Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?
arXiv:2606.31371v1 Announce Type: cross Abstract: When large language model (LLM) agents adapt their behavior through evaluator feedback, systematic evaluator biases propagate into the agent's learned strategy distribution - a phenomenon termed evaluator preference coupling. Prior work has documented this coupling and established a diagnostic framework (EPC) to measure it, but has not investigated whether calibration techniques can mitigate the effect. We present the first study of evaluator calibration as mitigation: applying probability calibration to the evaluator's pairwise judgments to re
The proliferation of LLM agents and their reliance on continuous feedback loops necessitates research into mitigating inherent biases to ensure robust and reliable autonomous systems.
Ensuring the integrity and independence of AI agent behavior is crucial for their deployment in critical applications, preventing the systemic propagation of unintended biases into autonomous processes.
This research introduces a potential method to enhance the reliability and reduce bias propagation in LLM agent feedback systems, offering a more stable foundation for agentic architectures.
- · LLM developers
- · AI safety researchers
- · Industries deploying AI agents
- · Uncalibrated LLM agent systems
- · Users relying on biased AI agents
Improved stability and predictability of AI agent behavior.
Accelerated adoption of AI agents in more sensitive and high-stakes domains due to increased trust.
Enhanced competition in AI agent development as reliability becomes a key differentiator, and new regulatory frameworks emerge around agent accountability.
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Read at arXiv cs.CL