The paper introduces a validation framework to evaluate the realism of LLM-based generative agents in urban simulators against real-world mobility data.

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 introduces CityTrajBench, a unified benchmark framework that standardizes the evaluation of city-scale vehicle trajectory generation, demonstrating that no single generation model dominates…

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 proposes a modular agent framework and novel learning methods to design and optimize practical, cost-effective, and controllable LLM-based agentic systems.

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 CRAB-Bench and RUSE, a rigorous evaluation framework that tests LLM agents on complex, interdependent tasks with realistic human user interactions, revealing significant performan…

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…

This book provides a compact, derivation-oriented mathematical primer that connects major families of generative AI models, showing their underlying structural relationships.

The paper introduces TaskWeave, a hierarchical agentic framework that successfully simulates long-horizon organizational dynamics by treating coordination as a memory-centered problem, demonstrating t…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

This paper introduces MCTS-Guided Group Relative Policy Optimization (M-GRPO) to enhance LLM spatial reasoning by improving the decomposition of complex tasks into optimal sub-tasks.

The paper proposes FluxMem, a novel connectivity-evolving memory framework that models memory as a dynamic graph to improve LLM agent performance in complex, changing environments.

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 AgentSchool, an advanced LLM-powered multi-agent simulator that models learning as state transitions to provide a robust, ethically viable testbed for educational research and ped…

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and…

This paper analyzes tool-calling in LLM agents, demonstrating that evaluation results are highly sensitive to implementation details and proposing new techniques to significantly improve the efficienc…

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 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…