arXiv:2512.03109v2 Announce Type: replace Abstract: Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success of their trajectories, researchers have developed verifiers, such as LLM judges and process-reward models, to score the quality of each action in an agent's trajectory. Although these heuristic scores can be informative, there are no guarantees of correctness when used to decide whether an agent will yield a successful output. Here, we introduce e-valuator, a method to convert any black-box verifier sc
The proliferation of agentic AI systems necessitates robust evaluation methods to ensure their reliability and safety, making research into verifiers critical at this moment.
Reliably verifying the success of agentic AI system outputs is fundamental for their deployment in high-stakes environments and for accelerating their utility across various applications.
The ability to convert black-box verifiers into more reliable decision-making tools through sequential hypothesis testing could significantly enhance the trustworthiness and practical application of AI agents.
- · AI Agent Developers
- · Enterprise AI Adopters
- · AI Safety Researchers
- · Verification Tool Providers
- · Unreliable AI Verifiers
- · Systems reliant on heuristic scores
Improved reliability and faster deployment cycles for AI agent applications.
Increased trust in autonomous AI systems leading to broader adoption in sensitive industries.
Accelerated automation of complex workflows currently resistant to AI due to verification challenges.
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Read at arXiv cs.LG