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CDTS2 attains the highest Interventional Robustness Score (IRS) and Disentanglement–Completeness–Informativeness (DCI) metrics, indicating that it learns causally disentangled representations that remain robust under interventions.
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.
Across financial filings and earnings-call transcripts, it is found that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions, suggesting that financial compression should be evaluated not only by efficiency or factuality, but also by its abil...
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.
The results show that an LLM's aggregate investment stance can be calibrated toward a specified target at inference time.
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