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Frontier LLMs fail to recall post-2024 facts in new benchmark

By PulseAugur Editorial · [1 sources] ·

A new benchmark, "Post-Cutoff Knowledge 150," has been developed to test the parametric knowledge of frontier LLMs regarding events occurring after their training data cutoff. The benchmark, consisting of 150 factual questions derived from a web index restricted to 2025-2026 events, found that even the top-performing models struggle significantly, with Gemini 3.7 Flash achieving the highest score at 24%. The study indicated that reasoning capabilities do not compensate for a lack of post-cutoff knowledge, and model size or price did not consistently correlate with performance, suggesting training data freshness is a critical factor. AI

IMPACT Highlights the significant gap in LLMs' parametric knowledge of recent events, impacting applications relying on up-to-date information without RAG.

RANK_REASON New benchmark published on dev.to evaluating LLM knowledge of post-training data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Frontier LLMs fail to recall post-2024 facts in new benchmark

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New benchmark published on dev.to evaluating LLM knowledge of post-training data. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Jeffrey Turov ·

    I benchmarked what frontier models actually know about 2026 — most of it, they don't

    <p><strong>Benchmark:</strong> <a href="https://www.kaggle.com/benchmarks/tasks/jeffreyturov/post-cutoff-150" rel="noopener noreferrer">https://www.kaggle.com/benchmarks/tasks/jeffreyturov/post-cutoff-150</a><br /> <strong>Dataset:</strong> <a href="https://www.kaggle.com/dataset…

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