EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems
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Computer Science > Machine Learning
arXiv:2607.00297 (cs)
[Submitted on 1 Jul 2026]
Title:EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems
Authors:Zewen Liu
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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. This paper provides the protocol. We specify EPC (Evaluator Preference Coupling) -- a detailed, RFC-style protocol specification for the four-phase isolation paradigm, covering executor and evaluator configuration, strategy and task design, the TTRL update rule, metric computation (gamma, JSD, ECE, Brier), and output schema. We accompany the protocol with a versioned Reference Snapshot v1.0: coupling measurements for eight evaluator conditions (N=122 unique experimental repetitions across GPT-4o, Qwen, DeepSeek, and others) derived from five independent studies, annotated with evaluator version identifiers, API endpoints, and measurement dates. The snapshot is explicitly time-bound: all values are conditional on specific model versions and are expected to decay as proprietary evaluators update. We define a versioning convention (vX.Y-Z, encoding protocol version, snapshot version, and evaluator generation) and provide a usage guide covering adoption, interpretation, and known pitfalls. The protocol, reference snapshot, and implementation code are released as open infrastructure.
| Comments: | 10 pages, 3 tables |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.2.11 |
| Cite as: | arXiv:2607.00297 [cs.LG] |
| (or arXiv:2607.00297v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00297
arXiv-issued DOI via DataCite (pending registration)
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