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Affordable Generative Agents
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The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged agent interactions poses challenge over the deployment of believable LLM-based agents. Therefore, in this paper, we develop Affordable Generative Agents (AGA), a framework for enabling the generation of believable and low-cost interactions on both agent-environment and inter-agents levels. Specifically, for agent-environment interactions, we substitute repetitive LLM inferences with learned policies; while for inter-agent interactions, we model the social relationships between agents and compress auxiliary dialogue information. Extensive experiments on multiple environments show the effectiveness and efficiency of our proposed framework. Also, we delve into the mechanisms of emergent believable behaviors lying in LLM agents, demonstrating that agents can only generate finite behaviors in fixed environments, based upon which, we understand ways to facilitate emergent interaction behaviors. Our code is publicly available at: https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents.
Forward citations
Cited by 6 Pith papers
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Step-Level Preference Learning for Generative Agents in Social Simulations
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A large-scale LLM-driven urban simulator with recursive planning, memory, and belief modules, claimed to reproduce real-world time use, travel, and crowd patterns better than prior agent frameworks.
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Hardware-Efficient Attention for Fast Decoding
Grouped-Tied Attention and Grouped Latent Attention reduce KV-cache memory and speed up LLM decoding by up to 2x while matching the quality of GQA and MLA at up to 1.47B parameters.
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Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation
Multi-GraspLLM uses a single multimodal LLM, trained on a new 140k-grasp, 1.1M-dialogue dataset, to generate semantic grasp poses for five different robotic hands.
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Playable Game Generation
An autoregressive latent diffusion system, PlayGen, generates real-time playable Super Mario Bros and Doom sessions on an RTX 2060, with accuracy of game mechanics measured by action-recognition metrics.
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From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
A structured survey that categorizes LLM-based social simulation into individual, scenario, and society simulation, with associated methods, benchmarks, and observed trends.
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