Discrete events such as holidays, policy announcements, and public gatherings leave no trace in historical time series yet fundamentally shape future demand. Language models capture precisely this event knowledge, making them a natural source of auxiliary signal for event-driven forecasting. However, some multimodal forecasters borrow semantic alignment from vision-language pretraining, enforcing geometric proximity between text and series embeddings. For time series regression, this objective is theoretically unnecessary and actively harmful when training data is limited or text-series coupling is weak: contrastive gradients compress the high-variance series features required to represent event-induced spikes. We propose predictive grounding as the principled alternative, shaping language representations through prediction loss alone. PROSE (Predictive grOunding for event-driven time-Series forEcasting) realizes this framework through a fully fine-tuned language encoder, token-level Event Token Injection, LLM-generated Trajectory Reasoning Anchors as global prefix tokens, and a Soft Functional Alignment loss. On a 464K-sample Chinese holiday benchmark, PROSE surpasses the strongest baseline by 10.7% WMAPE; on Time-MMD, it achieves state-of-the-art results across 7 of 9 domains.

Although large language models have enhanced automated travel planning abilities, current systems remain misaligned with real-world scenarios. First, they assume users provide explicit queries, while in reality requirements are often implicit. Second, existing solutions ignore diverse environmental factors and user preferences, limiting the feasibility of plans. Third, systems can only generate plans with basic POI arrangements, failing to provide all-in-one plans with rich details. To mitigate these challenges, we construct a novel dataset RETAIL, which supports decision-making for implicit queries while covering explicit queries, both with and without revision needs. It also enables environmental awareness to ensure plan feasibility under real-world scenarios, while incorporating detailed POI information for all-in-one travel plans. Furthermore, we propose a topic-guided multi-agent framework, termed TGMA. Our experiments reveal that even the strongest existing model achieves merely a 1.0% pass rate, indicating real-world travel planning remains extremely challenging. In contrast, TGMA demonstrates substantially improved performance 2.72%, offering promising directions for real-world travel planning.