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Found 20 papers, 8 papers with code
Understanding World or Predicting Future? A Comprehensive Survey of World Models
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence.
A Survey on Human-Centric LLMs
The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction.
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN
Motivated by the remarkable abilities of large language models (LLMs) in commonsense reasoning, embedding, and multi-agent collaboration, in this work, we synergize LLM agents and knowledge graph for socioeconomic prediction.
OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
Besides, the substantial speedup of OpenCity allows us to establish a urban simulation benchmark for LLM agents for the first time, comparing simulated urban activities with real-world data in 6 major cities around the globe.
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction
Second, logical relationships between papers are implicit, and directly prompting an LLM to predict citations may result in surface-level textual similarities rather than the deeper logical reasoning.
AgentSquare: Automatic LLM Agent Search in Modular Design Space
We believe that the modular design space and AgentSquare search framework offer a platform for fully exploiting the potential of prior successful designs and consolidating the collective efforts of research community.
LIMP: Large Language Model Enhanced Intent-aware Mobility Prediction
Moreover, we propose a transformer-based intention-aware mobility prediction model to effectively harness the intention inference ability of LLM.
Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions
In the absence of navigation instructions, such abilities are vital for the agent to make high-quality decisions in long-range city navigation.
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
ReStruct uses a grammar translator to encode the meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate their semantic feasibility.
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs
We find mechanistic mobility models, such as gravity model, can effectively map mobility intentions to physical mobility behaviours.
Synergy-of-Thoughts: Eliciting Efficient Reasoning in Hybrid Language Models
If these intuitive thoughts exhibit conflicts, SoT will invoke the reflective reasoning of scaled-up language models to emulate the intervention of System 2, which will override the intuitive thoughts and rectify the reasoning results.
Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
How enlightened self-interest guided global vaccine sharing benefits all: a modelling study
We explore the possibility of the enlightened self-interest incentive mechanism, i. e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model.
Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network
To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs.
Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities
With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment.
AttnMove: History Enhanced Trajectory Recovery via Attentional Network
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service.
Automorphic Equivalence-aware Graph Neural Network
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e. g., computational biologist and social network analysis.
Smartphone App Usage Prediction Using Points of Interest
In this paper we present the first population-level, city-scale analysis of application usage on smartphones.
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data
By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets.
Computers and Society Cryptography and Security
