CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks

CalVerT is a new framework that enhances LLM agents by providing calibrated confidence scores and grounding verification, helping agents distinguish between reliable and uncertain knowledge during question-answering tasks. The approach reduces both inaccurate confident answers and wasteful over-retrieval, improving performance across multiple QA benchmarks without requiring additional training.

CalVerT addresses a fundamental limitation in LLM-based agents: the inability to accurately assess their own knowledge state. When answering knowledge-intensive questions, agents face a critical decision-making problem—whether to retrieve additional information or commit to their current answer. Without proper calibration, agents either confidently provide unsupported answers that harm accuracy or excessively retrieve information, squandering computational resources.

The framework operates by augmenting an agent's decision-making context with two key signals: a calibrated self-confidence score reflecting the agent's certainty about its parametric knowledge, and a grounding verifier score evaluating whether existing context sufficiently supports the current answer. This dual-signal approach directly targets both failure modes simultaneously, enabling agents to make more informed retrieval decisions.

The research demonstrates significant practical value across multiple dimensions. In training-free settings, CalVerT immediately improves performance on established QA benchmarks by correcting retrieval decisions. More impressively, the framework also benefits agents undergoing reinforcement learning training, suggesting that better state representation fundamentally improves learning efficiency. This training-agnostic design means the approach can integrate into existing systems without requiring expensive retraining pipelines.

For the broader AI development community, CalVerT exemplifies how agent performance can be enhanced through better state representation rather than model scaling alone. The calibration component addresses a recognized weakness in LLM confidence estimation, making agents more reliable for production deployments. As knowledge-intensive QA systems become increasingly important for enterprise applications, the ability to reduce both hallucinations and compute waste represents meaningful progress toward more efficient and trustworthy AI systems.

• →CalVerT augments LLM agents with calibrated confidence and grounding verification scores to improve knowledge-intensive question answering
• →Framework reduces two failure modes: confident unsupported answers and wasteful over-retrieval, improving F1 scores across four QA benchmarks
• →CalVerT works without additional training and can enhance existing QA systems immediately upon implementation
• →Training-based agents using CalVerT show improved performance after reinforcement learning compared to agents without telemetry
• →Approach demonstrates that better state representation improves both agent decision-making and learning efficiency