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Collections including paper arxiv:2508.14704

a collection of algorithmic agents for user interfaces/interactions, program synthesis, and robotics

End-to-End Goal-Driven Web Navigation

Paper • 1602.02261 • Published Feb 6, 2016
Learning Language Games through Interaction

Paper • 1606.02447 • Published Jun 8, 2016
Naturalizing a Programming Language via Interactive Learning

Paper • 1704.06956 • Published Apr 23, 2017
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Paper • 1802.08802 • Published Feb 24, 2018 • 1

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

Peer-Reviewed Articles

AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 155
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Paper • 2403.13372 • Published Mar 20, 2024 • 171
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Paper • 2508.09834 • Published Aug 13 • 53

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

Paper • 2508.14111 • Published Aug 18 • 33
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Paper • 2508.10751 • Published Aug 14 • 28
Reinforcement Pre-Training

Paper • 2506.08007 • Published Jun 9 • 262
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 155

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

Paper • 2504.19678 • Published Apr 28 • 3
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

Paper • 2503.23278 • Published Mar 30 • 1
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use

Paper • 2509.24002 • Published Sep 28 • 171
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools

Paper • 2510.19286 • Published Oct 22 • 8
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations

Paper • 2508.09789 • Published Aug 13 • 5
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents

Paper • 2508.13186 • Published Aug 14 • 18
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents

Paper • 2508.04038 • Published Aug 6 • 1
Prompt Orchestration Markup Language

Paper • 2508.13948 • Published Aug 19 • 48

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Paper • 2508.07407 • Published Aug 10 • 97
Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 120
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

Paper • 2508.05748 • Published Aug 7 • 139
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Paper • 2508.13167 • Published Aug 6 • 127

a collection of algorithmic agents for user interfaces/interactions, program synthesis, and robotics

End-to-End Goal-Driven Web Navigation

Paper • 1602.02261 • Published Feb 6, 2016
Learning Language Games through Interaction

Paper • 1606.02447 • Published Jun 8, 2016
Naturalizing a Programming Language via Interactive Learning

Paper • 1704.06956 • Published Apr 23, 2017
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Paper • 1802.08802 • Published Feb 24, 2018 • 1

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

Paper • 2504.19678 • Published Apr 28 • 3
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

Paper • 2503.23278 • Published Mar 30 • 1
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use

Paper • 2509.24002 • Published Sep 28 • 171
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools

Paper • 2510.19286 • Published Oct 22 • 8
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

Peer-Reviewed Articles

AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 155
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Paper • 2403.13372 • Published Mar 20, 2024 • 171
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Paper • 2508.09834 • Published Aug 13 • 53

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

Paper • 2508.14111 • Published Aug 18 • 33
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations

Paper • 2508.09789 • Published Aug 13 • 5
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents

Paper • 2508.13186 • Published Aug 14 • 18
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents

Paper • 2508.04038 • Published Aug 6 • 1
Prompt Orchestration Markup Language

Paper • 2508.13948 • Published Aug 19 • 48

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Paper • 2508.10751 • Published Aug 14 • 28
Reinforcement Pre-Training

Paper • 2506.08007 • Published Jun 9 • 262
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Paper • 2508.14704 • Published Aug 20 • 42
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 155

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Paper • 2508.07407 • Published Aug 10 • 97
Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 120
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

Paper • 2508.05748 • Published Aug 7 • 139
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Paper • 2508.13167 • Published Aug 6 • 127

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