Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on structured attributes and predefined traveler personas, but real travel decisions are often shaped by reviews that reveal experiential factors such as comfort, safety, service quality, ambiance, crowding, and hidden risks absent from structured databases. Incorporating such review information is therefore critical to realistic, user-centric itinerary generation. We propose TRIPPULSE (Captures experiential signals from reviews beyond rigid hard constraints), a multi-agent framework for review-grounded travel planning. Instead of relying on a monolithic planner (and face context and reasoning bottlenecks), TRIPPULSE decomposes itinerary generation into specialized agents (each operating over localized contexts) for accommodations, transportation, meals, attractions, and events, coordinated through a global orchestrator with scheduling mechanisms that enforce temporal and budget feasibility. We augment TripCraft with 100K+ real-world reviews and introduce Review-Grounded Persona Alignment (RGPA), an LLM-as-a-Judge metric for evaluating alignment with human-centric travel experiences. Experiments across multiple trip durations and diverse proprietary and open-source models show that TRIPPULSE maintains strong constraint satisfaction while generating more personalized and experientially grounded itineraries.

Large Language Models (LLMs) have recently demonstrated strong capabilities in automated travel itinerary generation. However, real-world travel planning is inherently uncertain: transportation delays, crowd fluctuations, and unexpected stochastic delays frequently invalidate otherwise feasible schedules. Existing benchmarks like TravelPlanner and TripCraft assume deterministic environments, evaluating only static constraint satisfaction and ignoring whether generated plans remain robust when such uncertainties arise.

To address this limitation, we introduce UTP-Bench, a large-scale benchmark for uncertainty-aware travel planning. The dataset integrates real-world travel data spanning 504 cities of India, including attractions, restaurants, accommodations, and multi-modal transportation networks. To model realistic disruptions, UTP-Bench incorporates empirical delay distributions and crowd-density patterns collected from major cities, enabling evaluation of travel plans under stochastic conditions.

We further propose three evaluation metrics, namely Buffer Adequacy Score (BAS), Crowd-Aware Timing Score (CATS), and Transport Delay Absorption Score (TDAS), which quantify the ability of generated itineraries to maintain robustness against transit delays and crowd variability. Experiments with state-of-the-art LLMs like GPT-5, Qwen3, Mistral and Phi-4 reveal substantial gaps between model-generated and human-authored plans, particularly in temporal buffering, delay-aware transportation scheduling, and crowd-sensitive planning.

Recent work, such as TripCraft and TravelPlanner, has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation. However, real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attractions. To address this gap, we introduce TripTide, the first benchmark designed to evaluate the ability of LLMs to revise travel itineraries under realistic disruptions.TripTide models both disruption severity and traveler tolerance, enabling systematic evaluation of how LLMs respond to unexpected travel events. The benchmark simulates scenarios where existing itineraries must be revised while preserving the traveler’s original intent and respecting practical constraints. We conduct a three-fold evaluation of itinerary revision quality: (i) Automatic metrics measuring Preservation of Intent, Responsiveness, and Adaptability (semantic, spatial, and sequential), (ii) LLM-as-a-Judge evaluation assessing the quality and plausibility of revised itineraries and (iii) Human evaluation examining overall revision quality and user satisfaction.Our findings show that LLMs generally preserve semantic intent and sequential structure, while spatial deviations are more pronounced in shorter itineraries and diminish for longer ones. However, the ability to handle disruptions degrades as itinerary length increases, highlighting limitations in long-horizon itinerary revision. The TripTide benchmark provides a foundation for systematically evaluating robustness and adaptability in LLM-based travel planning systems.