arXiv:2607.04329v1 Announce Type: new Abstract: Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, permissions, communication paths, and action authority. Building on this framework, HAS-Bench evaluates Human-Agent Systems under configurable human participation across agency levels, interaction channels, and persona policies. The benchmark measures both task outcomes and p
The increasing sophistication of large language models is demanding more robust frameworks for integrating human collaboration and oversight, especially as these models move into critical operational settings.
This development is crucial for understanding and designing responsible, effective AI deployments where humans and AI agents must work together, directly influencing the next wave of automation and partnership paradigms.
The focus shifts from LLM-as-standalone to LLM-as-participant in complex human-agent systems, necessitating new evaluation benchmarks and design principles for their collaboration.
- · AI platform developers
- · Enterprise software companies
- · Researchers in human-computer interaction
- · Companies adopting agentic workflows
- · Companies relying on simplistic AI integration
- · Academic disciplines ignoring human-AI collaboration
- · Closed-source foundational models lacking integration capabilities
It enables more reliable and trustworthy integration of AI agents into mission-critical processes by establishing a clear framework for human collaboration and oversight.
This will accelerate the deployment of advanced AI agents in regulated industries, collapsing white-collar workflows and necessitating new organizational structures.
The structured integration of human and agent roles could lead to entirely new forms of collective intelligence that blend human intuition with AI processing power.
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