arXiv:2607. 15552v1 Announce Type: cross Abstract: Generating personalized trip itineraries is a complex planning task and involves a tension between hard combinatorial feasibility and soft latent desirability.
arXiv:2607. 10651v1 Announce Type: new Abstract: In large urban areas, planning multi-day travel itineraries is challenging due to the abundance of Points of Interest (POIs), diverse user preferences, and constraints such as opening hours.
arXiv:2606. 01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.
TripScore is a benchmark and evaluation framework for large language models (LLMs) in travel planning, built from real user logs and calibrated with 1,468 pairwise judgments from 203 travel experts. It uses a hierarchical feasibility gate for format and commonsense checks, and a unified point-wise reward that combines soft quality and preference fulfillment. Experiments show that reinforcement learning fine‑tuning, such as GRPO, consistently outperforms other methods when evaluated with TripScore.
Behavior2Trip introduces a new task—Behavior‑Aware Travel Planning—where user preferences are inferred from past behavior trajectories rather than explicit instructions. The benchmark contains 11,400 instances from a major Chinese travel platform, each with nearly 40 recorded behaviors across 14 attributes and 5 preference dimensions. A reinforcement‑learning agent, B2T‑Agent, leverages these trajectories, external retrieval tools, and internal memory, outperforming GPT‑4.1 and other baselines on the dataset.
arXiv:2607. 15562v1 Announce Type: cross Abstract: Packing for air travel is recurring and error-prone: the checklist must be personal and context-aware, yet feasible under safety rules, item dependencies, and luggage limits.
Behavior2Trip introduces a new task—Behavior‑Aware Travel Planning—where user preferences are inferred directly from past behavior trajectories rather than explicit instructions. The benchmark contains 11,400 Chinese travel‑planning instances, each with an average of 39.8 past behaviors across 14 attributes and 5 preference dimensions. A reinforcement‑learning agent, B2T‑Agent, leveraging behavior trajectories, external retrieval tools, and internal memory, outperforms strong baselines such as GPT‑4.1 on this challenging dataset.
arXiv:2509. 21842v2 Announce Type: replace Abstract: Travel planning (TP) agent has recently worked as an emerging building block to interact with external tools/resources for travel itinerary generation, ensuring an enjoyable user experience.
arXiv:2608.30924v1 Announce Type: new Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on st...
The paper introduces PrefDT, a preference-conditioned Decision Transformer designed for multi‑objective scheduling of UAV mobile edge computing fleets. PrefDT accepts a desired energy‑delay trade‑off as input, enabling a single offline‑trained model to generate any point on the Pareto front during runtime. The authors employ attention pooling with a per‑user bypass to maintain scheduler operation when user reports are lost, and a distillation pipeline to create a preference‑labeled flight corpus, achieving superior trade‑off curves and tight energy budget adherence in simulations.
CALM is a reproducible hybrid framework that combines an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. It executes a closed traveler‑day loop and evaluates each generative module against an empirical, reproducible baseline, using the 2024 New York City Citywide Mobility Survey data. The framework demonstrates significant improvements in mode‑choice accuracy, quantifies trade‑offs through live‑LLM ablation, and supports controlled stress testing and deterministic replay of downstream simulations.
UTP-Bench is a new benchmark for uncertainty-aware travel planning that evaluates large language models on their ability to generate robust itineraries under real-world stochastic conditions. The dataset covers 504 Indian cities, incorporating attractions, restaurants, accommodations, and multi-modal transportation networks, and includes empirical delay distributions and crowd-density patterns to simulate realistic disruptions. Three new metrics—Buffer Adequacy Score, Crowd-Aware Timing Score, and Transport Delay Absorption Score—measure how well generated plans maintain robustness against transit delays and crowd variability, revealing significant gaps between state-of-the-art LLMs and human-authored itineraries.
arXiv:2605.25200v3 Announce Type: replace Abstract: Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single us...