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Found 18 papers, 7 papers with code
UFO2: The Desktop AgentOS
Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language.
API Agents vs. GUI Agents: Divergence and Convergence
Large language models (LLMs) have evolved beyond simple text generation to power software agents that directly translate natural language commands into tangible actions.
Large Action Models: From Inception to Implementation
As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actions.
Large Language Model-Brained GUI Agents: A Survey
This has paved the way for a new generation of LLM-brained GUI agents capable of interpreting complex GUI elements and autonomously executing actions based on natural language instructions.
RuAG: Learned-rule-augmented Generation for Large Language Models
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection.
Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud Infrastructure
The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability and reliability of cloud services, resulting in negative customer experiences.
An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
Efficient resource utilization and perfect user experience usually conflict with each other in cloud computing platforms.
Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
Time series anomaly detection (TSAD) plays a crucial role in various industries by identifying atypical patterns that deviate from standard trends, thereby maintaining system integrity and enabling prompt response measures.
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning
The entire framework has a message module and an action module.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.
Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft.
TaskWeaver: A Code-First Agent Framework
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System
To establish such scoring systems, several "empirical criteria" are firstly determined, followed by dedicated top-down design for each factor of the score, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario.
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms.
Introspective Tips: Large Language Model for In-Context Decision Making
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning
To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Solving the Batch Stochastic Bin Packing Problem in Cloud: A Chance-constrained Optimization Approach
This paper investigates a critical resource allocation problem in the first party cloud: scheduling containers to machines.
