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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 • 29 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 34 -
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
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The Smol Training Playbook
📚3.2kThe secrets to building world-class LLMs
-
LLM-in-Sandbox Elicits General Agentic Intelligence
Paper • 2601.16206 • Published • 87 -
EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
Paper • 2601.15876 • Published • 92 -
BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
Paper • 2510.08697 • Published • 40
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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 • 43
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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 • 180 -
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools
Paper • 2510.19286 • Published • 9 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43
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Attention Is All You Need
Paper • 1706.03762 • Published • 124 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Paper • 2210.04186 • Published
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AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 162 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 183 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53
-
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 • 29 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 34 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
-
The Smol Training Playbook
📚3.2kThe secrets to building world-class LLMs
-
LLM-in-Sandbox Elicits General Agentic Intelligence
Paper • 2601.16206 • Published • 87 -
EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
Paper • 2601.15876 • Published • 92 -
BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
Paper • 2510.08697 • Published • 40
-
Attention Is All You Need
Paper • 1706.03762 • Published • 124 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Paper • 2210.04186 • Published
-
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 • 43
-
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 • 180 -
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools
Paper • 2510.19286 • Published • 9 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 162 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 183 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53