Llm as a mastermind: A survey of strategic reasoning with large language models

Y Zhang, S Mao, T Ge, X Wang, A de Wynter… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper presents a comprehensive survey of the current status and opportunities for
Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning …

A survey on large language model-based game agents

S Hu, T Huang, F Ilhan, S Tekin, G Liu… - arxiv preprint arxiv …, 2024 - arxiv.org
The development of game agents holds a critical role in advancing towards Artificial General
Intelligence (AGI). The progress of LLMs and their multimodal counterparts (MLLMs) offers …

Towards general computer control: A multimodal agent for red dead redemption ii as a case study

W Tan, Z Ding, W Zhang, B Li, B Zhou… - ICLR 2024 Workshop …, 2024 - openreview.net
Despite the success in specific tasks and scenarios, existing foundation agents, empowered
by large models (LMs) and advanced tools, still cannot generalize to different scenarios …

Richelieu: Self-evolving llm-based agents for ai diplomacy

Z Guan, X Kong, F Zhong… - Advances in Neural …, 2025 - proceedings.neurips.cc
Diplomacy is one of the most sophisticated activities in human society, involving complex
interactions among multiple parties that require skills in social reasoning, negotiation, and …

Large language model for participatory urban planning

Z Zhou, Y Lin, D **, Y Li - arxiv preprint arxiv:2402.17161, 2024 - arxiv.org
Participatory urban planning is the mainstream of modern urban planning that involves the
active engagement of residents. However, the traditional participatory paradigm requires …

LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents

C **ao, BZ Yang - arxiv preprint arxiv:2410.02829, 2024 - arxiv.org
Recent advances in Large Language Models (LLMs) have demonstrated their potential as
autonomous agents across various tasks. One emerging application is the use of LLMs in …

EscapeBench: Pushing Language Models to Think Outside the Box

C Qian, P Han, Q Luo, B He, X Chen, Y Zhang… - arxiv preprint arxiv …, 2024 - arxiv.org
Language model agents excel in long-session planning and reasoning, but existing
benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting …

SELU: Self-Learning Embodied MLLMs in Unknown Environments

B Li, H Jiang, Z Ding, X Xu, H Li, D Zhao… - arxiv preprint arxiv …, 2024 - arxiv.org
Recently, multimodal large language models (MLLMs) have demonstrated strong visual
understanding and decision-making capabilities, enabling the exploration of autonomously …

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

X Feng, L Dou, E Li, Q Wang, H Wang, Y Guo… - arxiv preprint arxiv …, 2024 - arxiv.org
Game-theoretic scenarios have become pivotal in evaluating the social intelligence of Large
Language Model (LLM)-based social agents. While numerous studies have explored these …

A contextual combinatorial bandit approach to negotiation

Y Li, Z Mu, S Qi - arxiv preprint arxiv:2407.00567, 2024 - arxiv.org
Learning effective negotiation strategies poses two key challenges: the exploration-
exploitation dilemma and dealing with large action spaces. However, there is an absence of …