EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems
arXiv:2607.00297v1 Announce Type: cross Abstract: When LLM agents use evaluator feedback to adapt their behavior in closed loops, evaluator biases propagate through the agent's strategy distribution -- a phenomenon known as evaluator preference coupling. Prior work has documented coupling across multiple evaluator families and model versions, but the field lacks a standardized protocol that enables third-party researchers to (i) reproduce coupling measurements, (ii) compare results across evaluators and time points, and (iii) detect measurement decay as proprietary evaluators silently update.
The proliferation of LLM agent systems and reliance on evaluator feedback necessitates a standardized approach to measure and understand preference dynamics, as proprietary systems update frequently.
A standardized protocol for measuring evaluator preference dynamics is critical for reliable development and deployment of autonomous AI agents, ensuring transparency and preventing hidden biases from propagating.
The introduction of EPC provides a common framework for researchers and developers to compare, reproduce, and detect changes in how AI systems learn from human or automated evaluators.
- · AI researchers
- · LLM developers
- · AI ethics and safety organizations
- · Proprietary AI labs resistant to transparency
- · Developers relying on opaque evaluator systems
The adoption of EPC will lead to more robust and explainable LLM agent systems.
Improved reproducibility and comparability could accelerate the development of advanced AI agents, fostering greater trust.
Standardized evaluation could become a regulatory requirement for AI systems, influencing market access and compliance.
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Read at arXiv cs.CL