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Benchmarking Forecasting Agents on Yesterday's Tomorrows — arXiv:2610.40940

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40940
License (as archived): CC0-1.0
Archived: 2026-11-03T07:30:40.809Z
SHA-256 of the archived text: f6acc02e7e06f1e6564e213876c31ad1209b831279dbd81d616c7ef8cf48c268
Stable URL of this page: https://metraipolis.com/documento/dc_caaa33ea67ee

How to cite

Benchmarking Forecasting Agents on Yesterday's Tomorrows — arXiv:2610.40940 — arXiv (en), https://arxiv.org/abs/2610.40940 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_caaa33ea67ee (Metraipolis archive dc_caaa33ea67ee, sha256 f6acc02e7e06f1e6564e213876c31ad1209b831279dbd81d616c7ef8cf48c268)

BibTeX

@misc{dccaaa33ea67ee,
  title = {Benchmarking Forecasting Agents on Yesterday's Tomorrows — arXiv:2610.40940},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40940},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_caaa33ea67ee; sha256 f6acc02e7e06f1e6564e213876c31ad1209b831279dbd81d616c7ef8cf48c268},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40940. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Digital agents can now gather and organize information, reason over it, and use tools to complete complex tasks. Forecasting puts all of these abilities to the test: it requires integrating incomplete evidence, inferring causal drivers, and committing to calibrated probabilistic judgments. Existing benchmarks are poorly suited to measuring and improving this ability. Live benchmarks provide feedback only after events resolve, so every evaluation cycle waits on the calendar; fixed-corpus evaluations return results immediately but confine research to pre-collected documents. We introduce FABench, a scalable backtesting environment for Forecasting Agents. An automated pipeline discovers events, generates questions, and resolves outcomes for any past forecast date, so the benchmark can be renewed to stay ahead of model training cutoffs; its first release poses 287 questions across nine domains. Agents research each question freely in an archive environment that combines an offline archive with live web search screened to prevent temporal leakage. Evaluating agents built on eight recent models, we find that retrieval improves the Brier score of all eight in every retrieval setting and lifts the leading agents to the market consensus on the 54 questions linked to Polymarket, with the best three scoring slightly above it. Research traces reveal recurring failures in how agents weigh and apply evidence, leaving ample room for improvement. We release our code, data, and archive environment as a foundation for community research on forecasting agents.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.40940 — 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 2026-11-03T07:29:33.580Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).