arXiv:2606.22737v2 Announce Type: replace-cross Abstract: Before letting an agent operate over real context, can you prove it used the right evidence? GroundEval turns that question into a deterministic test of what the agent searched, fetched, cited, and was permitted to access. In one case study, two frontier LLM judges scored a plausible agent response 0.85 and higher. But the trace told a different story: the agent had never retrieved the artifact its answer depended on, yielding a GroundEval score of 0.000. We introduce GroundEval, a judge-free framework for evaluating agents against grou
The proliferation of AI agents operating in complex environments necessitates more robust and deterministic evaluation methods beyond subjective LLM-as-judge approaches.
Deterministic evaluation of AI agent truthfulness and grounding is critical for their safe and reliable deployment in real-world, high-stakes contexts, impacting trust and adoption.
The shift from subjective LLM-based agent evaluation to objective, trace-based verification changes how agent performance and trustworthiness are assessed, potentially accelerating their reliable integration.
- · AI Agent developers
- · AI Safety researchers
- · Enterprises deploying AI agents
- · Subjective LLM-as-judge evaluation methods
- · AI agents lacking verifiable grounding
GroundEval will become a standard for evaluating the factual grounding of AI agent decisions and actions, reducing reliance on human intuition.
Increased transparency and verifiability will accelerate the adoption of AI agents in sensitive industries such as finance, healthcare, and defense.
The demand for 'groundable' AI architectures will rise, influencing foundational model design and training methodologies towards transparent evidence chains.
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