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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 • 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:2508.16153
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Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 483 -
SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Paper • 2509.05276 • Published • 3 -
Self-Adapting Language Models
Paper • 2506.10943 • Published • 6 -
The Art of Scaling Reinforcement Learning Compute for LLMs
Paper • 2510.13786 • Published • 30
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
Paper • 2508.07407 • Published • 97 -
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Paper • 2504.20073 • Published • 13 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 154
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FastVLM: Efficient Vision Encoding for Vision Language Models
Paper • 2412.13303 • Published • 71 -
rStar2-Agent: Agentic Reasoning Technical Report
Paper • 2508.20722 • Published • 115 -
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Paper • 2508.16279 • Published • 52 -
OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
Paper • 2509.12201 • Published • 103
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AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Paper • 2407.04363 • Published • 33 -
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Paper • 2508.19828 • Published • 6 -
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Paper • 2507.05257 • Published • 14 -
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Paper • 2508.15305 • Published
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Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 274 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 262 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 238 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 258
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Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 120 -
HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
Paper • 2509.08494 • Published • 1 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 154 -
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights
Paper • 2509.22944 • Published • 78
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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 • 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
-
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Paper • 2407.04363 • Published • 33 -
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Paper • 2508.19828 • Published • 6 -
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Paper • 2507.05257 • Published • 14 -
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Paper • 2508.15305 • Published
-
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 483 -
SpikingBrain Technical Report: Spiking Brain-inspired Large Models
Paper • 2509.05276 • Published • 3 -
Self-Adapting Language Models
Paper • 2506.10943 • Published • 6 -
The Art of Scaling Reinforcement Learning Compute for LLMs
Paper • 2510.13786 • Published • 30
-
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
Paper • 2508.07407 • Published • 97 -
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Paper • 2504.20073 • Published • 13 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 154
-
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 274 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 262 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 238 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 258
-
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 120 -
HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
Paper • 2509.08494 • Published • 1 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 154 -
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights
Paper • 2509.22944 • Published • 78
-
FastVLM: Efficient Vision Encoding for Vision Language Models
Paper • 2412.13303 • Published • 71 -
rStar2-Agent: Agentic Reasoning Technical Report
Paper • 2508.20722 • Published • 115 -
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Paper • 2508.16279 • Published • 52 -
OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
Paper • 2509.12201 • Published • 103