A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper discusses the potential high-level and long-term benefits of using multimodal large language models (M-LLMs) in health care, including their ability to integrate diverse data types for improved clinical decision-making.
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This paper reviews the applications, challenges, and future outlook of multimodal large language models in healthcare, emphasizing their role in integrating diverse medical data for improved clinical decision-making.
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This paper proposes a flexible definition of hallucination in foundation models for decision-making and reviews state-of-the-art detection and mitigation methods, including those requiring internal model access.
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OSWorld-MCP is the first comprehensive and fair benchmark for assessing computer-use agents' tool invocation, GUI operation, and decision-making abilities in real-world scenarios.
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This paper introduces Ragen, a multi-turn reinforcement learning framework for understanding self-evolution in LLM agents, evaluated on symbolic and realistic decision-making environments.
Riikka Koulu
This paper critically examines human oversight in EU AI policy for legal decision-making, arguing it risks becoming an empty procedural safeguard without addressing inherent human limitations.
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This paper surveys the use of foundation models in decision-making systems, categorizing their roles and discussing methods, challenges, and future opportunities.
Shaoguang Mao, Yuzhe Cai, Yan Xia, et al.
This paper introduces ALYMPICS, a framework for evaluating LLM agents' strategic decision-making in game-theoretic scenarios, revealing their capabilities and limitations in human-like interactions.
Adam Kostka, J. Chudziak
This paper introduces a novel multi-agent architecture integrating Theory of Mind, BDI-style internal beliefs, and symbolic solvers to improve collaborative decision-making in LLM-based systems.