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Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal …
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Can Large Language Models Independently Complete Tasks? A Dynamic Evaluation Framework for Multi-turn Task Planning and Completion
Large language models (LLMs) are increasingly relied upon for multi-turn dialogue to conduct complex tasks. However, existing …
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ReDMan: Reliable Dexterous Manipulation with Safe Reinforcement Learning
Dexterous hand manipulation is a crucial ability for robots in various applications. However, ensuring safety and reliability during …
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Med-Aligner Empowers LLM Medical Applications for complex medical scenarios
Large language models (LLMs) show great promise in medical applications, but challenges like limited high-quality data, closed-source …
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Hierarchical Multi-Agent Framework for Dynamic Macroeconomic Modeling Using Large Language Models
Large Language Models (LLMs) have demonstrated potential in simulating macroeconomic systems by integrating the agent-based models. …
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Large Language Models in Medicine: Applications, Challenges, and Future Directions
In recent years, large language models (LLMs) represented by GPT-4 have developed rapidly and performed well in various natural …
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TIMAR: Transition-Informed Representation for Sample-Efficient Multi-agent Reinforcement Learning
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive …
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Discrete Information Acquisition in Financial Markets
We study investors’ information acquisition strategies under arbitrary and discrete sets of information precision and derive conditions …
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Transforming the Synthesis of Carbon Nanotubes with Machine Learning Models and Automation
Carbon-based nanomaterials (CBNs) hold immense promise in electronics, energy, and mechanics. However, their practical applications …
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Correlated Mean Field Imitation Learning
We investigate multi-agent imitation learning (IL) within the framework of mean field games (MFGs), considering the presence of …
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