LEAF: בנך לאימות תחזיות

תקציר מקורי באנגליתarXiv: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 trend, event, and time series forecasting. LEAF couples a recursive retrieval agent system with dual-agent cross-validation to gather comprehensive, relevant, and temporally aligned auxiliary context