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20212026
most citedOpenAGI: When LLM Meets Domain Experts

76 citations · 293 across the 38 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Understanding and Reducing Misalignment in Persuasion Optimization

Boyi Wei, Ruta Desai, Peter Henderson +1

Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training models to be persuasive. But wha…

cs.AI2024★ 15 cited

Game-theoretic LLM: Agent Workflow for Negotiation Games

Wenyue Hua, Ollie Liu, Lingyao Li +9

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several…

cs.AI2023★ 1 cited

NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

Lizhou Fan, Wenyue Hua, Lingyao Li +2

Complex reasoning ability is one of the most important features of current LLMs, which has also been leveraged to play an integral role in complex decision-making tasks. Therefore,…

cs.AI2023★ 13 cited

War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Wenyue Hua, Lizhou Fan, Lingyao Li +5

Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research,…

cs.AI2023★ 76 cited

OpenAGI: When LLM Meets Domain Experts

Yingqiang Ge, Wenyue Hua, Kai Mei +5

Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive A…