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It is shown that the annual pretraining run is not necessary, and it is found that a small adapter is enough to add a new period to the knowledge an old checkpoint already encodes, and that this outperforms continued pretraining.
This work proposes EXAONE Demand, a time series foundation model built on a demand-specific corpus and a demand-aware adapter, which outperform 36 TSFMs, and real-world demand adds a gain over synthetic data alone.
This paper proposes Horizon-Resolved eXplanation, which adds a horizon axis to the explanation, so that every forecast step receives its own importance map, and shows that this step-wise dependence is low-dimensional, as the explanations of all steps are built from a few shared maps whose number does not grow with the...
ExAONE Finance is pretrained on a synthetic financial corpus whose generative process is designed to reproduce the properties of financial series such as heavy tails, volatility clustering, jumps, regime shifts, and cross-asset dependence, combined with a domain-agnostic synthetic source.
ReasonCast, the recipe for finetuning any LLM to perform both tasks jointly, yields a model that generates a reasoning chain and a forecast together in a single autoregressive pass, and Extensive experiments show that ReasonCast outperforms both LLMs and TS models on prediction accuracy while producing verifiable, caus...
FinVerse is introduced, a finance-domain time-series forecasting benchmark that takes a first step toward more realistic evaluation and highlights the need for domain-aware benchmarks that evaluate models under objectives closer to real-world decision making.
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