AI agent developers
- · Businesses adopting AI agents
- · UX researchers
- · Users of AI systems
- · Companies relying solely on traditional LLM benchmarks
- · Unethical AI agent developers
Widespread adoption of human-centric evaluation frameworks will lead to more effective and user-friendly AI agents.
Improved human-agent collaboration will accelerate the integration of AI into complex workflows and decision-making processes.
The development of agents that can accurately predict and optimize for human satisfaction could redefine efficiency and productivity across industries.
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Read at arXiv cs.AI