arXiv Artificial Intelligence

LEAF: A Living Benchmark for Event-Augmented Forecasting

1 hour ago 05.10.2026 07:00 👁 2 views
LEAF: A Living Benchmark for Event-Augmented Forecasting

Quick summary

arXiv:2605.16358v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remains compromised by pre-training data contamination and look-ahead leakage in automated search. Existing benchmarks either rely on static contexts, restrict evaluations to narrow environments, or fail to audit auxiliary textual events for future information leakage. To establish a rigorous evaluation paradigm, we propose LEAF, the first living benchmark for event-augmented forecasting tasks, including

Key takeaways

  • arXiv:2605.16358v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remains compromised by pre-training data contamination and look-ahead leakage in automated search.
  • Existing benchmarks either rely on static contexts, restrict evaluations to narrow environments, or fail to audit auxiliary textual events for future information leakage.
  • To establish a rigorous evaluation paradigm, we propose LEAF, the first living benchmark for event-augmented forecasting tasks, including

Why it matters

“LEAF: A Living Benchmark for Event-Augmented Forecasting” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗