LEAF: A Living Benchmark for Event-Augmented Forecasting
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, relev...
| Authors | Mingtian Tan, Mihir Parmar, Palash Goyal et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 May 2026 |
| Last updated at source | 1 October 2026 |
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