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This work introduces What If TSF (WIT), a benchmark for evaluating whether models effectively respond to future scenarios, and shows that factual accuracy gains do not consistently translate into appropriate responses to alternative scenarios, demonstrating the need to evaluate multimodal forecasting beyond predictive...
A comprehensive evaluation across a diverse set of open-source LLMs of varying sizes that support Korean finds that even the strongest model generates correct answer only 31.6% of the time, underscoring the challenging nature of KoSimpleQA.
MoNe is a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining, achieving strong performance on needle-in-a-haystack and word extraction benchmarks from RULER, where ICL degrades sharply.
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