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New benchmark GIFT-Eval-CTX boosts LLM forecasting accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GIFT-Eval-CTX, a new benchmark designed to evaluate contextual forecasting models by assessing their ability to use verified future information. This benchmark, built upon GIFT-Eval, includes 10,000 instances across 20 datasets, providing structured 'effects' that describe future conditions. When tested, frontier LLMs like GPT-5.6 "Sol" and Claude Opus 4.8 showed significant improvements in forecasting accuracy when provided with these structured effects compared to using historical data alone. An agentic system, MoiraiAgent, achieved the best overall performance, highlighting the potential of combining LLMs with structured future information for enhanced time series forecasting. AI

IMPACT This benchmark could lead to more accurate time series forecasting by LLMs, improving applications in finance, logistics, and planning.

RANK_REASON New benchmark paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark GIFT-Eval-CTX boosts LLM forecasting accuracy

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New benchmark paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xu Liu, Hanshu Yan, Taha Aksu, Quang Pham, Zhongzheng Qiao, Jun Hao Liew, Silvio Savarese, Doyen Sahoo, Junnan Li ·

    GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation

    arXiv:2610.17794v1 Announce Type: cross Abstract: Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expectations. However, reliable evaluation remains difficult: ex…

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