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Collections including paper arxiv:2508.16153

Less is More: Recursive Reasoning with Tiny Networks

Paper • 2510.04871 • Published Oct 6 • 494
SpikingBrain Technical Report: Spiking Brain-inspired Large Models

Paper • 2509.05276 • Published Sep 5 • 3
Self-Adapting Language Models

Paper • 2506.10943 • Published Jun 12 • 6
The Art of Scaling Reinforcement Learning Compute for LLMs

Paper • 2510.13786 • Published Oct 15 • 30

Self-Evolving-Agents

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

Paper • 2508.07407 • Published Aug 10 • 98
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Paper • 2504.20073 • Published Apr 24 • 12
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Agent Workflow Memory

Paper • 2409.07429 • Published Sep 11, 2024 • 32
Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training

Paper • 2501.11425 • Published Jan 20 • 109
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

FastVLM: Efficient Vision Encoding for Vision Language Models

Paper • 2412.13303 • Published Dec 17, 2024 • 72
rStar2-Agent: Agentic Reasoning Technical Report

Paper • 2508.20722 • Published Aug 28 • 116
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

Paper • 2508.16279 • Published Aug 22 • 53
OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

Paper • 2509.12201 • Published Sep 15 • 104

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

Paper • 2508.16279 • Published Aug 22 • 53
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition

Paper • 2507.20526 • Published Jul 28 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning

Paper • 2505.24726 • Published May 30 • 277
Reinforcement Pre-Training

Paper • 2506.08007 • Published Jun 9 • 263
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Paper • 2507.01006 • Published Jul 1 • 241
A Survey of Context Engineering for Large Language Models

Paper • 2507.13334 • Published Jul 17 • 259

Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 121
HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants

Paper • 2509.08494 • Published Sep 10 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights

Paper • 2509.22944 • Published Sep 26 • 79

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

Paper • 2407.04363 • Published Jul 5, 2024 • 33
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

Paper • 2508.19828 • Published Aug 27 • 7
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

Paper • 2507.05257 • Published Jul 7 • 14
Coarse-to-Fine Grounded Memory for LLM Agent Planning

Paper • 2508.15305 • Published Aug 21

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Paper • 2508.15760 • Published Aug 21 • 46
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Paper • 2508.01780 • Published Aug 3 • 20
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Paper • 2304.08244 • Published Apr 14, 2023 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Less is More: Recursive Reasoning with Tiny Networks

Paper • 2510.04871 • Published Oct 6 • 494
SpikingBrain Technical Report: Spiking Brain-inspired Large Models

Paper • 2509.05276 • Published Sep 5 • 3
Self-Adapting Language Models

Paper • 2506.10943 • Published Jun 12 • 6
The Art of Scaling Reinforcement Learning Compute for LLMs

Paper • 2510.13786 • Published Oct 15 • 30

Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition

Paper • 2507.20526 • Published Jul 28 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Self-Evolving-Agents

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

Paper • 2508.07407 • Published Aug 10 • 98
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Paper • 2504.20073 • Published Apr 24 • 12
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning

Paper • 2505.24726 • Published May 30 • 277
Reinforcement Pre-Training

Paper • 2506.08007 • Published Jun 9 • 263
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Paper • 2507.01006 • Published Jul 1 • 241
A Survey of Context Engineering for Large Language Models

Paper • 2507.13334 • Published Jul 17 • 259

Agent Workflow Memory

Paper • 2409.07429 • Published Sep 11, 2024 • 32
Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training

Paper • 2501.11425 • Published Jan 20 • 109
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 121
HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants

Paper • 2509.08494 • Published Sep 10 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights

Paper • 2509.22944 • Published Sep 26 • 79

FastVLM: Efficient Vision Encoding for Vision Language Models

Paper • 2412.13303 • Published Dec 17, 2024 • 72
rStar2-Agent: Agentic Reasoning Technical Report

Paper • 2508.20722 • Published Aug 28 • 116
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

Paper • 2508.16279 • Published Aug 22 • 53
OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

Paper • 2509.12201 • Published Sep 15 • 104

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

Paper • 2407.04363 • Published Jul 5, 2024 • 33
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

Paper • 2508.19828 • Published Aug 27 • 7
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

Paper • 2507.05257 • Published Jul 7 • 14
Coarse-to-Fine Grounded Memory for LLM Agent Planning

Paper • 2508.15305 • Published Aug 21

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

Paper • 2508.16279 • Published Aug 22 • 53
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Paper • 2508.15760 • Published Aug 21 • 46
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Paper • 2508.01780 • Published Aug 3 • 20
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Paper • 2304.08244 • Published Apr 14, 2023 • 1
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs

Paper • 2508.16153 • Published Aug 22 • 158

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