ORBITER is a new framework for next‑order decision‑making in last‑mile delivery that uses large language models (LLMs) to reason about courier spatiotemporal states and visible orders. It structures decision points with local trade‑offs, ranks candidates via fixed proposers, and employs a critic to verify decisions against gathered evidence. Experiments on data from four cities show ORBITER outperforms state‑of‑the‑art baselines by up to 9.2% on average.
Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-based methods model spatiotemporal dependencies am...
DeliveryGym is a 3D reinforcement learning environment that simulates continuous courier shifts, integrating multimodal tool interaction and persistent world dynamics to compute trajectory rewards based on simulator events. It provides feedback on resource consumption—time, energy, and money—across entire delivery trajectories, enabling agents to learn planning that balances immediate task success with long‑term constraints. The environment also adapts future training shifts to a policy’s weaknesses while keeping evaluation fixed, demonstrating that both learning from complete shifts and targeted training improve agent performance on complex delivery tasks.
MAPF-World introduces an autoregressive action world model for multi‑agent path finding that predicts short‑horizon local observations and neighboring agents’ action intentions, enabling decisions beyond immediate local views. The model incorporates a spatio‑agent positional encoding within Transformer architectures to enhance spatial awareness and agent semantics, fostering coordinated behaviors. Experiments on diverse maps, including a new automated map generator based on real‑world urban layouts, show that MAPF‑World outperforms existing learned solvers, achieving strong performance, robust zero‑shot generalization, and high success rates even at high agent densities.
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.
DeliveryGym is a 3D reinforcement learning environment that simulates continuous courier shifts, integrating multimodal tool interaction, persistent world dynamics, and trajectory‑based rewards derived from simulator events. It allows agents to learn how their decisions affect time, energy, and money across an entire shift, and it adapts future training shifts to the policy’s weaknesses while keeping evaluation fixed. Experiments on six models and 13 city maps show a significant gap between task execution and optimal sequencing, with RL improving Qwen3‑VL‑4B’s net income by 54.3% and adaptive training boosting test income by 16.5% over uniform sampling.
arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.
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. 10897v1 Announce Type: new Abstract: Instant delivery platforms have become a critical component of urban logistics, increasingly relying on crowdsourced couriers to fulfill highly dynamic orders.
arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This f...
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 presents an agent‑based model of daily multimodal mobility in Toulouse, comparing fifteen decision‑making approaches—including generative agents (GAs) with minimal and tuned prompts, simple baselines, survey‑fitted models, and a typed classifier—against real household travel survey data. Results show that GAs with tuned prompts approximate the modal split of a simulated day without disruptions but fall short on trip‑by‑trip accuracy, while the Jev‑1.13 TypeSafe classifier performs surprisingly well, indicating that generating output text is not essential. The study highlights the potential and current limitations of generative agent‑based modelling for urban mobility.
arXiv:2608.30399v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequ...