StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction

Abstract:We introduce StableEval Arena, a cost-aware benchmark framework for evaluating agentic AI systems on stablecoin peg-risk prediction. StableEval Arena evaluates LLM-backed agentic systems on diagnosing peg stress and forecasting deviations from the one-dollar peg over a hidden seven-day horizon, using leakage-safe historical replay with exchange price-volume data and market-context features. We report two complementary experiment blocks: a 120-case stress-enriched validation block and a 507-case natural-distribution full-arena evaluation block. Across six LLM-backed agent configurations and baselines, StableEval Arena measures prediction quality, calibrated-label behavior, structured-output reliability, latency, token consumption, and estimated inference cost. Rather than ranking agents by accuracy alone, the framework treats trustworthiness as a joint property of forecast quality, operational reliability, and computational cost. The results show a gap between protocol-following reliability and financial-risk reliability: agents reliably produce valid structured outputs at modest measured cost, but still miss most rare severe-stress and sustained-depeg cases. To support auditing and replication, we release the benchmark dataset on Hugging Face and the source code on GitHub.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.18949 [cs.LG]

(or arXiv:2609.18949v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.18949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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