A Diagnostic Framework and Multi-Evaluator Audit of Evaluator-Driven Preference Dynamics in Self-Adapting LLM Agents
arXiv:2606.29719v1 Announce Type: new Abstract: Measurements of proprietary LLM evaluators can become invalid within weeks -- we document one case and provide the diagnostic framework to detect it. We introduce EPC -- comprising the Multimodal Preference Collapse Index (MPCI), evaluator-indexed coupling matrix, and Jensen-Shannon divergence (JSD) -- and apply it across eight experimental conditions (N=112 main + N=10 ablation = 122 unique repetitions, all reported). Coupling coefficients range from 0.00 to 1.18 across per-condition means (CV approx 0.9, n=8 conditions). Four conditions show st
The rapid deployment and increasing sophistication of LLMs and their evaluators necessitate robust diagnostic frameworks to ensure their efficacy and prevent model degradation, which this paper directly addresses.
This research provides a critical toolset for assessing the stability and validity of LLM evaluation systems, directly impacting the development and reliability of advanced AI agents.
The introduction of the EPC framework offers a standardized method for detecting 'preference collapse' in LLM evaluators, enabling developers to identify and mitigate issues proactively.
- · LLM developers
- · AI researchers
- · Users of AI agents
- · AI evaluation companies
- · Developers ignoring evaluator drift
- · Companies relying on unstable LLM performance
Improved reliability and stability of proprietary LLM systems and AI agents.
Faster development cycles and more effective deployment of AI agents due to clearer diagnostic capabilities.
Enhanced trust in AI systems as their underlying evaluation mechanisms become more transparent and robust.
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Read at arXiv cs.LG