-
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Paper • 2402.15506 • Published • 18 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 30 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 33 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
Collections
Discover the best community collections!
Collections including paper arxiv:2508.14704
-
From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
Paper • 2504.19678 • Published • 3 -
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
Paper • 2503.23278 • Published • 1 -
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Paper • 2508.20453 • Published • 63 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42
-
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Paper • 2508.20453 • Published • 63 -
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use
Paper • 2509.24002 • Published • 171 -
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools
Paper • 2510.19286 • Published • 8 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 155 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 170 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53
-
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
Paper • 2508.10751 • Published • 28 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 262 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 155
-
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Paper • 2402.15506 • Published • 18 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 30 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 33 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
-
End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
-
From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
Paper • 2504.19678 • Published • 3 -
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
Paper • 2503.23278 • Published • 1 -
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Paper • 2508.20453 • Published • 63 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42
-
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Paper • 2508.20453 • Published • 63 -
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use
Paper • 2509.24002 • Published • 171 -
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools
Paper • 2510.19286 • Published • 8 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 155 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 170 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
Paper • 2508.10751 • Published • 28 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 262 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 42 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 155