The paper proposes HTP, a novel framework that leverages Large Language Models (LLMs) to first generate abstract travel patterns and then synthesize realistic GPS points, significantly improving traje…

The paper introduces CityGen, a diffusion-based framework that enables zero-label city adaptation for autonomous driving by synthesizing city-style data conditioned on HD maps and visual prompts, sign…

MobEvolve introduces an agentic self-evolving heuristic system that significantly improves human mobility generation by iteratively refining its internal logic using an LLM agent, outperforming deep g…

The paper proposes a novel four-stage simulation framework that uses GPS-derived seasonal spatial priors and LLMs to generate demographically accurate, synthetic tourist mobility schedules for urban p…

The paper introduces TravelEval, a comprehensive, six-dimensional benchmarking framework that evaluates LLM-powered travel plans using realistic spatio-temporal simulation, revealing that current LLMs…

The paper introduces a genetic algorithm framework to calibrate complex urban traffic simulations using only sparse real-world traffic observations, eliminating the need for detailed employment data.

The paper introduces a higher-order network framework to compare observed and simulated human mobility data, demonstrating that while synthetic data is promising, current simulation models have specif…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

AlphaTransit introduces a novel search-based planning framework that combines Monte Carlo Tree Search (MCTS) with a neural policy-value network to efficiently design high-quality, city-scale bus trans…

The paper introduces VeriTrip, a new verifiable benchmark that evaluates travel planning agents' ability to perform evidence-grounded reasoning over complex, unstructured, and multimodal web data, rev…

The paper proposes BitTP, a lightweight bitlinear architecture that quantizes LLM-based trajectory predictors to 1.58-bit weights while keeping activations full-precision, enabling high-performance de…

RiskFlow is a novel framework that generates realistic and safety-critical multi-agent traffic scenarios by reformulating trajectory generation as a single-pass transport problem in the action space.

The paper introduces WorldCoder-Bench, a comprehensive benchmark and evaluation protocol for testing LLMs' ability to autonomously generate complex, physically grounded, and interactive 3D web worlds.

The paper proposes a Risk Horizon Profiling (RHP) module that uses a continuous potential field model to profile future risk distributions, significantly improving trajectory prediction accuracy in bo…

This paper demonstrates that reasoning-enabled Vision-Language-Action (VLA) models for autonomous driving are highly vulnerable to realistic input perturbations, significantly compromising both reason…

The paper proposes CPGAN, a novel Generative Adversarial Network (GAN) that incorporates a collision-penalizing loss function to significantly improve the simulation of collision avoidance in dense, b…

Crazyflow is a novel, highly accelerated, and differentiable drone simulator that provides a unified platform for generating large-scale synthetic data for aerial robotics, enabling advanced training…

TraceGraph introduces a graph-based framework to map agent decision-making across pooled trajectories, revealing hidden differences in agent behavior and improving performance by targeting known failu…

The paper introduces RedundancyBench, a new benchmark for detecting unnecessary steps in LLM agent trajectories, finding that this task is highly complex and difficult to solve.