Artificial Intelligence Applications
328 papers on this topic
Multi-Agent Collaboration Mechanisms: A Survey of LLMs
With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act...
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
While reasoning models (e.g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, such as geometric...
Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution
Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually predefined tools...
LLM Social Simulations Are a Promising Research Method
Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However,...
The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces and analyzes...
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D...
AI-Researcher: Autonomous Scientific Innovation
The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present...
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
This review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding. While both leverage large language models (LLMs), they...
Simulating Environments with Reasoning Models for Agent Training
LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools and schemas....
AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration
Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm for...
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation
Quantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the...
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating...
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems...
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in...
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that...
CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions
While AI agents hold transformative potential in business, effective performance benchmarking is hindered by the scarcity of public, realistic business data on widely used platforms. Existing...
A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services...
A Rosetta Stone for AI Benchmarks
Most AI benchmarks saturate within years or even months after they are introduced, making it hard to study long-run trends in AI capabilities. To address this challenge, we build a statistical...