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StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction — arXiv:2609.18949

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18949
License (as archived): CC0-1.0
Archived: 2026-10-10T03:22:26.984Z
SHA-256 of the archived text: 1fd657c9c22fb52b05ef6356d1fda7258121d1e9ff1721aa6b87689c2e1b5f5b
Stable URL of this page: https://metraipolis.com/documento/dc_a031bdcc6eef

How to cite

StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction — arXiv:2609.18949 — arXiv (en), https://arxiv.org/abs/2609.18949 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a031bdcc6eef (Metraipolis archive dc_a031bdcc6eef, sha256 1fd657c9c22fb52b05ef6356d1fda7258121d1e9ff1721aa6b87689c2e1b5f5b)

BibTeX

@misc{dca031bdcc6eef,
  title = {StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction — arXiv:2609.18949},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18949},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a031bdcc6eef; sha256 1fd657c9c22fb52b05ef6356d1fda7258121d1e9ff1721aa6b87689c2e1b5f5b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18949. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-18 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); General Economics (econ.GN); Computational Finance (q-fin.CP).
Pagina del lavoro: https://arxiv.org/abs/2609.18949 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:04:13.767Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).